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Question 1261Question →

A university student union operates a mobile voting application on AWS for annual campus elections. The application is completely idle for 360360 days of the year, but experiences highly unpredictable, spiky traffic during a 55-day election period. The database stores simple, non-relational voting records consisting of key-value pairs. The student union operates on a very limited budget and must minimize database operational costs while ensuring the system does not throttle or fail. Which database configuration is the most cost-effective and meets these requirements?

Show answer & explanation

Answer: An Amazon DynamoDB table configured in On-Demand capacity mode.

Answer

An Amazon DynamoDB table configured in On-Demand capacity mode is the most cost-effective solution.
The correct answer is the option specifying Amazon DynamoDB in On-Demand capacity mode. Because the application is idle for 360360 days a year, On-Demand capacity mode ensures there are no hourly write or read capacity charges during the idle period. During the 55-day election period, it instantly scales to accommodate any sudden write spikes, providing zero-throttling reliability at the lowest possible cost.

Step-by-Step Solution

1
Analyze the application workload characteristics, traffic patterns, and data model.
The database workload is non-relational key-value data, completely idle for 360360 days of the year, and subject to sudden, unpredictable traffic spikes during a 55-day window.
Identifying the workload and data structure helps determine whether a relational or non-relational database is appropriate and informs the scaling strategy.
2
Select the correct database engine based on data model and baseline costs.
Amazon DynamoDB is selected over relational engines (RDS and Aurora) because it natively supports key-value storage and avoids the continuous hourly compute costs of idle virtual instances.
Eliminating relational database options avoids unnecessary baseline compute costs during the long idle periods.
3
Evaluate the capacity planning options for the chosen database engine to optimize cost and performance.
DynamoDB On-Demand capacity mode is chosen instead of Provisioned capacity mode because On-Demand scales instantly to handle unpredictable peaks without throttling, and charges only for requests consumed, resulting in near-zero cost during idle periods.
Choosing On-Demand capacity mode aligns database costs directly with actual usage, which is ideal for spiky, unpredictable, and primarily idle workloads.

Key Concept

Selecting the cost-optimal database engine and capacity scaling mode for spiky, highly irregular workloads.
Estimated Time:1m 30s
Question 1262Question →

An IoT-enabled smart parking management system generates vehicle entry and exit events that must be processed in the exact sequence they occur for each vehicle to calculate parking fees accurately. The events must be delivered to both a real-time billing application and an occupancy tracking dashboard. Both downstream applications must receive every event. Which solution meets these requirements while ensuring strict message ordering is preserved for each vehicle?

Show answer & explanation

Answer: Publish the events to an Amazon SNS FIFO topic. Subscribe two Amazon SQS FIFO queues to the topic, one for each downstream application. Use the vehicle ID as the message group ID when publishing the events.

Answer

Publish the events to an Amazon SNS FIFO topic, subscribe two Amazon SQS FIFO queues to the topic, and use the vehicle ID as the message group ID when publishing.
To preserve message ordering end-to-end and deliver every message to multiple downstream consumers (fan-out), both the Amazon SNS topic and the Amazon SQS queues must be configured as FIFO (First-In-First-Out). Publishing events to an SNS FIFO topic with a specific message group ID (the vehicle ID) ensures that all messages for that vehicle are grouped and processed sequentially. The SQS FIFO queues subscribed to the SNS FIFO topic will receive the messages in the exact order they were published, allowing each consumer to process them in sequence.

Step-by-Step Solution

1
Identify the requirement for message ordering and fan-out capability.
The application requires message ordering to be preserved per vehicle ID (ordering requirement) and messages to be delivered to two separate applications (fan-out requirement).
This dictates the choice of messaging services that support both event fan-out and first-in, first-out delivery guarantees.
2
Select the appropriate messaging services to implement a fan-out architecture.
Amazon SNS is selected for fanning out messages, and Amazon SQS is selected to act as the buffer queues for the downstream applications.
SNS allows a single published message to be distributed to multiple subscribers, while SQS queues ensure that messages are buffered and processed asynchronously without losing data.
3
Determine the required queue and topic type for strict ordering.
Select SNS FIFO and SQS FIFO instead of standard types.
Standard SNS and SQS do not guarantee message ordering. To maintain chronological sequence end-to-end, both the SNS topic and the SQS queues must be FIFO.
4
Configure the grouping identifier to order messages per vehicle.
Use the vehicle ID as the Message Group ID (MessageGroupId) when publishing to the SNS FIFO topic.
SQS FIFO and SNS FIFO use the Message Group ID to group messages. Messages within the same group are processed in strict FIFO order, while messages across different groups can be processed in parallel, optimizing performance.

Key Concept

End-to-end first-in, first-out (FIFO) message ordering in fan-out architectures using Amazon SNS FIFO and Amazon SQS FIFO.
Question 1263Question →

A company has a legacy web application running on Amazon EC2 instances with a MySQL database hosted on a single Amazon RDS DB instance. The database is experiencing performance degradation due to a high volume of read queries. The application requires high availability, and the database must support automatic failover in the event of an outage.

Which solution should a solutions architect implement to resolve the read bottleneck while meeting the high availability requirements?

Show answer & explanation

Answer: Migrate the database to an Amazon Aurora MySQL DB cluster with a primary instance and at least one Aurora Replica. Configure the application to route read queries to the reader endpoint and write queries to the cluster endpoint.

