All practice questions

1462 questions

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A logistics company is designing an IoT fleet management platform for $150,000$ delivery vehicles. Each vehicle transmits telemetry data every $10$ seconds. The storage layer must support write ingestion at scale and enable real-time queries for vehicle locations with sub-millisecond read latency. While the write traffic is evenly distributed across the fleet, read requests are highly concentrated: a small subset of high-priority delivery vehicles (representing less than $1\%$ of the active fleet) is queried hundreds of times per second for live tracking dashboards. Which database design meets these performance requirements while optimizing costs?

Amazon RDS for PostgreSQL configured with a Multi-AZ deployment and a Read Replica, routing all read queries to the Read Replica and promoting the Read Replica to the primary DB instance if write latency spikes.

Amazon DynamoDB with a partition key of `timestamp` and a sort key of `vehicle_id`, fronted by Amazon DynamoDB Accelerator (DAX) to cache read requests.

Amazon DynamoDB with a partition key of `vehicle_id` and a sort key of `timestamp`, fronted by Amazon DynamoDB Accelerator (DAX) to cache read queries for the high-priority vehicles.

Amazon DynamoDB with a partition key of `vehicle_id` and a sort key of `timestamp`, using DynamoDB Auto Scaling in provisioned capacity mode with local secondary indexes (LSIs) to handle the read spikes.

Show answer & explanation

Answer: Amazon DynamoDB with a partition key of `vehicle_id` and a sort key of `timestamp`, fronted by Amazon DynamoDB Accelerator (DAX) to cache read queries for the high-priority vehicles.

Answer

Amazon DynamoDB with a partition key of vehicle_id and a sort key of timestamp, fronted by Amazon DynamoDB Accelerator (DAX) to cache read queries for the high-priority vehicles.

The correct database design uses Amazon DynamoDB with a partition key of vehicle_id, which provides high cardinality and evenly distributes write requests across partitions. Fronting the table with Amazon DynamoDB Accelerator (DAX) caches read requests for the hot vehicle IDs, delivering sub-millisecond read latency for the live dashboards and preventing partition read throttling in a cost-effective manner.

Step-by-Step Solution

Analyze the ingestion and query patterns of the vehicle fleet telemetry workload.

Identify that the system requires high-throughput write scaling and sub-millisecond read latency for a small, heavily-queried set of hot keys (the high-priority vehicles).

This establishes that a caching layer is necessary to achieve sub-millisecond read latencies and offload hot partitions.

Select a partition key schema that ensures uniform write distribution across partitions.

Choose vehicle_id as the partition key instead of a sequential timestamp.

Using vehicle_id distributes the write payload across partitions, preventing a hot partition write bottleneck.

Select a caching strategy to handle read hot keys and deliver sub-millisecond latency.

Configure Amazon DynamoDB Accelerator (DAX) in front of the DynamoDB table.

DAX provides microsecond response times for cached reads and prevents read operations on high-priority vehicles from exhausting the table's read capacity.

Key Concept

High-Performance DynamoDB design using high-cardinality partition keys and DynamoDB Accelerator (DAX) to resolve read hotspots.

Question 882Question →

A logistics company is building a package tracking system that needs to ingest updates from various transit hubs. A solutions architect is designing an Amazon DynamoDB table to store these status updates. The system must support high-throughput write operations with sub-10 millisecond latency. Which partition key design should the solutions architect choose to maximize database write performance and avoid partition throttling?

A unique Package ID as the partition key

The date of the update formatted as YYYY-MM-DD as the partition key

A constant static string value as the partition key and the update timestamp as the sort key

A sequential auto-incrementing integer identifier as the partition key

Show answer & explanation

Answer: A unique Package ID as the partition key

Answer

A unique Package ID as the partition key

A unique Package ID provides a wide distribution of values (high cardinality). Amazon DynamoDB hashes this key to determine the physical partition for the item. High cardinality ensures that writes are evenly distributed across all partitions, maximizing overall write capacity and preventing write bottlenecks.

Step-by-Step Solution

Determine how Amazon DynamoDB allocates throughput and partitions data.

DynamoDB uses the partition key to hash and distribute data across physical partitions.

Understanding data distribution is crucial for avoiding hot partitions under high write load.

Compare the cardinality of a unique Package ID, a daily date format, a static string, and sequential integers.

A unique Package ID has the highest cardinality and random distribution, while dates, static values, and sequential IDs concentrate writes.

High-cardinality keys distribute writes evenly across multiple partitions, maximizing overall throughput.

Select the unique Package ID to ensure even write distribution and prevent partition throttling.

Write operations are balanced across all available physical partitions.

This design maintains sub-10 millisecond latency and scales performance workloads seamlessly.

Key Concept

Amazon DynamoDB partition key design and partition distribution

Estimated Time:50s

Question 883Question →

A company hosts a microservices application on AWS Fargate tasks running in private subnets. The tasks frequently pull large container images, totaling $120\text{ TB}$ of data transfer per month, from Amazon Elastic Container Registry (Amazon ECR) in the same AWS Region. Currently, the route tables for the private subnets direct all default outbound traffic ( $0.0.0.0/0$ ) through a NAT Gateway. A solutions architect must design a routing architecture to minimize data transfer costs without exposing the tasks to the public internet.

Which network routing configuration should the solutions architect recommend?

Provision a gateway VPC endpoint for Amazon S3, and configure interface VPC endpoints for the Amazon ECR API and Docker registry in the VPC.

Configure interface VPC endpoints for the Amazon ECR API and Docker registry, while continuing to route S3-bound image layer downloads through the NAT Gateway.

Configure interface VPC endpoints for the Amazon ECR API, Docker registry, and Amazon S3.

Configure a VPC peering connection to a transit VPC containing an Internet Gateway, and route ECR traffic through the peering connection.

Show answer & explanation

Answer: Provision a gateway VPC endpoint for Amazon S3, and configure interface VPC endpoints for the Amazon ECR API and Docker registry in the VPC.

Answer

Provision a gateway VPC endpoint for Amazon S3, and configure interface VPC endpoints for the Amazon ECR API and Docker registry in the VPC.

The correct solution uses interface VPC endpoints for the Amazon ECR API and registry services alongside a gateway VPC endpoint for Amazon S3. Amazon ECR hosts actual container image layers in Amazon S3. By using ECR interface endpoints, the private Fargate tasks communicate with the ECR control plane without internet exposure. By using an S3 gateway endpoint, the large image layers (representing the bulk of the

120\text{ TB}

data transfer) are retrieved directly from S3 without passing through the NAT Gateway. This eliminates both the NAT Gateway processing fees and any PrivateLink data processing fees for the S3 traffic, achieving the lowest possible cost.

Step-by-Step Solution

Identify the storage mechanism for Amazon ECR container images.

Amazon ECR stores the actual container image layers (the bulk of the data transfer) in Amazon S3 buckets, while control plane tasks use the ECR API.

Understanding where the data is stored determines how to route the traffic cost-effectively.

Evaluate the current NAT Gateway data transfer cost.

The current setup incurs NAT Gateway data processing charges of

\$0.045

per GB for all

120\text{ TB}

of data, totaling over

\$5\text{}400

per month.

Establishes the baseline cost to optimize.

Compare the cost profiles of VPC endpoint types for Amazon S3.

Gateway VPC endpoints for S3 are free of hourly and data processing charges. Interface VPC endpoints (PrivateLink) for S3 charge a processing fee of

\$0.01

per GB.

Allows selecting the most cost-effective endpoint for the high-volume S3 traffic.

Combine the endpoints to form the final architecture.

