All practice questions

1462 questions

Question 981Question

A company is migrating a financial transaction application to AWS. The application database tier uses Amazon RDS for PostgreSQL. The database must be highly available with automatic failover to support a low recovery time objective (RTO) during an Availability Zone outage. Additionally, the application must offload read-intensive reporting queries from the primary database to prevent performance degradation on write operations.

Which combination of database configurations will meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure a Multi-AZ DB instance deployment to achieve automatic failover across different Availability Zones.; Deploy RDS Read Replicas in different Availability Zones to handle the reporting query workload.

Answer

Deploying a Multi-AZ DB instance configuration to provide high availability and creating RDS Read Replicas to offload reporting workloads is the correct combination of configurations.
To meet the requirements, the solutions architect should deploy a Multi-AZ DB instance configuration and create RDS Read Replicas. A Multi-AZ DB instance deployment provides high availability and automatic failover by synchronously replicating data to a standby instance in a different Availability Zone. Read Replicas allow the database to offload read-intensive reporting workloads by asynchronously replicating data to one or more read-only instances, which prevents read queries from affecting the primary database's write performance.

Step-by-Step Solution

1
Identify the high availability and failover requirements.
Determine that an Amazon RDS Multi-AZ DB instance deployment is needed to provide synchronous replication and automatic failover in the event of an Availability Zone outage.
Multi-AZ deployments provide built-in high availability and automatic failover for Amazon RDS databases.
2
Identify the read scaling and performance requirements.
Determine that one or more RDS Read Replicas must be created to handle read-heavy reporting queries.
Read replicas use asynchronous replication to offload read traffic from the primary DB instance, protecting write performance.
3
Eliminate options that use incorrect failover mechanisms or disaster recovery strategies.
Reject options proposing read replicas as automatic failover targets, Route 53 latency routing for database failover, or Pilot Light DR for high availability.
Read replicas do not support automatic HA failover, Route 53 latency routing does not manage RDS failover, and Pilot Light configurations have higher RTOs and are meant for disaster recovery, not HA.

Key Concept

Configuring Amazon RDS for high availability using Multi-AZ deployments and scaling read capacity using Read Replicas.
Question 982Question

An application experiences sudden, highly unpredictable spikes in database write traffic during marketing events, followed by long periods of complete inactivity. Which Amazon DynamoDB capacity configuration is the most cost-effective for this workload?

Show answer & explanation

Answer: DynamoDB in On-Demand capacity mode

Answer

DynamoDB in On-Demand capacity mode is the most cost-effective option for unpredictable, spiky workloads with idle periods because it scales instantly and charges only for actual read and write requests.
DynamoDB in On-Demand capacity mode is designed for workloads with highly unpredictable traffic patterns and periods of inactivity. It automatically manages capacity and instantly scales to meet demand without requiring manual configuration or auto-scaling policies. Because it charges only for the read and write request units actually consumed, it eliminates costs during idle periods, making it the most cost-effective choice.

Step-by-Step Solution

1
Analyze the workload characteristics
The workload has sudden, highly unpredictable write spikes and long periods of complete inactivity.
Understanding the traffic pattern helps select the right capacity planning strategy.
2
Compare DynamoDB capacity modes for cost and responsiveness
On-Demand mode scales instantly to handle spikes and charges 00 when idle. Provisioned capacity requires setting minimum throughput, which incurs continuous costs even when idle, and standard auto-scaling takes time to scale up, causing potential throttling during sudden spikes.
Comparing how each capacity mode handles unpredictable spikes and idle times reveals the most cost-effective choice.

Key Concept

Choosing between Provisioned and On-Demand capacity modes in DynamoDB to optimize costs based on workload predictability.
Question 983Question

A company is migrating its corporate Windows-based file share application to AWS. The application uses the Server Message Block (SMB) protocol and integrates with Microsoft Active Directory to enforce file-level permissions (ACLs) and user storage quotas. During peak hours, the storage solution must scale dynamically to deliver up to 2 GB/s2\text{ GB/s} of throughput with sub-millisecond latencies. Which storage solution should a solutions architect recommend to meet these requirements?

Show answer & explanation

Answer: Amazon FSx for Windows File Server

Answer

Amazon FSx for Windows File Server
The correct option is the one specifying Amazon FSx for Windows File Server. This service is designed specifically for Windows workloads, offering native support for the SMB protocol, Windows ACLs, and Microsoft Active Directory. It supports SSD storage with sub-millisecond latencies and can scale throughput capacity up to 2 GB/s2\text{ GB/s} to meet high-performance demands.