Answer

Migrating the database to an Amazon Aurora MySQL DB cluster, configuring Aurora Replicas, and using the reader and cluster endpoints is the correct solution.
Amazon Aurora MySQL DB clusters separate the primary write instance from the reader instances (replicas). The cluster endpoint handles write traffic, while the reader endpoint load-balances read traffic across the replica nodes. If the primary instance fails, Aurora automatically promotes one of the replicas to the primary role with minimal disruption, meeting both high-performance read and high-availability requirements.

Step-by-Step Solution

1
Analyze the requirements for scaling database reads and providing automatic failover.
The solution must support both read scale-out (high performance) and automatic, zero-downtime database failover (high availability).
This establishes the criteria to evaluate relational database designs on AWS.
2
Evaluate the capabilities of Amazon RDS Multi-AZ and Read Replicas.
RDS Multi-AZ standby instances cannot be read from, while RDS Read Replicas do not support automatic failover.
This eliminates options that confuse RDS standby instances with read targets or read replicas with automatic failover targets.
3
Evaluate Amazon Aurora capabilities for read scaling and failover.
Amazon Aurora MySQL DB clusters support up to 15 replicas that serve dual roles: scaling read capacity via the reader endpoint and acting as automatic failover targets.
This confirms that Aurora meets both the performance (read scaling) and resilience (automatic failover) goals.

Key Concept

Scaling relational database read performance using Aurora reader endpoints and replicas while maintaining high availability.
Question 1264Question →

A logistics and supply chain management company is migrating its operations platform to AWS. The platform consists of three main workloads:

1. A fleet tracking microservice running on Amazon ECS on AWS Fargate. This service runs 24/7 and has a highly predictable baseline CPU and memory utilization.
2. A route optimization engine running on Amazon EC2 instances that is executed once every night. The optimization engine takes approximately 3 hours to complete, is highly fault-tolerant, can be paused and restarted from checkpoints, and requires significant compute capacity.
3. An inventory database tier running on Amazon RDS for PostgreSQL that operates 24/7.

Which combination of purchasing strategies should the solutions architect recommend to achieve the most cost-effective compute architecture? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the baseline usage of the fleet tracking microservice running on AWS Fargate.; Use Amazon EC2 Spot Instances to run the nightly route optimization engine.

Answer

The correct options are to purchase a Compute Savings Plan to cover the baseline usage of the fleet tracking microservice on AWS Fargate, and to use Amazon EC2 Spot Instances to run the nightly route optimization engine.
Purchasing a Compute Savings Plan is correct because it offers the flexibility to apply discounts across EC2, Lambda, and AWS Fargate, making it perfect for the 24/7 predictable fleet tracking microservice. Using Spot Instances is correct because the nightly route optimization engine is highly fault-tolerant and can restart from checkpoints, allowing it to take advantage of the deeply discounted Spot pricing without risking task failure.

Step-by-Step Solution

1
Analyze the fleet tracking microservice requirements.
It runs 24/7 on AWS Fargate with a predictable baseline.
Identify the most suitable purchasing model for predictable Fargate compute, which is a Compute Savings Plan.
2
Analyze the route optimization engine requirements.
It runs nightly for 3 hours, requires high compute capacity, and is fault-tolerant and checkpointed.
Identify the most suitable purchasing model for interruptible, non-continuous batch compute, which is EC2 Spot Instances.
3
Analyze the RDS database tier requirements.
It runs 24/7 on Amazon RDS for PostgreSQL.
Determine if Savings Plans apply to RDS. Since they do not, rule out options suggesting Compute Savings Plans or EC2 Instance Savings Plans for RDS, and recognize that RDS Reserved Instances would be needed instead.

Key Concept

Selecting cost-optimized purchasing strategies (Savings Plans, Spot Instances) for different compute hosting models (ECS Fargate, EC2) while understanding their scope limitations.
Question 1265Question →

A law firm is designing a serverless system to automate the metadata extraction of scanned case files. Case documents are uploaded to Amazon S3 at unpredictable times throughout the day, followed by long periods of inactivity. The processing pipeline must process the documents in the exact chronological order of their upload to maintain case sequence integrity. The extraction logic is packaged as a containerized application and takes between 2020 and 3535 minutes per document batch. The extracted metadata is stored in a database that is queried sporadically by internal applications. Which system architecture meets these requirements in the most cost-effective manner?

Show answer & explanation

Answer: Configure Amazon S3 event notifications to publish messages to an Amazon SQS FIFO queue. Deploy the containerized application on Amazon ECS using the AWS Fargate launch type, scaling the number of tasks dynamically based on queue depth. Store the metadata in an Amazon DynamoDB table configured with On-Demand capacity mode.

Answer

Configure Amazon S3 event notifications to publish messages to an Amazon SQS FIFO queue, deploy the containerized application on Amazon ECS using the AWS Fargate launch type scaling dynamically based on queue depth, and store the metadata in an Amazon DynamoDB table configured with On-Demand capacity mode.
The correct solution uses an Amazon SQS FIFO queue to guarantee the chronological sequence of case document updates. It runs the containerized logic on Amazon ECS with the AWS Fargate launch type, which supports runtimes exceeding 1515 minutes and scales dynamically down to zero when the queue is empty to avoid idle compute charges. Finally, it uses Amazon DynamoDB with On-Demand capacity mode to store the metadata, avoiding any minimum hourly costs during long periods of database inactivity.