Provisioning interface endpoints for ECR API/registry handles the control plane traffic, while the S3 gateway endpoint handles the

120\text{ TB}

image layer download traffic for free.

Maximizes cost savings while maintaining private subnet communication.

Key Concept

VPC Endpoints for Cost Optimization

Question 884Question →

A biotechnology company is deploying a distributed genomic sequencing pipeline on Amazon EC2. The sequencing tasks require tightly-coupled compute nodes that communicate via Message Passing Interface (MPI) to process large datasets with sub-millisecond node-to-node latency. Additionally, a Network Load Balancer (NLB) is configured to route incoming telemetry data to a proprietary ingestion service running on custom port $50051$ on these EC2 instances. The solutions architect needs to design a high-performing and elastic compute infrastructure that supports both the low-latency MPI communication and the correct routing of ingestion data.

Which TWO actions should the solutions architect take to meet these requirements?

Select all that apply

Launch the EC2 instances in a cluster placement group within a single Availability Zone.

Configure the Network Load Balancer target group health checks to explicitly monitor port

50051

Launch the EC2 instances in a spread placement group to distribute the instances across distinct hardware racks.

Configure the Network Load Balancer target group health checks to use the default target port of

80

Launch the EC2 instances in a partition placement group to distribute the instances across multiple logical partitions.

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group within a single Availability Zone.; Configure the Network Load Balancer target group health checks to explicitly monitor port $50051$ .

Answer

Launch the EC2 instances in a cluster placement group within a single Availability Zone, and configure the Network Load Balancer target group health checks to monitor port 50051.

The correct architecture requires launching the EC2 instances in a cluster placement group and configuring the Network Load Balancer target group health checks to explicitly monitor port

50051

. A cluster placement group places instances physically close to one another within a single Availability Zone, providing the low-latency, high-throughput network performance required for Message Passing Interface (MPI) and tightly-coupled compute workloads. Additionally, because the proprietary ingestion service is running on custom port

50051

, the health checks must be explicitly directed to that port to ensure correct monitoring of the application daemon; otherwise, the load balancer will fail to reach the instances and flag them as unhealthy.

Step-by-Step Solution

Analyze the network latency requirement for MPI-based workloads.

Tightly-coupled MPI workloads require sub-millisecond node-to-node latency, which is achieved by packing EC2 instances close together physically.

This determines the choice of EC2 placement groups, highlighting that a cluster placement group is necessary.

Identify the incorrect placement group configurations.

Spread and partition placement groups introduce physical separation to reduce risk, which conflicts with low-latency requirements.

This eliminates options recommending spread or partition placement groups.

Analyze the ingestion service architecture and port settings.

The proprietary ingestion service is bound to port

50051

, and standard HTTP services (port

80

) are not serving this application.

This establishes that the target group health checks must query the service port to properly reflect instance health.

Evaluate the behavior of default health checks.

Using the default traffic port

80

for health checks fails because no process is listening on it, causing healthy instances to be marked unhealthy.

This ensures the selection of explicit custom health check port configurations.

Key Concept

Low-latency compute clusters and custom port target group health checks

Estimated Time:2m 30s

Question 885Question →

A retail company runs a web application on AWS that uses an Amazon RDS for MySQL database. During peak shopping periods, the database experiences high volumes of read queries for generating inventory reports, which leads to performance degradation for transactional write operations. A solutions architect must redesign the database tier to be highly available within a single AWS Region with a recovery time objective (RTO) of less than 60 seconds. Additionally, the solution must offload the inventory reporting queries to scale read capacity independently.

Which TWO configurations should the solutions architect implement to meet these requirements? (Select TWO.)

Select all that apply

Deploy the RDS for MySQL database as a Multi-AZ DB instance deployment.

Create one or more RDS read replicas in different Availability Zones and update the reporting application to use the read replica endpoints.

Configure RDS read replicas as the failover target and use custom scripts to promote them to the primary database when the main instance is unavailable.

Deploy a warm standby RDS DB instance in a secondary AWS Region to handle automatic write failover during an Availability Zone outage.

Configure an Amazon Route 53 latency routing policy to distribute write requests directly to the read replicas during failover events.

Show answer & explanation

Answer: Deploy the RDS for MySQL database as a Multi-AZ DB instance deployment.; Create one or more RDS read replicas in different Availability Zones and update the reporting application to use the read replica endpoints.

Answer

The solutions architect should deploy the database as an Amazon RDS Multi-AZ DB instance deployment to achieve automatic high availability and failover, and create RDS read replicas to offload inventory reporting queries.

To meet the low Recovery Time Objective (RTO) of less than 60 seconds within a single Region, the primary database must be configured with an Amazon RDS Multi-AZ DB instance deployment. This setup automatically replicates data synchronously to a standby instance in a different Availability Zone and performs automatic failover in the event of an outage. To scale read capacity independently and offload read-heavy reporting queries, the architect should deploy RDS read replicas and route read traffic to their endpoints, which prevents write operations on the primary DB instance from being degraded.

Step-by-Step Solution

Analyze the high availability and recovery time objective (RTO) constraints.

An Amazon RDS Multi-AZ DB instance deployment provides automatic failover within a single Region, typically taking less than 60 seconds, meeting the RTO requirement.

Multi-AZ DB instances provide synchronous replication and automatic failover, which is required for high availability.

Analyze the read scaling requirements to offload analytics traffic from write operations.

Creating one or more RDS read replicas allows read-heavy queries to be routed to dedicated replica endpoints.

Read replicas utilize asynchronous replication to scale read workloads independently of the primary write database.

Evaluate and eliminate incorrect configurations that violate high availability or read scalability rules.

Discard manual failover configurations, multi-region database strategies that lack automatic failover, and options using read replicas as write targets.

These configurations violate regional high-availability constraints, Route 53 capabilities, and read-replica write limitations.

Key Concept

High availability via Multi-AZ deployments and read scaling via Read Replicas in Amazon RDS.

Estimated Time:2m 0s

Question 886Question →

An e-commerce platform generates two types of files that are stored in an Amazon S3 bucket:

* Hourly transaction summaries: Average file size of $450\text{ KB}$ . These files are accessed frequently during the first $15\text{ days}$ for financial reconciliation, and rarely thereafter. They must be available for immediate download in the event of an audit over a $1\text{-year}$ retention period.
* Raw server access logs: Average file size of $20\text{ MB}$ . These logs are kept for $90\text{ days}$ for security analysis. They are rarely accessed, but if they are needed for a security investigation, a retrieval time of $3$ to $5\text{ hours}$ is acceptable.

Which combination of Amazon S3 Lifecycle rules will provide the most cost-optimized storage solution? (Select TWO.)

Select all that apply

Transition the hourly transaction summaries to Amazon S3 Standard-Infrequent Access (S3 Standard-IA)

30\text{ days}

after object creation.

Transition the raw server access logs to Amazon S3 Glacier Flexible Retrieval immediately after object creation, and expire them after

90\text{ days}

Transition the hourly transaction summaries to Amazon S3 Standard-Infrequent Access (S3 Standard-IA)

15\text{ days}

after object creation.

Transition the raw server access logs to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) immediately after object creation, and expire them after

15\text{ days}

Transition the hourly transaction summaries to Amazon S3 Glacier Flexible Retrieval

30\text{ days}

after object creation.

Show answer & explanation

Answer: Transition the hourly transaction summaries to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) $30\text{ days}$ after object creation.; Transition the raw server access logs to Amazon S3 Glacier Flexible Retrieval immediately after object creation, and expire them after $90\text{ days}$ .