Step-by-Step Solution

1
Analyze the application requirements: Windows-based, SMB protocol, Microsoft Active Directory integration, ACLs, and user quotas.
Identify that the storage solution must natively support Windows file system features (NTFS and SMB).
This narrows down the choices to Windows-native file systems.
2
Analyze the performance requirements: up to 2 GB/s2\text{ GB/s} throughput and sub-millisecond latency.
Determine that the storage solution must scale throughput independently and offer solid-state drive (SSD) performance levels.
This rules out solutions with limited performance or high latency profiles like S3 File Gateway.
3
Compare the remaining options against AWS service capabilities.
Amazon FSx for Windows File Server fits all criteria, whereas Amazon EFS is NFS-based, Amazon EBS gp3 doesn't support Multi-Attach or clustered SMB out-of-the-box, and S3 File Gateway cannot meet the performance and Windows quota requirements.
To select the highest performance option that meets all business and protocol constraints.

Key Concept

Selecting high-performance, shared storage solutions that support native Windows protocols (SMB) and security integrations.
Question 984Question

A company is designing a serverless backend for an application. The database experiences sudden, unpredictable spikes in traffic followed by long periods of idle time. The application also needs to process transactional orders sequentially in the exact order they are received. Which two options should the solutions architect select to meet these requirements in the most cost-effective manner?

Select all that apply

Show answer & explanation

Answer: Configure Amazon DynamoDB with on-demand capacity mode to store application data.; Use an Amazon SQS FIFO queue to handle the transactional orders.

Answer

Use Amazon DynamoDB with on-demand capacity mode and an Amazon SQS FIFO queue to process transactional orders.
To achieve cost-efficiency for a database with unpredictable, spiky traffic and long idle times, Amazon DynamoDB on-demand capacity mode is the best choice because it scales instantly and charges only for active requests, incurring zero idle costs. To guarantee that transactional orders are processed in the exact sequence they are received, an Amazon SQS FIFO queue must be used as it enforces strict ordering.

Step-by-Step Solution

1
Analyze the database workload patterns.
The workload has unpredictable spikes and long idle times.
This determines that Amazon DynamoDB on-demand capacity mode is the most cost-effective option as it scales to zero during idle times.
2
Analyze the message delivery and ordering requirements.
Messages must be processed sequentially in the exact order they are received.
This requires an Amazon SQS FIFO queue because SQS standard queues do not guarantee strict ordering.

Key Concept

Selecting cost-effective serverless database scaling options and message ordering mechanisms
Estimated Time:1m 30s
Question 985Question

A research institution stores two types of data in a single Amazon S3 bucket using different prefixes:

* Genomic Sequence Datasets (stored under the `genomics/` prefix): The average file size is 250 MB250\text{ MB}. These files are accessed frequently during the first 35 days after creation. After 35 days, they are rarely accessed but must be retained for 10 years10\text{ years} for regulatory compliance. When access is required, the datasets must be retrievable within minutes.
* Intermediate Pipeline Run Logs (stored under the `logs/` prefix): The average file size is 10 KB10\text{ KB}. These files are only needed for debugging within the first 14 days of creation, after which they can be permanently deleted.

Which combination of actions will meet these requirements in the most cost-effective manner? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create a lifecycle rule for the `genomics/` prefix to transition the objects to Amazon S3 Glacier Flexible Retrieval after 35 days.; Create a lifecycle rule for the `logs/` prefix to expire the objects after 14 days.

Answer

Transitioning the genomic datasets to Amazon S3 Glacier Flexible Retrieval after 35 days, and expiring the pipeline run logs after 14 days directly from S3 Standard.
Transitioning the genomic datasets to Amazon S3 Glacier Flexible Retrieval after 35 days is the most cost-effective option because it matches the 10-year retention requirement, and the Glacier Flexible Retrieval class supports Expedited retrievals, which can return objects within 1 to 5 minutes, satisfying the requirement to retrieve data within minutes. Expiring the pipeline run logs after 14 days directly from S3 Standard is the most cost-effective option for that prefix because the average file size is 10 KB10\text{ KB} (well below the 128 KB128\text{ KB} minimum billing size for Infrequent Access tiers) and the retention period is only 14 days (well below the 30-day minimum billing duration). Transitioning these logs to S3 Standard-IA or S3 One Zone-IA would incur significant cost penalties due to minimum storage size and duration constraints.