Step-by-Step Solution

1
Select the appropriate messaging queue to ensure message ordering.
Choose Amazon SQS FIFO queue.
The scenario requires processing documents in the exact chronological order of their upload. SQS Standard does not guarantee ordering, whereas SQS FIFO guarantees first-in, first-out order.
2
Select the appropriate serverless compute service based on the processing duration.
Choose Amazon ECS on AWS Fargate.
The processing takes 2020 to 3535 minutes. AWS Lambda has a hard execution limit of 1515 minutes and cannot be used. AWS Fargate allows containerized tasks to run for hours without server management, and scaling them based on SQS queue depth ensures tasks scale down to zero when idle, minimizing compute cost.
3
Select the database capacity mode based on the access pattern.
Choose Amazon DynamoDB with On-Demand capacity mode.
The metadata is queried sporadically with unpredictable activity. DynamoDB On-Demand capacity mode charges per request and has no minimum hourly cost for idle capacity, making it more cost-effective than Provisioned capacity mode.

Key Concept

Serverless queue-based container orchestration and database capacity optimization for unpredictable, long-running batch workloads.
Question 1266Question →

A company runs a social media application where users post updates. The database workloads are split into two Amazon DynamoDB tables:

1. A table storing user posts, which receives highly unpredictable, sudden spikes of write traffic when posts go viral.
2. A table storing user profile information, which has a stable, predictable read volume during daytime hours.

Both tables are currently configured with high Provisioned Capacity Units (PCUs) to prevent throttling during peak times, leading to significant idle capacity costs.

Which TWO actions should the solutions architect recommend to optimize the database costs? (Select TWO).

Select all that apply

Show answer & explanation

Answer: Configure the user posts table to use DynamoDB On-Demand capacity mode.; Enable DynamoDB Auto Scaling with provisioned capacity for the user profile table.

Answer

Configure the user posts table to use DynamoDB On-Demand capacity mode, and enable DynamoDB Auto Scaling with provisioned capacity for the user profile table.
For the user posts table, DynamoDB On-Demand capacity mode automatically accommodates sudden traffic spikes without throttling and charges only for what is consumed, making it ideal for unpredictable workloads. For the user profile table, DynamoDB Provisioned capacity mode with Auto Scaling allows the company to take advantage of lower baseline rates for predictable traffic while adjusting to daily usage changes.

Step-by-Step Solution

1
Analyze the workload characteristics of each DynamoDB table.
The user posts table has highly unpredictable, spiky traffic. The user profile table has stable, predictable read traffic.
Choosing the right capacity mode requires aligning database scaling behavior with the specific predictability of each workload.
2
Select the optimal capacity mode for the user posts table.
Choose On-Demand capacity mode.
On-demand mode eliminates the need to overprovision for spikes, preventing idle capacity costs for unpredictable workloads.
3
Select the optimal capacity mode for the user profile table.
Choose Provisioned capacity mode with Auto Scaling enabled.
Predictable workloads benefit from the lower pricing of provisioned capacity, while Auto Scaling handles gradual shifts during daytime hours.

Key Concept

Selecting cost-optimal capacity modes and scaling policies in Amazon DynamoDB based on workload predictability.
Estimated Time:2m 0s
Question 1267Question →

An online ticketing platform hosts its application on Amazon EC2 instances and its database on an Amazon RDS for MySQL DB instance in the us-east-1 Region. The platform needs to implement a disaster recovery (DR) strategy in the us-west-2 Region. The strategy must support a Recovery Point Objective (RPO) of 5 minutes and a Recovery Time Objective (RTO) of 30 minutes. Which combination of actions should a solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create a cross-region read replica of the RDS DB instance in the us-west-2 Region.; Configure Amazon Route 53 with a failover routing policy and associate it with health checks to route traffic to the secondary Region.

Answer

Create a cross-region read replica of the RDS DB instance in the secondary Region, and configure Amazon Route 53 with a failover routing policy and health checks to route traffic to the secondary Region.
Creating a cross-region read replica of the database satisfies the 5-minute RPO requirement because data is replicated asynchronously to the secondary Region. Promoting the replica to a primary instance takes under 30 minutes. Concurrently, Route 53 failover routing with health checks detects primary region outages and automatically redirects client application traffic to the secondary Region, keeping overall RTO under 30 minutes.

Step-by-Step Solution

1
Address the 5-minute RPO constraint for database replication.
Create a cross-region read replica of the RDS MySQL DB instance in the target DR Region.
This establishes asynchronous replication across Regions, keeping data lag down to seconds or minutes and meeting the RPO.
2
Address the 30-minute RTO constraint for traffic failover.
Configure Route 53 with a failover routing policy and configure active health checks on the primary endpoints.
Health checks monitor endpoint status and failover routing redirects traffic to the DR Region within minutes of a detected failure.

Key Concept

Combining cross-region database replication with Route 53 failover routing to meet low RTO and RPO disaster recovery objectives.
Question 1268Question →

A financial services company is migrating a legacy payment settlement system to AWS. The system must process account withdrawal requests sequentially on a per-account basis to prevent overdrafts. The transaction volume is highly spiky and can reach up to 80008{}000 requests per second. The company wants to design a decoupled, resilient, and serverless messaging architecture that guarantees ordering within each account while maximizing throughput and minimizing operational overhead.