Answer

The correct combination of lifecycle rules is to transition the hourly transaction summaries to Amazon S3 Standard-IA after

30\text{ days}

, and transition the raw server access logs to Amazon S3 Glacier Flexible Retrieval immediately and expire them after

90\text{ days}

For the hourly transaction summaries, S3 Standard-IA is the most cost-optimized tier because the files are larger than the

128\text{ KB}

transition threshold, are accessed infrequently after

15\text{ days}

, and must support immediate download. Since S3 Lifecycle rules require objects to stay in S3 Standard for a minimum of

30\text{ days}

before transitioning to S3 Standard-IA, transitioning them after

30\text{ days}

is the most cost-effective correct configuration. For the raw server access logs, the

20\text{ MB}

files are large enough for Glacier storage without excessive metadata overhead, can tolerate a

3

5\text{ hour}

retrieval window, and are retained for

90\text{ days}

. Transitioning them immediately to S3 Glacier Flexible Retrieval minimizes costs, and expiring them after

90\text{ days}

aligns with the retention requirement while satisfying the

90\text{-day}

minimum storage duration of Glacier Flexible Retrieval, avoiding early deletion penalties.

Step-by-Step Solution

Analyze requirements for the hourly transaction summaries.

The transaction summaries require immediate download and a

1\text{-year}

retention. They are accessed rarely after

15\text{ days}

. Their average size is

450\text{ KB}

, which is above the

128\text{ KB}

minimum size limit for S3 Standard-IA transition.

To identify if S3 Standard-IA is the correct tier and determine the size and retrieval constraints.

Check the S3 Standard-IA transition constraint.

S3 Lifecycle configurations require objects to remain in S3 Standard for at least

30\text{ days}

before transitioning to S3 Standard-IA or S3 One Zone-IA.

To determine the earliest possible transition window for S3 Standard-IA.

Analyze requirements for the raw server access logs.

The logs are

20\text{ MB}

each, are kept for

90\text{ days}

, and can tolerate a retrieval time of

3

5\text{ hours}

. This makes S3 Glacier Flexible Retrieval (standard retrieval) the most cost-effective tier.

To determine the correct target tier based on file size, access frequency, retrieval window, and retention period.

Verify Glacier minimum storage duration constraints.

S3 Glacier Flexible Retrieval has a

90\text{-day}

minimum storage duration. Expiring the logs after

90\text{ days}

matches the retention requirement and avoids early deletion penalties.

To verify that deleting the logs at

90\text{ days}

does not trigger extra charges.

Key Concept

Selecting S3 storage tiers based on access frequency, file size, retrieval time, and S3 Lifecycle minimum transition and storage duration rules.

Question 887Question →

A software development team uses a dedicated sandbox account for experimental projects. The team leads want to ensure they receive a proactive notification if the total monthly cost of the sandbox environment is forecasted to exceed a specific dollar amount, allowing them to stop unnecessary resources.

Which AWS tool should be used to configure this forecast-based alert?

AWS Budgets

AWS Cost Explorer

AWS Cost and Usage Report (CUR)

Compute Savings Plans

Show answer & explanation

Answer: AWS Budgets

Answer

AWS Budgets

AWS Budgets is the correct tool because it allows you to set custom budgets that track your cost and usage, and configure alerts that trigger when your actual or forecasted costs exceed your budget threshold. This matches the requirement to proactively notify the team when monthly costs are forecasted to exceed a specific amount.

Step-by-Step Solution

Identify the core requirement of the scenario: the team needs proactive notifications (alerts) when forecasted monthly costs exceed a threshold.

Proactive alerts based on forecasted budgets are required.

This helps target the tool specifically designed for budget tracking and threshold alerting.

Evaluate the capabilities of the available AWS cost management tools against the requirement.

AWS Budgets natively supports alerts on forecasted costs, whereas AWS Cost Explorer and Cost and Usage Reports are retrospective tools.

Choosing the right service ensures that cost overruns are caught before they happen rather than analyzed after the fact.

Key Concept

Proactive cost monitoring and alerting using AWS Budgets

Estimated Time:45s

Question 888Question →

A logistics company stores daily vehicle route telemetry reports in an Amazon S3 bucket. The average file size of each report is $8\text{ MB}$ . These reports are accessed frequently for real-time tracking and optimization during the first $14$ days after upload. After $14$ days, the reports are rarely accessed, but the company must retain them for a total of $90$ days for monthly compliance auditing. After $90$ days, the reports must be permanently deleted.

Which two actions should the company take to meet these requirements in the most cost-effective manner?

Select all that apply

Configure an S3 Lifecycle rule to transition the reports from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after

14

days.

Configure an S3 Lifecycle rule to expire the reports after

90

days.

Configure an S3 Lifecycle rule to transition the reports to S3 Standard-Infrequent Access (S3 Standard-IA) after

5

days.

Configure an S3 Lifecycle rule to transition the reports to Amazon S3 Glacier Flexible Retrieval after

14

days and expire them after

90

days.

Configure an S3 Lifecycle rule to transition the reports to S3 Standard-Infrequent Access (S3 Standard-IA) after

14

days and expire them after

25

days.

Show answer & explanation

Answer: Configure an S3 Lifecycle rule to transition the reports from S3 Standard to S3 Standard-Infrequent Access (S3 Standard-IA) after $14$ days.; Configure an S3 Lifecycle rule to expire the reports after $90$ days.

Answer

The company should configure an S3 Lifecycle rule to transition the reports from S3 Standard to S3 Standard-Infrequent Access after 14 days, and configure another lifecycle rule to expire the reports after 90 days.

Transitioning reports to S3 Standard-IA after 14 days aligns with the access patterns and object size requirements, while staying in the tier for 76 days avoids any minimum storage duration penalties. Expiring the reports after 90 days fulfills the compliance requirement while minimizing storage costs.

Step-by-Step Solution

Analyze access patterns and object sizes to select the correct storage class transition timing.

Since the reports are frequently accessed for the first 14 days, they must remain in S3 Standard during this period. After 14 days, they are rarely accessed and have an average size of 8 MB (larger than the 128 KB minimum size threshold), making them ideal candidates for S3 Standard-Infrequent Access (S3 Standard-IA).

S3 Standard-IA is cost-optimized for infrequently accessed data that requires rapid access when needed, but it charges a retrieval fee and has a minimum object size limit of 128 KB.

Evaluate the storage duration in S3 Standard-IA against the minimum storage duration requirement.

The objects will transition to S3 Standard-IA on day 14 and be deleted on day 90, meaning they will spend 76 days in S3 Standard-IA. This satisfies the 30-day minimum storage duration requirement for S3 Standard-IA without incurring early deletion penalties.

S3 Standard-IA requires a minimum storage duration of 30 days. Deleting or transitioning objects before this period results in charges for the remaining days.

Select the correct expiration rule to automate the final lifecycle stage.

Create an S3 Lifecycle expiration action to delete the reports after 90 days.

The scenario requires that reports be permanently deleted after 90 days to minimize storage costs once the retention compliance period has ended.

Key Concept

S3 Lifecycle Management and Storage Tier Minimum Durations

Question 889Question →

A company is migrating a database workload to AWS. The database experiences sudden, unpredictable spikes in traffic that last for only a few minutes, followed by long periods of idle time. The solutions architect needs to select a database capacity strategy that prevents application throttling while minimizing costs for idle resources. Which configuration meets these requirements most cost-effectively?