Step-by-Step Solution

1
Analyze the access pattern and size of the genomic sequence datasets.
The datasets are large (250 MB250\text{ MB}), accessed frequently for 35 days, then rarely accessed for 10 years, and must be retrievable within minutes.
To identify the correct storage class that supports minutes-level retrieval and has low storage cost for long-term retention.
2
Evaluate S3 Glacier tiers for the genomic sequence datasets.
Amazon S3 Glacier Flexible Retrieval supports Expedited retrieval (1-5 minutes) and has low cost for 10-year retention, whereas Glacier Deep Archive takes at least 12 hours.
To select the most cost-effective tier that meets the minutes-level retrieval constraint.
3
Analyze the access pattern, size, and retention requirements of the intermediate pipeline run logs.
The logs are small (10 KB10\text{ KB}), only needed for 14 days, and then can be deleted.
To determine if transitioning to an Infrequent Access or Archive tier is financially beneficial.
4
Calculate potential penalties for transitioning the logs to S3 Standard-IA or S3 One Zone-IA.
Transitioning to IA classes would charge for 128 KB128\text{ KB} instead of 10 KB10\text{ KB}, and deleting after 14 days would trigger early deletion fees for the 30-day minimum duration.
To verify that keeping the logs in S3 Standard and expiring them after 14 days is the most cost-effective strategy.

Key Concept

Cost-optimized storage tiering depends on understanding minimum object sizes, minimum storage durations, and retrieval times across S3 storage classes.
Question 986Question

A materials science laboratory is deploying a distributed crystalline structure simulation on Amazon EC2 instances. The simulation nodes run a specialized Message Passing Interface (MPI) application that requires sub-10 microsecond network latency. The coordination service runs on port 90959095. A Network Load Balancer (NLB) is configured to route traffic to the instances, but all instances are currently marked as unhealthy in the target group, and latency between the nodes is suboptimal. Which combination of actions will resolve the latency issues and fix the health check failures?

Show answer & explanation

Answer: Deploy the EC2 instances in a cluster placement group within a single Availability Zone, and update the Network Load Balancer target group health check port to 9095.

Answer

Deploy the EC2 instances in a cluster placement group within a single Availability Zone, and update the Network Load Balancer target group health check port to 9095.
Deploying the instances in a cluster placement group within a single Availability Zone ensures the lowest possible latency and highest network performance, which is required for tightly coupled MPI applications. Updating the target group health check port to 9095 aligns the Network Load Balancer's health checks with the custom port where the simulation's coordination service is listening, resolving the health check failures.

Step-by-Step Solution

1
Analyze the network latency requirement.
Tightly coupled MPI applications require sub-10 microsecond latency, which is only supported by launching instances in a cluster placement group.
A cluster placement group places instances physically close together within a single Availability Zone to achieve low-latency, high-throughput network performance.
2
Verify cluster placement group constraints.
A cluster placement group cannot span multiple Availability Zones.
Placing instances across multiple Availability Zones introduces inter-AZ network latency, which violates the cluster placement group's physical proximity design.
3
Diagnose the health check failure.
The application runs on port 9095, but the target group is using a mismatched port (e.g., port 80).
If the health check port does not match the application port, the NLB will mark the instances as unhealthy.
4
Determine the resolution steps.
Launch instances in a single-AZ cluster placement group and update the target group health check port to 9095.
This combination achieves the latency requirement and ensures the NLB correctly monitors the application's status.

Key Concept

Selecting the correct EC2 placement group for low-latency requirements and configuring matching health check ports in Elastic Load Balancing.
Question 987Question

A media company is designing a globally distributed video-on-demand (VOD) streaming application. The application delivers HTTP Live Streaming (HLS) video files, which consist of static media segments stored in an Amazon S3 bucket. The company requires a highly resilient architecture that minimizes delivery latency for global viewers and automatically handles S3 origin failures. The solution must minimize operational overhead.

Which TWO actions should a solutions architect take to meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create an Amazon CloudFront origin group that contains the primary S3 bucket as the primary origin and a replicated secondary S3 bucket in another AWS Region as the failover origin.; Update the default cache behavior of the CloudFront distribution to target the origin group instead of individual S3 origins.