Which combination of steps should a solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create an Amazon SQS FIFO queue and enable high-throughput mode for the queue.; Publish the withdrawal events using the unique account number as the Message Group ID.

Answer

The correct solution is to create an Amazon SQS FIFO queue with high-throughput mode enabled, and publish the withdrawal events using the unique account number as the Message Group ID.
To satisfy both ordering and scale requirements, the architecture must use Amazon SQS FIFO with high-throughput mode enabled. Using the account number as the Message Group ID ensures that messages for the same account are processed in order, while messages for different accounts can be processed concurrently by multiple consumers. This design provides both the necessary ordering guarantees and the scalability to support 8,000 transactions per second.

Step-by-Step Solution

1
Select a message queue service that guarantees first-in, first-out (FIFO) order.
Amazon SQS FIFO queue is selected because SQS Standard queues cannot guarantee message ordering.
Sequential processing on a per-account basis is a hard requirement to prevent overdrafts.
2
Enable high-throughput mode on the SQS FIFO queue.
The queue can support throughput scales of up to 80008{}000 transactions per second or more.
Normal SQS FIFO queues are limited to 300 transactions per second (or 3,000 with batching) by default, which is insufficient for the peak load of 8,000 requests per second.
3
Configure the message grouping strategy using a granular partition key.
The unique account number is used as the Message Group ID for each SQS FIFO message.
Using the account number ensures strict ordering within each individual account while allowing parallel processing across different accounts to meet the high-throughput demands.

Key Concept

Decoupling event-driven workloads with Amazon SQS FIFO high-throughput mode and Message Group IDs.
Question 1269Question →

A media streaming platform stores user playback state and profile metadata. The application requires a database that can handle over 50,00050,000 writes per second of small (<2 KB< 2 \text{ KB}) items. The database must provide sub-millisecond read latency for active sessions. The platform is deployed in both the `us-east-1` and `eu-west-3` regions, and users in both regions must be able to read and write to their local region with minimal latency.

Which combination of database strategies should a solutions architect recommend to meet these performance requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure Amazon DynamoDB global tables with replicas in us-east-1 and eu-west-3.; Design the database table using a combination of UserID and SessionID as the partition key.

Answer

The correct strategies are to configure Amazon DynamoDB global tables with replicas in us-east-1 and eu-west-3, and to design the database table using a combination of UserID and SessionID as the partition key.
The correct approach uses Amazon DynamoDB global tables to handle multi-region, active-active low-latency reads and writes, combined with a high-cardinality partition key (UserID and SessionID) to distribute writes evenly across partitions and avoid hot partition bottlenecks.

Step-by-Step Solution

1
Analyze the active-active multi-region requirement.
Identify that the solution must allow local reads and writes in both US and European regions.
Amazon DynamoDB global tables are designed to support fully managed, active-active, multi-region deployments with low-latency local reads and writes.
2
Evaluate the partition key design for high write throughput.
Select a partition key with high cardinality to distribute data and requests uniformly.
Using a combined key like UserID and SessionID ensures uniform distribution, whereas a sequential key like a timestamp would create a hot partition and throttle write throughput.

Key Concept

Designing high-performing, multi-region database architectures using Amazon DynamoDB global tables and proper partition key design to prevent hot partitions.
Question 1270Question →

A logistics company is building a serverless tracking application. The application must process incoming telemetry events that are highly unpredictable, with long periods of zero activity followed by sudden bursts of thousands of requests per second. The telemetry events must be processed in the exact order they are received to ensure tracking accuracy. Additionally, the application requires a continuous, 24/7 background reconciliation process to run reports on the accumulated telemetry data. The company wants to minimize compute and database costs, ensuring that they do not pay for idle resources during periods of no traffic. Which two of the following architectural options should the Solutions Architect select to design a cost-effective and functionally correct solution? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Store the telemetry data in Amazon DynamoDB configured with on-demand capacity mode.; Deploy the 24/7 background reconciliation process on Amazon ECS with AWS Fargate.

Answer

Store the telemetry data in Amazon DynamoDB configured with on-demand capacity mode, and deploy the 24/7 background reconciliation process on Amazon ECS with AWS Fargate.
The correct architecture uses Amazon DynamoDB on-demand capacity mode for the telemetry storage and Amazon ECS on AWS Fargate for the continuous 24/7 background reconciliation engine. Amazon DynamoDB on-demand mode is optimal for spiky, unpredictable workloads because it scales to zero throughput costs when idle, charging only for active reads and writes. For the continuous 24/7 background reporting engine, containerized compute on Amazon ECS with AWS Fargate is more cost-effective than serverless function runtimes, which are priced for short-lived event-driven execution.

Step-by-Step Solution

1
Analyze the database capacity mode requirement for unpredictable, spiky telemetry events with long idle periods.
Identify that Amazon DynamoDB on-demand capacity mode scales down to zero throughput cost when there is no traffic, making it more cost-effective than provisioned capacity mode.
Choosing provisioned capacity mode (even with Auto Scaling) incurs a continuous hourly charge for the minimum configured write/read capacity units during idle periods.
2
Analyze the compute requirement for the 24/7 continuous reconciliation process.
Identify that running containerized compute on Amazon ECS with AWS Fargate is more cost-effective for continuous 24/7 execution compared to AWS Lambda.
AWS Lambda is designed for short-lived, event-driven executions, and running it continuously leads to high invocation and duration costs.
3
Combine the database and compute architectures for cost efficiency.
Select the combination of Amazon DynamoDB on-demand capacity mode and Amazon ECS with AWS Fargate.
This combination ensures zero database costs during idle periods and minimizes continuous compute costs for the 24/7 background engine.