Amazon DynamoDB configured with On-Demand capacity mode

Amazon DynamoDB configured with Provisioned capacity mode and a high constant throughput limit

Amazon DynamoDB configured with Provisioned capacity mode and Application Auto Scaling

Amazon RDS Multi-AZ deployment utilizing Read Replicas as the primary disaster recovery failover target to handle the write spikes

Show answer & explanation

Answer: Amazon DynamoDB configured with On-Demand capacity mode

Answer

Amazon DynamoDB configured with On-Demand capacity mode

The configuration utilizing Amazon DynamoDB configured with On-Demand capacity mode is correct. On-demand capacity mode is ideal for highly unpredictable or spiky workloads because it instantly accommodates the traffic peaks and charges only for the reads and writes performed, avoiding costs during idle hours.

Step-by-Step Solution

Analyze the workload characteristics

The workload is characterized by sudden, unpredictable traffic spikes lasting a few minutes, followed by long periods of inactivity.

Identifying the traffic pattern determines whether provisioned or on-demand capacity is more cost-effective.

Evaluate DynamoDB capacity modes against the workload profile

Provisioned capacity mode with auto scaling is too slow to adapt to instantaneous multi-minute spikes. Constant high provisioning wastes money during idle hours. On-Demand capacity mode scales instantly and bills only for active read/write requests.

Matching the resource scaling characteristics to the speed and frequency of traffic spikes ensures optimal cost and performance.

Key Concept

Selecting between DynamoDB On-Demand and Provisioned capacity modes based on workload predictability and spikiness to optimize costs.

Question 890Question →

A financial analytics company stores daily risk assessment datasets in a single Amazon S3 bucket. The datasets consist of two distinct file types:

* Market Simulation Results: Average file size of $200\text{ MB}$ . These files are actively analyzed for $15\text{ days}$ after creation. Afterwards, they are rarely accessed but must be retained for $7\text{ years}$ for compliance audits. Audit requests require a retrieval time of less than $5\text{ minutes}$ .
* Execution Log Fragments: Average file size of $15\text{ KB}$ . These files are used for debugging during the first $10\text{ days}$ and are never accessed afterwards. They can be safely deleted after $10\text{ days}$ .

Which TWO actions should a Solutions Architect take to configure a lifecycle policy that minimizes storage and retrieval costs?

Select all that apply

Configure a lifecycle rule to transition Market Simulation Results to Amazon S3 Glacier Instant Retrieval after

15\text{ days}

Configure a lifecycle rule to delete Execution Log Fragments after

10\text{ days}

without transitioning them to another storage class.

Configure a lifecycle rule to transition the Execution Log Fragments to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after

3\text{ days}

and delete them after

10\text{ days}

Configure a lifecycle rule to transition the Market Simulation Results to Amazon S3 Glacier Flexible Retrieval after

15\text{ days}

and use standard retrieval for audit requests.

Configure a lifecycle rule to transition the Execution Log Fragments to Amazon S3 Glacier Instant Retrieval after

5\text{ days}

and delete them after

10\text{ days}

Show answer & explanation

Answer: Configure a lifecycle rule to transition Market Simulation Results to Amazon S3 Glacier Instant Retrieval after $15\text{ days}$ .; Configure a lifecycle rule to delete Execution Log Fragments after $10\text{ days}$ without transitioning them to another storage class.

Answer

The correct configuration is to transition the Market Simulation Results to Amazon S3 Glacier Instant Retrieval after 15 days, and to delete the Execution Log Fragments after 10 days without transitioning them to another storage class.

Transitioning the Market Simulation Results to Amazon S3 Glacier Instant Retrieval after

15\text{ days}

is correct because the files are large (

200\text{ MB}

, avoiding minimum size charges), rarely accessed after

15\text{ days}

, kept for

7\text{ years}

, and require retrieval in under

5\text{ minutes}

(which S3 Glacier Instant Retrieval satisfies with millisecond access). Deleting the Execution Log Fragments after

10\text{ days}

without transitioning them is correct because the files are very small (

15\text{ KB}

) and short-lived (

10\text{ days}

). Transitioning them to S3 Standard-IA or S3 Glacier storage tiers would incur a minimum

128\text{ KB}

billing size penalty and early deletion fees for violating the minimum storage duration rules.

Step-by-Step Solution

Evaluate the storage requirements and retrieval times for the Market Simulation Results.

The files are large (

200\text{ MB}

), accessed frequently for

15\text{ days}

, then rarely accessed, but must be retrieved in less than

5\text{ minutes}

for audit purposes over a

7\text{-year}

period.

This establishes the constraints needed to choose a storage tier that supports rapid retrieval (under

5\text{ minutes}

) and is cost-effective for long-term archival of large files.

Select the optimal storage class for the Market Simulation Results.

Amazon S3 Glacier Instant Retrieval is selected because it offers millisecond retrieval (meeting the

< 5\text{-minute}

requirement) and has the lowest storage cost for rarely accessed data with instant retrieval requirements.

S3 Standard and S3 Standard-IA have higher storage costs, while S3 Glacier Flexible Retrieval standard retrieval (

3\text{ to }5\text{ hours}

) is too slow.

Evaluate the storage requirements and lifecycle constraints for the Execution Log Fragments.

The files are very small (

15\text{ KB}

), accessed for debugging for

10\text{ days}

, and then deleted.

This establishes the file size and retention constraints to check against S3 storage class billing rules.

Determine the cost impact of transitioning the Execution Log Fragments to other storage classes.

Transitioning the

15\text{ KB}

files to S3 Standard-IA or S3 Glacier Instant Retrieval would trigger minimum billing size charges of

128\text{ KB}

per file and early deletion charges (since the files are deleted on day

10

, violating the

30\text{-day}

90\text{-day}

minimum storage durations).

This demonstrates that leaving the small files in S3 Standard and expiring them after

10\text{ days}

is the most cost-optimized choice.

Key Concept

S3 Lifecycle Policy Cost Optimization and Storage Class Constraints

Question 891Question →

A financial services company is optimizing its network architecture in the us-east-1 Region to reduce mounting AWS data transfer and routing costs. The company's main application VPC (VPC-A) contains Amazon EC2 instances in private subnets that perform the following operations:

* Retrieve 120 TB of archive data monthly from an Amazon S3 bucket located in the us-east-1 Region.
* Query configuration metadata from an Amazon DynamoDB table in the us-east-1 Region.
* Replicate 50 TB of database logs monthly to another VPC (VPC-B) in the us-east-1 Region. This traffic is currently routed through an AWS Transit Gateway.
* Fetch software updates from the internet (approximately 20 GB monthly) via a NAT Gateway.

Which combination of architectural modifications will achieve the maximum cost reduction? (Select TWO.)

Select all that apply

Create Gateway VPC Endpoints for both Amazon S3 and Amazon DynamoDB in the main application VPC, and configure the private subnet route tables to direct traffic to these endpoints.

Establish a VPC Peering connection between the two VPCs, and update the subnet route tables to route the database replication traffic through the peering connection instead of the Transit Gateway.

Deploy Interface VPC Endpoints for Amazon S3 and Amazon DynamoDB in the main application VPC, and configure DNS resolution to route traffic through these endpoints.

Configure an AWS Site-to-Site VPN connection over the internet between the two VPCs, and update the route tables to route the database replication traffic through the VPN tunnels.

Replace the NAT Gateway in the main application VPC with a NAT Instance deployed on a single small EC2 instance, and route all S3 and DynamoDB traffic through it.

Show answer & explanation

Answer: Create Gateway VPC Endpoints for both Amazon S3 and Amazon DynamoDB in the main application VPC, and configure the private subnet route tables to direct traffic to these endpoints.; Establish a VPC Peering connection between the two VPCs, and update the subnet route tables to route the database replication traffic through the peering connection instead of the Transit Gateway.