Answer

Create an Amazon CloudFront origin group containing the primary S3 bucket and a replicated secondary S3 bucket as the failover origin, and configure the cache behavior to target this origin group.
To design a resilient edge caching architecture with minimal latency and low operational overhead, Amazon CloudFront must be used. By creating an origin group containing the primary S3 bucket and a secondary S3 bucket, CloudFront natively manages failover if the primary origin is unavailable. The cache behavior must target the origin group to direct incoming viewer requests to the correct group.

Step-by-Step Solution

1
Analyze the requirements for global low-latency delivery and high origin availability of static assets.
Amazon CloudFront is identified as the optimal tool to cache static media segments at global edge locations to minimize latency.
Direct S3 endpoint access or network routing services do not provide caching capabilities.
2
Establish origin redundancy by replicating content to a secondary S3 bucket in another AWS Region.
A secondary S3 bucket is prepared to act as a fallback origin.
A secondary origin is required to enable failover capability.
3
Group the primary and secondary origins within CloudFront.
A CloudFront origin group is created with the primary S3 bucket as the primary origin and the secondary S3 bucket as the failover origin.
This enables native origin failover within CloudFront without adding custom application logic.
4
Configure the distribution's cache behavior to target the origin group.
The default cache behavior is updated to point to the origin group.
CloudFront cache behaviors must be explicitly pointed to the origin group to leverage failover routing.

Key Concept

CloudFront Origin Groups and Origin Failover
Question 988Question

An analytics workload runs on Amazon EC2 instances in private subnets. The instances download 30 TB30\text{ TB} of data monthly from Amazon S3 and write 15 TB15\text{ TB} of data monthly to an Amazon DynamoDB table in the same Region. Currently, all outbound traffic from the private subnets routes through NAT Gateways, leading to high data processing charges. Which two actions should a solutions architect recommend to reduce network data transfer costs? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create a Gateway VPC endpoint for Amazon S3 and associate it with the private subnet route tables.; Create a Gateway VPC endpoint for Amazon DynamoDB and associate it with the private subnet route tables.

Answer

Create a Gateway VPC endpoint for Amazon S3 and a Gateway VPC endpoint for Amazon DynamoDB, and associate both with the private subnet route tables.
Creating Gateway VPC endpoints for both Amazon S3 and Amazon DynamoDB and associating them with the private subnet route tables is the most cost-effective solution. Gateway VPC endpoints do not charge for data transfer or hourly usage, allowing the EC2 instances to bypass the NAT Gateways entirely for S3 and DynamoDB traffic and avoiding data processing charges.

Step-by-Step Solution

1
Analyze the current traffic flow and identify data transfer cost contributors.
The EC2 instances transfer 45 TB45\text{ TB} of data monthly (30 TB30\text{ TB} to S3 and 15 TB15\text{ TB} to DynamoDB) through NAT Gateways, incurring significant data processing charges.
Identifying the destinations and volumes allows choosing the most cost-effective routing mechanism.
2
Compare VPC Endpoint options for S3 and DynamoDB.
Both S3 and DynamoDB support Gateway VPC endpoints, which are free of charge, whereas Interface VPC endpoints charge hourly and per-GB processing fees.
Using Gateway VPC endpoints eliminates data transfer and hourly fees for traffic destined to S3 and DynamoDB.
3
Configure the routing topology.
Create Gateway VPC endpoints for S3 and DynamoDB and associate them with the private subnet route tables.
This updates the route tables so that traffic destined for S3 and DynamoDB is routed directly to the AWS services instead of passing through the NAT Gateway.

Key Concept

Gateway VPC Endpoints for S3 and DynamoDB allow cost-free private routing of traffic, avoiding NAT Gateway data processing charges.
Question 989Question