Key Concept

Designing serverless and automated scaling architectures that align cost structures with usage patterns, specifically using on-demand capacity for unpredictable workloads with idle times and containerized compute for continuous, long-running processes.
Question 1271Question →

A healthcare technology provider manages patient portals across multiple AWS regions. To satisfy regulatory compliance, the provider must identify unauthorized configuration changes to security groups and detect whether any EC2 instances are communicating with known malicious IP addresses. The provider requires a solution that centralizes these security findings and automates threat detection without impacting application performance. Which combination of actions should the solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Enable Amazon GuardDuty in the target regions to analyze VPC Flow Logs and DNS query logs for identifying communications with malicious IP addresses.; Enable AWS Config to continuously monitor and record configuration changes of VPC security groups to track compliance.

Answer

The solutions architect should recommend enabling Amazon GuardDuty to analyze VPC Flow Logs and DNS query logs, and enabling AWS Config to monitor and record changes to VPC security groups.
To detect unauthorized changes to security groups, AWS Config is the appropriate tool because it continuously monitors and records configuration details. To detect traffic to known malicious IPs without affecting performance, Amazon GuardDuty is the correct tool because it analyzes metadata from sources like VPC Flow Logs and DNS logs in an isolated plane.

Step-by-Step Solution

1
Address the requirement for configuration auditing.
AWS Config is configured to track security groups.
AWS Config tracks configuration history and compliance status of AWS resources, which directly satisfies the requirement to identify unauthorized security group changes.
2
Address the threat detection requirement for malicious IP communication.
Amazon GuardDuty is enabled to monitor VPC Flow Logs and DNS query logs.
Amazon GuardDuty analyzes logs in the background without affecting performance, matching traffic patterns against known threat feeds to identify communication with malicious hosts.

Key Concept

Continuous security monitoring using AWS Config for resource changes and Amazon GuardDuty for intelligent, out-of-band threat detection.
Estimated Time:2m 0s
Question 1272Question →

A research institution is deploying a high-performance seismic data visualization application on a cluster of Amazon EC2 instances. The application requires extremely low-latency, high-throughput node-to-node communication for rendering complex 3D geological models in real time. The instances must read and write to a shared directory containing several terabytes of active simulation files. The application's visualization service runs on a custom port 9000, which is exposed to internal researchers through an Application Load Balancer (ALB). Which combination of configuration steps should the solutions architect implement to meet these requirements?

Show answer & explanation

Answer: Launch the instances in a cluster placement group, mount an Amazon Elastic File System (Amazon EFS) on all instances to share the simulation files, and configure the load balancer's target group health check to use port 9000.

Answer

Launch the instances in a cluster placement group, mount an Amazon Elastic File System (Amazon EFS) on all instances to share the simulation files, and configure the load balancer's target group health check to use port 9000.
The correct configuration addresses all constraints. It uses a cluster placement group to achieve minimal network latency between instances. It mounts Amazon EFS to provide a shared, scalable file system for concurrent read/write operations on simulation files. Lastly, it maps the target group health check to the active application port of 9000, preventing health check failures.

Step-by-Step Solution

1
Evaluate network latency requirements.
Determine that a cluster placement group is necessary to achieve the low-latency, high-throughput node-to-node communication required by the tightly coupled visualization application.
Cluster placement groups place instances close together inside a single Availability Zone, enabling low-latency, high-throughput network performance.
2
Select the appropriate shared storage solution.
Choose Amazon EFS as the shared file system for the active simulation files.
Amazon EFS natively supports concurrent read/write access from multiple EC2 instances, whereas standard EBS volumes do not support multi-instance concurrent writes without complex cluster-aware filesystem configurations.
3
Configure the Application Load Balancer health check.
Set the health check port to 9000.
The application listens on port 9000. Setting the health check to the default port (like port 80) would result in health check failures and cause the load balancer to route no traffic to the instances.

Key Concept

Designing high-performing and elastic compute environments requires choosing the correct EC2 placement groups for latency requirements, selecting appropriate shared storage, and matching load balancer health checks to the application's active ports.
Estimated Time:1m 30s
Question 1273Question →

An online learning platform uses a web application to manage student course enrollments. The system must decouple the front-end enrollment portal from the back-end database processing service. To ensure fair registration for courses with limited capacity, enrollment requests must be processed in the exact sequence they are submitted. Which messaging service configuration should a solutions architect recommend to decouple these components while maintaining the correct message sequence?

Show answer & explanation

Answer: An Amazon SQS FIFO queue

Answer

An Amazon SQS FIFO queue
An Amazon SQS FIFO queue is the correct solution because it successfully decouples the application tiers and guarantees that messages are processed in the exact order they are sent, satisfying the requirement for sequential enrollment processing.