Answer

To minimize data transfer costs, the company should create Gateway VPC Endpoints in the main application VPC for Amazon S3 and DynamoDB, and establish a VPC Peering connection between the two VPCs to replace the Transit Gateway routing path.

The combination of creating Gateway VPC Endpoints for S3 and DynamoDB and establishing a VPC Peering connection between the two VPCs provides the most cost-effective architecture. Gateway VPC Endpoints do not charge any hourly or data processing fees, completely eliminating the NAT Gateway fees for the 120 TB of S3 traffic and DynamoDB queries. VPC Peering does not charge hourly or data processing fees for inter-VPC traffic within the same region, eliminating the $0.02 per GB processing fee charged by AWS Transit Gateway.

Step-by-Step Solution

Analyze S3 and DynamoDB data transfer requirements.

Identified 120 TB of S3 retrieval and frequent DynamoDB queries originating from private subnets in us-east-1.

Large-scale data traffic to S3 and DynamoDB within the same region can be optimized using Gateway VPC Endpoints, which are free and bypass NAT Gateways.

Evaluate inter-VPC replication traffic cost.

Identified 50 TB of inter-VPC traffic routed via Transit Gateway costing

0.02/GB in data processing fees (

1,000/month).

Since both VPCs are in the same region, replacing Transit Gateway with VPC Peering removes the processing fee while maintaining private connectivity.

Assess internet traffic requirements.

Software updates require 20 GB of public internet access monthly.

This low-volume public traffic should remain on the NAT Gateway as it is cost-effective at low volumes and requires external internet routing.

Compare alternative endpoint and routing choices.

Interface VPC Endpoints and NAT Instances would still charge for data processing or compute, whereas Gateway VPC Endpoints and VPC Peering offer the lowest cost routing paths.

Eliminating hourly and data processing fees for high-volume internal AWS traffic yields the maximum cost reduction.

Key Concept

Optimizing network routing paths and AWS endpoints to avoid data processing charges for internal and regional transfers.

Estimated Time:3m 0s

Question 892Question →

A biotechnology company runs clinical trial data pipelines and stores two types of files in an Amazon S3 bucket:

* Raw DNA Sequencing Outputs: Average file size is $250\text{ MB}$ . These files are accessed frequently during the first $14\text{ days}$ of analysis, accessed occasionally for the next $10\text{ days}$ (Days $15\text{--}24$ ), and then rarely accessed but must be preserved for $3\text{ years}$ to meet regulatory requirements. Retrieval within $12\text{ hours}$ is acceptable.
* Pipeline Execution Metadata Logs: Average file size is $65\text{ KB}$ . These files are accessed frequently during the first $7\text{ days}$ for execution verification and are no longer needed afterward, but must be deleted after a total of $25\text{ days}$ for security compliance.

Which S3 lifecycle configuration is the most cost-effective for these requirements?

Transition the raw DNA sequencing outputs directly from S3 Standard to S3 Glacier Deep Archive on Day 24, and configure the pipeline execution metadata logs to be deleted from S3 Standard on Day 25 without any transition.

Transition the raw DNA sequencing outputs to S3 Standard-IA on Day 14, transition them to S3 Glacier Deep Archive on Day 24, and transition the pipeline execution metadata logs to S3 Standard-IA on Day 7 before deleting them on Day 25.

Transition the raw DNA sequencing outputs to S3 Standard-IA on Day 14, transition them to S3 Glacier Flexible Retrieval on Day 24, and configure the pipeline execution metadata logs to be deleted from S3 Standard on Day 25 without any transition.

Transition the raw DNA sequencing outputs directly from S3 Standard to S3 Glacier Deep Archive on Day 24, and transition the pipeline execution metadata logs to S3 Standard-IA on Day 7 before deleting them on Day 25.

Show answer & explanation

Answer: Transition the raw DNA sequencing outputs directly from S3 Standard to S3 Glacier Deep Archive on Day 24, and configure the pipeline execution metadata logs to be deleted from S3 Standard on Day 25 without any transition.

Answer

The most cost-effective S3 lifecycle configuration transitions the raw DNA sequencing outputs directly from S3 Standard to S3 Glacier Deep Archive on Day 24, and deletes the pipeline execution metadata logs directly from S3 Standard on Day 25 without transition.

The correct answer identifies that transitioning the raw DNA sequencing outputs directly from S3 Standard to S3 Glacier Deep Archive on Day 24 avoids the

30\text{-day}

minimum duration storage penalty associated with S3 Standard-IA. It also correctly determines that keeping the

65\text{ KB}

metadata logs in S3 Standard and deleting them on Day 25 is more economical than transitioning them to S3 Standard-IA, as they are below the

128\text{ KB}

minimum billing size and would be deleted before the

30\text{-day}

minimum storage duration of S3 Standard-IA.

Step-by-Step Solution

Analyze the lifecycle requirements of the

250\text{ MB}

Raw DNA Sequencing Outputs.

Transitioning these files to S3 Standard-IA on Day 14 and then to S3 Glacier Deep Archive on Day 24 means they spend only

10\text{ days}

in S3 Standard-IA. This violates the

30\text{-day}

minimum storage duration of S3 Standard-IA, resulting in a billing penalty for the remaining

20\text{ days}

. Keeping them in S3 Standard until Day 24 and transitioning directly to S3 Glacier Deep Archive avoids this penalty and aligns with the

12\text{-hour}

retrieval window.

To identify the most cost-optimal transition path while satisfying retrieval time and retention requirements.

Analyze the lifecycle requirements of the

65\text{ KB}

Pipeline Execution Metadata Logs.

Since these logs are

65\text{ KB}

(which is less than the

128\text{ KB}

minimum storage size limit of S3 Standard-IA) and are deleted on Day 25 (which is less than the

30\text{-day}

minimum storage duration of S3 Standard-IA), transitioning them to S3 Standard-IA would result in double penalties (paying for

128\text{ KB}

size and paying for a full

30\text{ days}

of storage). Keeping them in S3 Standard until deletion is cheaper.

To prevent S3 Standard-IA size and duration billing penalties on small transient files.

Combine the evaluations to select the optimal configuration.

Configure a lifecycle rule that transitions the DNA sequencing outputs directly to S3 Glacier Deep Archive on Day 24, and a separate rule that deletes the metadata logs on Day 25 without transition.

Synthesize the individual file-type policies into a single cost-optimized strategy.

Key Concept

S3 Standard-IA has a minimum billing size of

128\text{ KB}

and a minimum storage duration of

30\text{ days}

. Moving files smaller than

128\text{ KB}

or transitioning/deleting objects before

30\text{ days}

in Standard-IA results in unnecessary costs. Direct transition from S3 Standard to S3 Glacier Deep Archive is valid and avoids intermediate IA tier penalties.

Estimated Time:3m 0s

Question 893Question →

A company manages several AWS accounts consolidated under a single organization in AWS Organizations. The cloud operations team recently identified a sudden, unexpected spike in Amazon EC2 compute charges caused by a developer deploying high-end GPU instances in an unused region. To prevent future surprise costs, the company wants to implement a cost management strategy that achieves two goals: first, automatically detect unusual spend patterns across all accounts and alert the operations team within $24$ hours; second, monitor a specific research department’s tag-based spend ( $CostCenter = Research$ ) by notifying the department lead when actual monthly charges reach $80\%$ of the allocated budget, and notifying the finance team when forecasted monthly charges are projected to exceed $100\%$ of the budget. Which combination of actions should the solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

Create an AWS Cost Anomaly Detection monitor with an Amazon Simple Notification Service (Amazon SNS) subscription to notify the cloud operations team when an anomaly is detected.