A software-as-a-service (SaaS) telemetry company is designing a metadata catalog using Amazon DynamoDB. The application has a predictable, continuous baseline workload of 4,0004,000 write requests per second (each 1 KB1\text{ KB} in size) and 8,0008,000 eventually consistent read requests per second (each 1 KB1\text{ KB} in size) running 24/724/7. Twice a week, during scheduled device reporting intervals, write requests surge suddenly to 40,00040,000 writes/second for a duration of 2 hours2\text{ hours}. The database storage grows by 1.5 TB1.5\text{ TB} per month, but 80%80\% of this data is historical telemetry older than 30 days30\text{ days} that is only accessed once a year for compliance auditing. To design the most cost-effective database architecture that prevents throttling during write spikes and minimizes storage costs, which TWO actions should the Solutions Architect take? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure the DynamoDB table in Provisioned Capacity Mode, purchase DynamoDB Reserved Capacity to cover the baseline read and write throughput, and use an Amazon SQS queue to buffer incoming telemetry writes to absorb the 2-hour2\text{-hour} spikes.; Enable DynamoDB Time to Live (TTL) to automatically delete records older than 30 days30\text{ days}, and configure DynamoDB Streams with an AWS Lambda function to archive the deleted items to Amazon S3 Glacier Flexible Retrieval.

Answer

The most cost-effective configuration uses DynamoDB Provisioned Capacity Mode with Reserved Capacity combined with an Amazon SQS queue to buffer the write spikes, alongside DynamoDB Time to Live (TTL) to expire data after 30 days30\text{ days} and archive it to Amazon S3 Glacier Flexible Retrieval via DynamoDB Streams and AWS Lambda.
The most cost-effective way to handle a high, stable baseline workload (4,0004,000 writes/second and 8,0008,000 reads/second) is using Provisioned Capacity Mode combined with Reserved Capacity, which offers significant discounts compared to standard provisioned rates. Since the write spikes are very high (40,00040,000 writes/second) but temporary and predictable, buffering the writes using an Amazon SQS queue allows the table to remain in Provisioned Capacity Mode without over-provisioning or paying for expensive On-Demand capacity. To minimize storage costs for the data older than 30 days30\text{ days} (which represents 80%80\% of the storage and is rarely accessed), enabling DynamoDB Time to Live (TTL) removes the data from DynamoDB automatically, and DynamoDB Streams can be used with AWS Lambda to archive the deleted items to Amazon S3 Glacier Flexible Retrieval for low-cost compliance storage.

Step-by-Step Solution

1
Analyze the baseline and peak write/read throughput patterns.
Identify a high, constant baseline of 4,0004,000 writes/second and 8,0008,000 reads/second, alongside short-lived write spikes of 40,00040,000 writes/second.
Understanding the ratio of steady-state to peak workloads dictates the optimal capacity mode selection.
2
Evaluate the cost options for baseline throughput capacity.
Determine that Provisioned Capacity Mode with Reserved Capacity is the most cost-effective option for the constant 24/724/7 baseline workload.
Reserved Capacity offers substantial discounts for predictable baseline resource usage compared to On-Demand capacity.
3
Formulate a strategy to absorb the write spikes without over-provisioning.
Introduce an Amazon SQS queue to buffer incoming write requests during the 2-hour2\text{-hour} spikes, allowing the database to ingest them at a steady rate.
This prevents write throttling and avoids provisioning 40,00040,000 write capacity units (or using expensive On-Demand pricing) for transient spikes.
4
Address the storage growth and archival requirements.
Enable DynamoDB TTL to remove records older than 30 days30\text{ days} and use DynamoDB Streams with AWS Lambda to archive them to Amazon S3 Glacier Flexible Retrieval.
This reduces the active database size by 80%80\%, keeping storage costs low in DynamoDB Standard while retaining historical data in a highly cost-efficient archival tier.

Key Concept

Cost-optimized database capacity planning using Provisioned Capacity with Reserved Capacity, write buffering (SQS), and lifecycle archival (TTL + S3 Glacier).
Question 990Question

A company hosts a containerized application on AWS. The application consists of a backend transaction processing service hosted on Amazon ECS on AWS Fargate that runs continuously, requiring a steady-state baseline of 8 tasks and scaling up to 20 tasks during peak hours. Additionally, the company runs a daily batch processing job on Amazon EC2 instances to generate reports; this job is fault-tolerant and can be interrupted without affecting the system. The database tier uses an Amazon RDS for PostgreSQL instance that runs continuously 24/7. Which two options should a solutions architect recommend to provide 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 AWS Fargate tasks.; Run the daily batch processing job on Amazon EC2 Spot Instances.

Answer

Purchasing a Compute Savings Plan to cover the baseline Fargate tasks and running the daily batch processing job on Amazon EC2 Spot Instances provides the most cost-effective compute architecture.
Purchasing a Compute Savings Plan to cover the baseline Fargate tasks is correct because Compute Savings Plans apply directly to AWS Fargate and provide substantial discounts for steady-state usage. Running the daily batch processing job on Amazon EC2 Spot Instances is correct because the job is fault-tolerant and can tolerate interruptions, allowing the company to take advantage of the steep discounts offered by Spot Instances.