Step-by-Step Solution

1
Analyze the requirement for decoupling the web portal and the database service.
Identify that a message queue or event bus is needed to decouple the frontend from the backend.
Decoupling ensures the front-end application remains responsive even if the back-end database has processing delays.
2
Evaluate the ordering constraint for the enrollment requests.
Identify that the solution must guarantee first-in, first-out (FIFO) processing to ensure fair registration.
Standard message queues do not guarantee strict message ordering.
3
Select the AWS service that provides both decoupling and strict ordering.
Determine that Amazon SQS FIFO (First-In-First-Out) queues meet both criteria.
SQS FIFO queues guarantee that the order in which messages are sent and received is strictly preserved.

Key Concept

Using Amazon SQS FIFO queues to decouple application components while maintaining message ordering.
Estimated Time:45s
Question 1274Question →

A company hosts a REST API on Amazon EC2 instances in an Auto Scaling group (ASG) behind an Application Load Balancer (ALB). The API service is configured to bind and listen on custom TCP port 5001. The EC2 instances are deployed in private subnets, and their security group permits inbound traffic on port 5001 from the ALB's security group. After a scale-out event, the newly launched instances are repeatedly marked as unhealthy by the ALB target group and are subsequently terminated by the ASG. An investigation reveals that the ALB target group health check is set to the default port, and the private subnet Network ACLs only have explicit rules allowing inbound TCP port 5001 and outbound TCP port 5001. Which two configuration changes must the solutions architect implement to resolve the health check failures and ensure the instances remain in service? (Select two.)

Select all that apply

Show answer & explanation

Answer: Modify the target group's health check port setting to use port 5001.; Add an outbound rule to the private subnet Network ACL to allow traffic to ephemeral ports (1024−655351024-65535).

Answer

Modify the target group's health check port setting to use port 5001, and add an outbound rule to the private subnet Network ACL to allow traffic to ephemeral ports (1024−655351024-65535).
To resolve the issue, the Solutions Architect must address two distinct configuration gaps. First, the Application Load Balancer target group must be modified to perform health checks on the custom port (5001) instead of the default port (80), ensuring it aligns with the API application port. Second, because Network ACLs are stateless, return traffic from the instances to the ALB must be explicitly allowed. Since the ALB initiates connections to the instances, the response traffic is sent back to the ALB's ephemeral client ports, requiring an outbound Network ACL rule to allow the ephemeral port range (1024−655351024-65535).

Step-by-Step Solution

1
Identify the cause of target group health check failure.
The target group defaults to port 80 for health checks, whereas the API listens on port 5001. Modifying the target group's health check configuration to target port 5001 is required.
Health checks must target the port where the application actually listens, otherwise the load balancer will receive connection timeouts or connection refused errors.
2
Analyze network traffic flow through the subnet Network ACLs.
The Network ACL currently only allows outbound traffic on TCP port 5001. Because Network ACLs are stateless, the return traffic from the instances back to the load balancer (which uses ephemeral ports 1024−655351024-65535) is blocked.
To allow communication, the Network ACL must allow inbound request traffic on port 5001 and outbound return traffic on the ephemeral port range.

Key Concept

ELB health checks require target group configuration alignment with application ports, and stateless network components like Network ACLs require explicit configuration of return traffic to ephemeral ports.
Estimated Time:2m 0s
Question 1275Question →

A financial technology company wants to ensure that all Network Access Control Lists (Network ACLs) within their production VPC remain compliant with their security policy, which forbids any inbound rule allowing public access on port 22. The security team needs to detect any configuration changes to Network ACLs in real-time, log the compliance status, and automatically send alerts to an administrator. Which solution meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Use AWS Config to record configuration changes for Network ACLs. Create a managed AWS Config rule to evaluate compliance, and configure an Amazon EventBridge rule to trigger on non-compliant evaluation results to send notifications to an Amazon Simple Notification Service (Amazon SNS) topic.

Answer

The correct solution involves utilizing AWS Config to monitor Network ACL resource compliance combined with Amazon EventBridge and Amazon SNS for real-time alerting.
The correct solution leverages AWS Config to monitor resource configuration changes and evaluate compliance against rules. EventBridge triggers notifications via Amazon SNS on non-compliant events. This utilizes fully managed AWS services to achieve real-time monitoring and alerting with minimal operational effort.

Step-by-Step Solution

1
Record Network ACL configuration history using AWS Config.
AWS Config continuously tracks and stores configuration details of Network ACLs in the VPC.
Config tracking is required to detect changes and verify compliance against rules.
2
Create an AWS Config rule to check Network ACL compliance.
Changes that violate the rule (e.g. allowing ingress SSH traffic from 0.0.0.0/0) mark the Network ACL resource as non-compliant.
A compliance rule acts as the monitoring and threat detection mechanism.
3
Configure an Amazon EventBridge rule targeted at AWS Config compliance change events to trigger Amazon SNS.
An email or message is sent automatically to administrators in real-time when compliance changes occur.
This completes the threat detection and alerting pipeline with minimal operational overhead.

Key Concept

Continuous security compliance monitoring and automated alerting using AWS Config, EventBridge, and SNS.
Question 1276Question →

A biotechnology firm is deploying a distributed genomic sequencing analysis suite on Amazon EC2 instances within an Auto Scaling group. The processing nodes require low-latency, high-throughput network communication between each other to assemble DNA sequence fragments. A web-based management portal for the suite is hosted on the same instances and listens on custom TCP port 50005000. An Application Load Balancer (ALB) routes traffic to this portal. Which combination of configurations should a solutions architect implement to support this workload? (Choose two.)