Create an AWS Budget for the Research cost center using tag filters, and configure budget notifications for actual spend at

80\%

and forecasted spend at

100\%

Configure a daily scheduled AWS Cost Explorer report filtered by region and instance type to notify the cloud operations team of any sudden cost changes.

Set up an AWS Billing Conductor billing group for the Research cost center and configure a pro forma billing rule to notify the department head and finance team when budget thresholds are crossed.

Configure an Amazon CloudWatch billing alarm for the Research department's resources that periodically queries AWS Cost Explorer to alert on forecasted monthly spend overruns.

Show answer & explanation

Answer: Create an AWS Cost Anomaly Detection monitor with an Amazon Simple Notification Service (Amazon SNS) subscription to notify the cloud operations team when an anomaly is detected.; Create an AWS Budget for the Research cost center using tag filters, and configure budget notifications for actual spend at $80\%$ and forecasted spend at $100\%$ .

Answer

Create an AWS Cost Anomaly Detection monitor with an Amazon SNS subscription to notify the operations team of cost anomalies, and create an AWS Budget filtered by the CostCenter tag with alerts set for actual and forecasted spend thresholds.

The correct options are to create an AWS Cost Anomaly Detection monitor and to create an AWS Budget with tag-based filters. AWS Cost Anomaly Detection uses machine learning to identify unusual spending patterns across all accounts in an organization and can notify teams via Amazon SNS within

24

hours of detection. AWS Budgets allows organizations to track costs and usage against budgeted amounts, supporting tag-based filtering (such as

CostCenter = Research

) and the creation of alerts based on both actual thresholds (such as

80\%

) and forecasted thresholds (such as

100\%

) to target different stakeholders.

Step-by-Step Solution

Identify the service for detecting unexpected spending spikes within

24

hours.

Determine that AWS Cost Anomaly Detection is a machine learning-backed service that detects unusual spend patterns and can send alerts within

24

hours via Amazon SNS.

This satisfies the requirement to automatically detect cost anomalies across all accounts in a cost-effective manner.

Identify the tool for setting tag-based budgets with actual and forecasted alerts.

Determine that AWS Budgets supports tag-based filters (e.g.,

CostCenter = Research

) and allows configuring alerts based on both actual spend (

80\%

) and forecasted spend (

100\%

) thresholds.

This satisfies the requirement to monitor specific department spend and alert different stakeholders based on actual and forecasted limits.

Key Concept

Proactive cost monitoring and alerting using AWS Budgets and AWS Cost Anomaly Detection.

Question 894Question →

A company has a fleet of Amazon EC2 instances running in a private subnet that daily upload $10\text{ TB}$ of data to an Amazon S3 bucket in the same AWS Region. Currently, the EC2 instances route this traffic through a NAT Gateway, which has led to high data processing charges. Which solution is the most cost-effective way to route this traffic and eliminate the NAT Gateway data processing charges?

Create a gateway VPC endpoint for Amazon S3 and configure the route table of the private subnet to point to the endpoint.

Deploy an interface VPC endpoint (AWS PrivateLink) for Amazon S3 in the private subnet and update the route table.

Provision a NAT Instance in a public subnet to replace the NAT Gateway and route the traffic through it.

Configure an AWS Transit Gateway to route the S3 traffic from the private subnet to the S3 bucket.

Show answer & explanation

Answer: Create a gateway VPC endpoint for Amazon S3 and configure the route table of the private subnet to point to the endpoint.

Answer

Create a gateway VPC endpoint for Amazon S3 and configure the route table of the private subnet to point to the endpoint.

The correct solution is to create a gateway VPC endpoint for Amazon S3 and configure the route table of the private subnet. Gateway VPC endpoints are provided by AWS at no additional cost and do not charge for data processing. By routing the S3-bound traffic through the gateway endpoint, the traffic stays within the AWS network and bypasses the NAT Gateway entirely, eliminating the NAT Gateway's data processing fees.

Step-by-Step Solution

Identify the destination and source of the traffic.

The source is EC2 instances in a private subnet, and the destination is an Amazon S3 bucket within the same AWS Region.

This establishes that both resources are in the same region, making local VPC endpoints viable.

Compare the cost structures of the routing options.

Gateway VPC endpoints for S3 are free of charge (no hourly or data processing fees). NAT Gateways and Interface VPC endpoints (AWS PrivateLink) both charge per GB of data processed.

Choosing a gateway VPC endpoint allows the

10\text{ TB}

of daily traffic to bypass the NAT Gateway entirely, saving substantial data processing costs.

Select the option that routes traffic directly and securely at zero cost.

Creating a gateway VPC endpoint and updating the private subnet's route table.

This configuration routes all traffic destined for S3 through the endpoint without traversing the internet or NAT Gateway, completely eliminating data processing fees for S3 traffic.

Key Concept

Gateway VPC Endpoints provide a secure, cost-free path (with no hourly or data processing charges) to connect a VPC to Amazon S3 and Amazon DynamoDB, bypassing expensive NAT Gateways.

Question 895Question →

A company runs a containerized microservices application on Amazon ECS using AWS Fargate across $2$ Availability Zones. The application requires shared, POSIX-compliant file storage that can handle highly unpredictable read/write spikes, support sub-millisecond (less than $1\text{ ms}$ ) latencies for metadata operations, and dynamically scale throughput without manual intervention or administrative overhead.

Which two Amazon EFS configurations should a solutions architect select to meet these requirements? (Choose two.)

Select all that apply

Amazon EFS configured with General Purpose performance mode

Amazon EFS configured with Elastic throughput mode

Amazon EFS configured with Max I/O performance mode

Amazon EFS configured with Provisioned throughput mode

Amazon EFS Archive storage class set as the primary target for active workloads

Show answer & explanation

Answer: Amazon EFS configured with General Purpose performance mode; Amazon EFS configured with Elastic throughput mode

Answer

The application requires Amazon EFS configured with General Purpose performance mode and Elastic throughput mode.

The correct configuration is Amazon EFS configured with General Purpose performance mode and Elastic throughput mode. General Purpose performance mode is optimized for latency-sensitive applications and provides sub-millisecond latencies for metadata operations. Elastic throughput mode automatically scales throughput capacity in response to workload activity, allowing the system to handle unpredictable spikes without manual provisioning or administrative overhead.

Step-by-Step Solution

Analyze the access and protocol requirements.

The application runs on AWS Fargate across

2

Availability Zones and requires a shared, POSIX-compliant file system, which identifies Amazon EFS as the appropriate service.

Amazon EFS is natively integrated with AWS Fargate and supports multi-AZ POSIX-compliant shared file access.

Select the appropriate EFS performance mode based on latency constraints.

Choose General Purpose performance mode instead of Max I/O performance mode.

General Purpose performance mode provides sub-millisecond latencies for metadata and file operations, matching the requirements.

Select the appropriate EFS throughput mode based on traffic patterns and management overhead.

Choose Elastic throughput mode instead of Provisioned throughput mode.

Elastic throughput mode scales throughput capacity dynamically to handle unpredictable bursts without manual provisioning or administrative overhead.

Key Concept

Selecting the optimal performance and throughput configurations for Amazon EFS to meet latency and dynamic scaling requirements.

Estimated Time:2m 0s

Question 896Question →

A financial technology company is migrating its core transaction ledger application to Amazon Aurora PostgreSQL. The application requires high availability within a single AWS Region, with a Recovery Point Objective (RPO) of zero (no data loss) and a Recovery Time Objective (RTO) of less than 30 seconds. Additionally, the company needs to dynamically scale read operations during peak trading hours. Which database configuration best meets these requirements?

Deploy an Amazon Aurora PostgreSQL DB cluster with a primary writer instance and at least one Aurora Replica in a different Availability Zone, and configure the application to use the cluster endpoint and reader endpoint.

Deploy a single-node Amazon Aurora PostgreSQL DB cluster, and configure an Amazon RDS PostgreSQL Read Replica in a different Availability Zone to serve as the automatic failover target.

Deploy an Amazon Aurora PostgreSQL DB cluster in a Single-AZ configuration, and configure Amazon Route 53 latency routing to automatically redirect write traffic to a read replica during an outage.

Deploy a primary Amazon Aurora PostgreSQL DB instance, configure a standby instance in another Availability Zone, and implement a pilot light replication scheme to sync transaction logs every 10 minutes.

Show answer & explanation

Answer: Deploy an Amazon Aurora PostgreSQL DB cluster with a primary writer instance and at least one Aurora Replica in a different Availability Zone, and configure the application to use the cluster endpoint and reader endpoint.

Answer

The correct configuration uses an Amazon Aurora PostgreSQL DB cluster with a primary writer instance and at least one Aurora Replica in a different Availability Zone. Amazon Aurora automatically replicates database storage across three Availability Zones within the region, ensuring an RPO of zero. If the primary instance fails, Aurora automatically fails over to the replica in another Availability Zone in less than 30 seconds (meeting the RTO requirement). The application can write to the cluster endpoint and scale read operations dynamically by using the reader endpoint.

Step-by-Step Solution

Evaluate the RPO and RTO requirements to determine the storage and replication design.

Since RPO is zero, synchronous replication at the storage layer is required. Amazon Aurora automatically replicates data across three Availability Zones, ensuring zero data loss.

Asynchronous replication schemes or cross-region setups can introduce replication lag, resulting in an RPO greater than zero.

Evaluate the high availability and failover mechanism to meet the RTO requirement.

Deploying an Aurora Replica in a separate Availability Zone enables automatic failover in less than 30 seconds.

Manual intervention, external scripting (e.g., Lambda), or relying on read replicas for standard RDS failover increases failover times and fails the sub-30-second RTO constraint.

Address the read scaling requirement.

Direct read queries to the Aurora reader endpoint, which routes traffic across active replicas.

This offloads read traffic from the primary writer instance without impacting write performance.

Key Concept

Amazon Aurora high availability relies on multi-AZ storage replication and Aurora Replicas acting as failover targets with automated failover handling.

Question 897Question →

A medical imaging enterprise hosts a PACS (Picture Archiving and Communication System) on AWS. The application runs on a fleet of auto-scaled Amazon EC2 instances across multiple Availability Zones in the primary Region. The architecture requires:
- An Amazon RDS for PostgreSQL database hosting clinical transaction metadata.
- A shared storage layer containing raw DICOM image files (average size $12 \text{ MB}$ ) that must be read and written concurrently by the EC2 fleet.

The enterprise must design a disaster recovery (DR) architecture in a secondary AWS Region. The DR plan requires a Recovery Point Objective (RPO) of under $15 \text{ minutes}$ and a Recovery Time Objective (RTO) of under $10 \text{ minutes}$ for both the database and the image files.

Which architectural design meets these requirements while minimizing recovery time?

Deploy the primary database in a Multi-AZ configuration, and create a cross-Region read replica in the DR Region to be promoted upon failover. Store the DICOM images on a Regional Amazon Elastic File System (Amazon EFS) file system, and configure Amazon EFS Replication to replicate the file system to the DR Region. Deploy a Warm Standby environment in the DR Region with a pre-provisioned, scaled-down EC2 fleet.

Deploy the primary database in a Multi-AZ configuration, and create a cross-Region read replica in the DR Region. Store the DICOM images on a Regional Amazon EFS file system, and use an AWS Backup lifecycle policy to transition and replicate the EFS backups to an Amazon S3 Glacier Flexible Retrieval vault in the DR Region. Deploy a Warm Standby environment in the DR Region.

Deploy the primary database in a Multi-AZ configuration, and create a cross-Region read replica in the DR Region. Store the DICOM images on a Regional Amazon EFS file system, and configure Amazon EFS Replication to the DR Region. Deploy a Pilot Light environment in the DR Region with all EC2 application instances stopped, relying on automated scripts to start the instances immediately upon failover.

Deploy a Single-AZ primary database with a local read replica in a different Availability Zone configured as the primary automatic failover target. Store the DICOM images on Amazon EBS General Purpose SSD (gp3) volumes attached to the EC2 instances, and configure EBS snapshot replication to the DR Region. Deploy a Warm Standby environment in the DR Region.

Show answer & explanation

Answer: Deploy the primary database in a Multi-AZ configuration, and create a cross-Region read replica in the DR Region to be promoted upon failover. Store the DICOM images on a Regional Amazon Elastic File System (Amazon EFS) file system, and configure Amazon EFS Replication to replicate the file system to the DR Region. Deploy a Warm Standby environment in the DR Region with a pre-provisioned, scaled-down EC2 fleet.

Answer

Deploy the primary database in a Multi-AZ configuration with a cross-Region read replica, store the diagnostic files on Regional Amazon EFS with cross-Region replication enabled, and deploy a Warm Standby environment in the secondary region.

The correct solution addresses high availability at the database layer (Multi-AZ) and provides cross-Region disaster recovery using RDS cross-Region Read Replicas. For storage, Regional Amazon EFS enables simultaneous read/write access across multiple Availability Zones, and EFS Replication fulfills the disaster recovery requirement asynchronously with a very low RPO. Deploying a Warm Standby (with pre-provisioned, scaled-down EC2 instances) ensures the systems can scale up to full production capacity within the

10 \text{ minute}

RTO.

Step-by-Step Solution

Determine the shared storage requirements for concurrent access and high availability.

Amazon EFS Regional storage is selected because it allows concurrent read/write access from EC2 instances across multiple Availability Zones with sub-millisecond local latencies.

The EC2 application fleet must access the same files concurrently across different AZs, which is supported by EFS but not by EBS gp3 volumes.

Determine the replication strategy to achieve the

15 \text{ minute}

RPO in the DR Region.

Amazon EFS Replication is configured to replicate files asynchronously to the destination Region, and a cross-Region RDS Read Replica is created for the metadata database.

EFS Replication typically replicates changes within minutes, and cross-Region read replicas provide continuous asynchronous updates to the secondary database, satisfying the low RPO.

Select the appropriate disaster recovery environment strategy to meet the

10 \text{ minute}

RTO.

A Warm Standby strategy is implemented in the secondary Region, keeping a minimally scaled, running EC2 fleet ready to receive traffic.

A Warm Standby ensures that instances are already running and can be scaled out rapidly, meeting the sub-

10 \text{ minute}

RTO, whereas a Pilot Light setup with stopped instances would introduce startup delays that violate the RTO.

Key Concept

Designing a resilient and highly available multi-region architecture using Amazon EFS replication, Multi-AZ database configurations, and a Warm Standby DR strategy to meet strict RTO and RPO requirements.

Estimated Time:1m 30s

Question 898Question →

A research group is deploying a molecular dynamics analysis platform on Amazon EC2. The application requires tightly-coupled, node-to-node communication with minimal network latency. Additionally, a web-based administration console running on port 9090 on the same instances needs to receive traffic via an Application Load Balancer (ALB). Which two configurations should the solutions architect select to satisfy these requirements? (Select two.)

Select all that apply

Launch the EC2 instances in a cluster placement group.

Configure the ALB target group's health checks to use port 9090.

Launch the EC2 instances in a spread placement group.