Select all that apply

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group.; Configure the ALB target group health check port to 50005000.

Answer

Launching the EC2 instances in a cluster placement group and configuring the Application Load Balancer target group health check port to 50005000.
The correct configurations are to launch the instances in a cluster placement group and configure the ALB target group health checks to use port 50005000. Launching instances in a cluster placement group places them close together inside a single Availability Zone, which minimizes latency and maximizes throughput for node-to-node communication. Configuring the health check port to match the custom application port (50005000) prevents the load balancer from incorrectly marking the instances as unhealthy.

Step-by-Step Solution

1
Analyze the networking requirements of the distributed genomic sequencing workload.
Identify the need for high-throughput, low-latency node-to-node communication.
Knowing the communication latency requirements directs the choice of the appropriate EC2 placement group.
2
Evaluate the EC2 placement group options based on latency needs.
Select a cluster placement group and reject spread or partition placement groups.
Cluster placement groups position instances within a single Availability Zone to achieve low-latency and high-throughput network performance.
3
Analyze the application routing and health check parameters.
Identify that the web portal listens on custom TCP port 50005000, requiring target group health checks to target port 50005000 instead of default port 8080.
Aligning the health check port with the application port prevents the load balancer from marking healthy nodes as offline.

Key Concept

Selecting the correct EC2 placement group and configuring load balancer health checks to match custom application ports.
Question 1277Question →

A solutions architect is designing a database schema for a smart utility grid system that collects hourly electricity usage readings from millions of smart meters. The data is written to an Amazon DynamoDB table. The table is currently configured with a partition key of `ReadingDate` (formatted as `YYYY-MM-DD`) and a sort key of `MeterID#Timestamp` to support daily regional reporting queries. During peak reporting hours, the application experiences write throttling and receives `ProvisionedThroughputExceededException` errors, even though the total consumed Write Capacity Units (WCUs) are far below the table's provisioned limit. Which database design modification will resolve this write throttling and optimize performance?

Show answer & explanation

Answer: Redesign the DynamoDB table to use `MeterID` as the partition key and a composite sort key of `ReadingDate#Timestamp`, and create a Global Secondary Index (GSI) with `ReadingDate` as the partition key to support the daily reporting queries.

Answer

Redesign the DynamoDB table to use MeterID as the partition key and a composite sort key of ReadingDate#Timestamp, and create a Global Secondary Index (GSI) with ReadingDate as the partition key to support the daily reporting queries.
Redesigning the table to use a high-cardinality attribute like the meter identifier as the partition key distributes the write workload evenly across all partitions. By pairing it with a composite sort key containing the date and timestamp, and setting up a global secondary index with the date as the partition key, the application can distribute writes to avoid throttling while still enabling efficient daily reporting queries.

Step-by-Step Solution

1
Analyze the cause of the ProvisionedThroughputExceededException.
Identify that using ReadingDate (formatted as YYYY-MM-DD) as the partition key results in all incoming writes for a specific day targeting the same partition key value, creating a hot partition.
DynamoDB partitions data based on the partition key value. When millions of smart meters write data simultaneously with the same ReadingDate value, they all target the same partition.
2
Select a partition key with high cardinality to distribute writes.
Choose MeterID as the partition key because it has millions of unique values, allowing DynamoDB to distribute write operations evenly across multiple partitions.
Distributing writes across many partition keys prevents any single partition from exceeding the physical limit of 1,0001,000 Write Capacity Units (WCUs) per partition.
3
Maintain support for regional daily reporting queries.
Combine ReadingDate and Timestamp into a composite sort key (ReadingDate#Timestamp) and create a Global Secondary Index (GSI) with ReadingDate as the partition key.
This allows the application to perform query operations on ReadingDate to retrieve all readings for a specific day, fulfilling the daily reporting requirements.

Key Concept

DynamoDB partition key design and hot partition prevention
Estimated Time:2m 0s
Question 1278Question →

A fleet management company collects real-time GPS telemetry from 10,00010,000 vehicles. The telemetry data is sent continuously at a highly predictable rate of 2,0002,000 writes per second. Users frequently query the GPS coordinate data from the last 1212 hours via a monitoring dashboard. Historical data older than 1212 hours is rarely accessed but must be retained for up to 33 years for regulatory compliance. The company wants to design a highly cost-optimized database and storage architecture. Which two database configurations should a solutions architect recommend to meet these requirements? (Select TWO).

Select all that apply

Show answer & explanation

Answer: Configure the Amazon DynamoDB table using provisioned capacity mode with Auto Scaling enabled to store the active data.; Enable DynamoDB Time to Live (TTL) on the table to automatically delete items older than 1212 hours, and use DynamoDB Streams to archive the deleted records to Amazon S3.

Answer

The solutions architect should recommend configuring the Amazon DynamoDB table with provisioned capacity mode and Auto Scaling enabled for the active data, and enabling DynamoDB Time to Live (TTL) to automatically delete records older than 1212 hours while archiving them to Amazon S3 via DynamoDB Streams.
The correct options are configuring the Amazon DynamoDB table with provisioned capacity mode and Auto Scaling enabled, and enabling DynamoDB TTL combined with DynamoDB Streams to archive deleted records to Amazon S3. Provisioned capacity mode with Auto Scaling is highly cost-effective for continuous, predictable workloads because it avoids the higher per-request cost of on-demand capacity mode. Enabling TTL automatically removes data older than 1212 hours from DynamoDB at no cost, and archiving those records via streams to Amazon S3 ensures that long-term historical data is stored in a cost-optimized storage tier rather than expensive database storage.