Configure the ALB target group's health checks to use the default port 80.

Launch the EC2 instances in a partition placement group.

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group.; Configure the ALB target group's health checks to use port 9090.

Answer

Launch the EC2 instances in a cluster placement group and configure the ALB target group's health checks to use port 9090.

The correct options are launching the instances in a cluster placement group to meet the low-latency network requirement, and configuring the target group health checks on port 9090 so that the ALB correctly verifies the status of the admin console.

Step-by-Step Solution

Identify the network latency requirement.

Tightly-coupled compute nodes require microsecond-level network latency.

This indicates that a cluster placement group is necessary to keep instances physically close on the underlying hardware.

Identify the application port configuration.

The web-based administration console is listening on port 9090.

This requires the Application Load Balancer target group to perform health checks on port 9090 rather than the default HTTP port 80.

Key Concept

Low-latency instance placement and correct ELB health check port alignment

Estimated Time:1m 30s

Question 899Question →

An IoT fleet management company operates a vehicle tracking platform on AWS. The platform's backend infrastructure includes:

1. A data ingestion API running $24/7$ on AWS Fargate to receive telemetry data from millions of active vehicles. This workload maintains a highly predictable, steady-state baseline of $32\text{ vCPUs}$ and $64\text{ GB RAM}$ .
2. A fleet command service running on Amazon EC2 instances. This service executes highly parallel, containerized tasks to send software updates back to vehicles. The tasks are short-lived, tolerant of interruptions, and triggered by sporadic queue-based events.
3. An Amazon Aurora PostgreSQL database cluster that stores telemetry data and device states.

Which two purchasing and compute strategies should a solutions architect recommend to optimize the system's costs? (Select TWO.)

Select all that apply

Purchase a Compute Savings Plan to cover the steady-state baseline compute usage of the data ingestion API running on AWS Fargate.

Use Spot Instances for the EC2 fleet command service to execute the fault-tolerant, short-lived tasks.

Purchase a Compute Savings Plan to cover the DB instances within the Amazon Aurora PostgreSQL database cluster.

Migrate the data ingestion API to AWS Lambda with Provisioned Concurrency to run the continuous, steady-state telemetry ingestion without provisioning infrastructure.

Configure the EC2 instances for the fleet command service to use Dedicated Hosts with a

3

-year Reservation.

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the steady-state baseline compute usage of the data ingestion API running on AWS Fargate.; Use Spot Instances for the EC2 fleet command service to execute the fault-tolerant, short-lived tasks.

Answer

The correct strategies are to purchase a Compute Savings Plan to cover the baseline AWS Fargate compute usage, and to use Spot Instances for the EC2-based fleet command service running short-lived, interruptible tasks.

Purchasing a Compute Savings Plan covers the baseline AWS Fargate data ingestion API, offering significant cost reduction for predictable compute usage. Using Spot Instances for the fleet command service on EC2 provides maximum savings for highly parallel, short-lived, and fault-tolerant tasks.

Step-by-Step Solution

Analyze the data ingestion API workload requirements.

The API runs

24/7

with a predictable, steady-state baseline of

32\text{ vCPUs}

and

64\text{ GB RAM}

on AWS Fargate.

Identifying the workload profile determines the best purchasing strategy. For Fargate compute with a steady baseline, Compute Savings Plans provide the maximum discount.

Analyze the fleet command service workload requirements.

The service runs highly parallel, containerized tasks on EC2 that are short-lived, triggered workloads, and tolerant of interruptions.

Since the workload is fault-tolerant, short-lived, and scales dynamically, Spot Instances are the most cost-effective option.

Evaluate the database tier cost optimization requirements.

The database is Amazon Aurora PostgreSQL.

Note that Compute Savings Plans do not cover RDS/Aurora DB instances, so any option suggesting this is incorrect.

Key Concept

Selecting the optimal compute hosting and purchasing models (Compute Savings Plans for baseline Fargate, Spot Instances for interruptible EC2 workloads) while recognizing the boundary limitations of AWS Savings Plans (does not apply to RDS/Aurora).

Question 900Question →

A meteorological research organization is designing a high-performing data ingestion and transformation pipeline to process real-time atmospheric readings from thousands of weather balloons. During peak hours, the sensor data stream reaches a throughput of $15\text{ MiB/s}$ , with an average payload size of $2\text{ KiB}$ per reading. The organization requires that the data be ingested without loss, preserved in chronological order per balloon for accurate modeling, transformed from JSON to Apache Parquet format to optimize SQL queries, and stored in Amazon S3 within $5$ minutes of generation. Which two solutions should a solutions architect recommend to meet these requirements with the lowest operational overhead? (Select two.)

Select all that apply

Ingest the sensor readings using an Amazon Kinesis Data Stream configured in On-Demand capacity mode.

Create an Amazon Data Firehose delivery stream with the Kinesis Data Stream as the source, and enable record format conversion to Apache Parquet using AWS Glue before writing to Amazon S3.

Ingest the sensor readings using an Amazon SQS standard queue to decouple the ingestion tier from downstream consumers.

Ingest the sensor readings using an Amazon Kinesis Data Stream configured in Provisioned capacity mode with

5

shards.

Configure an Amazon SQS standard queue to trigger an AWS Lambda function that converts individual JSON payloads to Apache Parquet and writes them directly to Amazon S3.

Show answer & explanation

Answer: Ingest the sensor readings using an Amazon Kinesis Data Stream configured in On-Demand capacity mode.; Create an Amazon Data Firehose delivery stream with the Kinesis Data Stream as the source, and enable record format conversion to Apache Parquet using AWS Glue before writing to Amazon S3.

Answer

Ingesting the sensor readings using an Amazon Kinesis Data Stream in On-Demand capacity mode, and using Amazon Data Firehose with the stream as a source to convert record formats to Apache Parquet using AWS Glue before writing to Amazon S3.

Ingesting the sensor data with an Amazon Kinesis Data Stream in On-Demand capacity mode ensures that the stream automatically scales to handle the peak write throughput of

15\text{ MiB/s}

without manual administrative intervention. Connecting Amazon Data Firehose to the Kinesis Data Stream allows for automatic, near-real-time delivery to Amazon S3. Enabling record format conversion in the Firehose delivery stream using AWS Glue allows the JSON data to be converted to Apache Parquet format on the fly with low operational overhead and minimal latency, meeting the

5

-minute storage requirement.

Step-by-Step Solution

Analyze ingestion scaling requirements and calculate the required capacity.

The peak ingestion rate is

15\text{ MiB/s}

. In Kinesis Data Streams, each shard supports

1\text{ MiB/s}

of ingress. An On-Demand stream automatically scales to handle this volume, whereas a provisioned stream with fewer than

15

shards would throttle.

To prevent data loss and support ingestion throughput demands.

Evaluate ordering requirements for processing.

The readings must preserve the chronological order per weather balloon. Kinesis Data Streams naturally preserves ordering per partition key (e.g., balloon ID), whereas Amazon SQS standard queues do not guarantee ordering.

Ensuring chronological data alignment is critical for accurate weather modeling.

Select the format conversion and storage mechanism.

Amazon Data Firehose can consume from the Kinesis Data Stream and perform native JSON-to-Parquet conversion using schema definition in the AWS Glue Data Catalog, then deliver the files directly to Amazon S3 within the

5

-minute time window.

This serverless integration provides the lowest operational overhead compared to custom processing configurations.

Key Concept

Leveraging Kinesis Data Streams On-Demand capacity for high-throughput scaling, combined with Amazon Data Firehose for zero-infrastructure data transformation and S3 delivery.

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