Step-by-Step Solution

1
Analyze the workload characteristics of the active write stream.
The workload consists of a steady, continuous stream of 2,0002,000 writes per second from 10,00010,000 vehicles, which is highly predictable.
Identifying the predictability of the workload determines the most cost-effective database capacity mode (provisioned vs. on-demand).
2
Evaluate the query patterns and data lifecycle requirements.
Dashboard users query only the last 1212 hours of data, while historical data must be kept for 33 years but is rarely accessed.
This establishes that the active database only needs to store a small window of data, and the rest can be archived to a cheaper storage tier.
3
Select the database and storage configuration that minimizes costs.
Use DynamoDB with provisioned capacity mode and Auto Scaling for active data, and configure DynamoDB TTL and Streams to automatically delete active records and archive them to Amazon S3.
Provisioned capacity is cheaper for predictable workloads. DynamoDB TTL deletes data at no extra cost, and S3 is the most cost-effective storage class for rarely accessed compliance data.

Key Concept

Selecting the most cost-effective database capacity mode and establishing tiered storage policies to minimize long-term data retention costs.
Question 1279Question →

A gaming studio is launching a multiplayer game where players can acquire, trade, and consume in-game items. The backend application must process these inventory state change events in the exact chronological order they occur for each player. Processing these events out of sequence will result in inventory corruption or item duplication. The game experiences unpredictable spikes in traffic, and the solution must scale to process millions of updates daily while allowing events from different players to be processed concurrently. Which solution meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Publish the inventory state change events to an Amazon SQS FIFO queue, using the player ID as the MessageGroupId. Configure an AWS Lambda function to consume and process the messages.

Answer

Publish the inventory state change events to an Amazon SQS FIFO queue, using the player ID as the MessageGroupId, and configure an AWS Lambda function to consume and process the messages.
An Amazon SQS FIFO queue preserves the exact order of messages within a message group. Using the player ID as the MessageGroupId ensures that all inventory events for a specific player are processed in the order they were sent, while allowing events for different players (different MessageGroupIds) to be processed concurrently. Lambda provides a serverless, automatically scaling consumer that requires minimal operational overhead.

Step-by-Step Solution

1
Identify the system requirement for strict ordering within individual user transaction streams.
Determine that inventory transactions must be processed chronologically per player, but can be processed concurrently across different players.
This establishes the need for a message grouping partition mechanism.
2
Evaluate Amazon SQS queue options for ordering guarantees.
Select Amazon SQS FIFO queues rather than standard queues, as standard queues do not guarantee first-in, first-out delivery.
To prevent out-of-order execution that would lead to database or inventory state corruption.
3
Configure the MessageGroupId attribute on the queue.
Map the MessageGroupId to the unique player ID to enable concurrent consumer processing across different players while preserving strict sequence per player.
Using a single static MessageGroupId would bottleneck the queue by serializing all messages, failing the concurrency requirements.

Key Concept

Preserving message ordering within logical groups using Amazon SQS FIFO queues

Alternative Method

An alternative design could use Amazon Kinesis Data Streams with the player ID as the partition key. This guarantees in-order processing per partition (player). However, Kinesis requires manual provisioning of shards, handling shard splits/merges, and managing consumer offset tracking, which results in significantly higher operational overhead compared to the fully serverless Amazon SQS FIFO queue solution.
Estimated Time:2m 0s
Question 1280Question →

A marketing company operates a notification platform that sends SMS messages to customers. The timing of promotional campaigns is unpredictable, resulting in sudden, massive spikes in write operations to a database table that records delivery logs. During these campaigns, write traffic surges from 00 to over 15,00015,000 writes per second within seconds, and then quickly drops back to 00 once the run is complete. Which database capacity configuration is the most cost-effective and reliable for this workload?

Show answer & explanation

Answer: Configure the database table in Amazon DynamoDB using On-Demand capacity mode.

Answer

Configuring the database table in Amazon DynamoDB using On-Demand capacity mode.
On-Demand capacity mode is the optimal choice because the workload has sudden, unpredictable peaks and long idle periods. It handles instantaneous surges up to the table's previous peak traffic immediately without throttling, and only charges for the actual requests processed, eliminating idle capacity costs.

Step-by-Step Solution

1
Analyze the workload pattern and performance requirements.
The workload is highly unpredictable, idle most of the time, and requires a rapid scale-up from 00 to over 15,00015,000 writes per second within seconds.
Understanding the latency tolerance and scaling rate helps select the correct scaling mechanism.
2
Evaluate DynamoDB capacity modes against this workload.
Provisioned capacity with Auto Scaling takes minutes to react, resulting in throttling. Fixed provisioned capacity leads to high idle costs. On-Demand capacity mode handles immediate spikes without throttling and costs nothing when idle.
DynamoDB On-Demand capacity mode is designed for workloads that are unpredictable and spiky.
3
Select the most cost-effective database configuration.
On-Demand capacity mode is the only option that meets both the instant scaling requirements and cost-efficiency goals.
It avoids both throttling and paying for provisioned capacity during idle hours.

Key Concept

DynamoDB capacity modes selection based on workload predictability and spike latency.
Estimated Time:2m 0s
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