Step-by-Step Solution

1
Analyze the workload characteristics of each tier in the architecture.
The Fargate-hosted backend service runs continuously (steady baseline of 8 tasks, scaling up to 20). The EC2 batch job is periodic, fault-tolerant, and interruptible. The RDS database runs continuously.
Identifying the lifecycle and tolerance of each workload is required to choose the correct billing model.
2
Determine the optimal purchasing model for the Fargate baseline tasks.
Compute Savings Plans apply to AWS Fargate and cover continuous baseline usage with significant discounts.
Baseline workloads running 24/7 are best covered by Savings Plans to reduce On-Demand costs.
3
Determine the optimal purchasing model for the periodic, interruptible batch processing job.
Spot Instances are ideal for fault-tolerant, flexible workloads because they offer up to 90% savings over On-Demand rates.
Since the batch job can be interrupted, it does not require On-Demand or committed capacity models like Savings Plans.

Key Concept

Selecting cost-optimized AWS compute models (Savings Plans vs Spot Instances) based on workload stability and fault tolerance.
Estimated Time:1m 30s
Question 991Question

A financial technology company is building a serverless transaction processing system on AWS. The system must ingest ledger transaction entries, process them in the exact order they are received to ensure ledger integrity, and store the results in a database. The volume of transactions is highly unpredictable, scaling from zero to tens of thousands of requests in seconds. Additionally, the system must run a nightly ledger reconciliation job that aggregates all transactions from the past 24 hours, which typically takes 45 minutes to execute.

To minimize idle costs and maximize operational efficiency, which two architectural components should a solutions architect select? (Select two.)

Select all that apply

Show answer & explanation

Answer: Deploy an Amazon SQS FIFO queue to receive the transaction entries, and configure an AWS Lambda function to process the queue.; Use AWS Batch with an AWS Fargate Spot compute environment to run the nightly reconciliation job.

Answer

The solutions architect should deploy an Amazon SQS FIFO queue to ingest the transaction entries with an AWS Lambda function for ordered processing, and use AWS Batch with an AWS Fargate Spot compute environment to run the nightly reconciliation job.
Deploying an Amazon SQS FIFO queue with AWS Lambda ensures both strict ordering of transaction ledger events and serverless auto-scaling from zero to handle unpredictable spikes cost-effectively. Running the 45-minute nightly job on AWS Batch with AWS Fargate Spot is the most cost-efficient method because it bypasses Lambda's 15-minute limit and runs on discounted spare capacity.

Step-by-Step Solution

1
Analyze ordering and scaling requirements for transaction ingestion.
Ledger integrity requires strict ordering, which dictates using Amazon SQS FIFO over SQS Standard. High variability of traffic scaling from zero requires a serverless consumer like AWS Lambda.
This establishes the cost-effective and scale-to-zero processing pipeline for the transactions.
2
Analyze execution time requirements for the nightly reconciliation job.
The job takes 45 minutes, exceeding AWS Lambda's 15-minute execution limit.
This rules out AWS Lambda for the batch reconciliation workload.
3
Select a cost-efficient serverless container execution method for long-running batch jobs.
AWS Batch managing a containerized task on AWS Fargate Spot is the most cost-effective solution.
Fargate Spot utilizes spare capacity at a highly discounted rate, and AWS Batch automatically handles scheduling and scaling without persistent compute management overhead.

Key Concept

Leveraging SQS FIFO for ordered transaction processing and AWS Batch with Fargate Spot for long-running, cost-efficient batch processing.
Question 992Question

A company is deploying a machine learning inference application on a fleet of Amazon EC2 instances distributed across three Availability Zones. The instances require concurrent read-write access to a shared repository of model weights and input datasets. The storage solution must be POSIX-compliant, support sub-millisecond read latencies, and scale dynamically to handle peak throughput of up to 6 GB/s6\text{ GB/s}.

Which combination of storage solutions and configurations will meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create an Amazon EFS file system configured with Elastic throughput mode.; Create an Amazon FSx for NetApp ONTAP file system in a Multi-AZ deployment.

Answer

The correct configurations are creating an Amazon EFS file system configured with Elastic throughput mode and deploying an Amazon FSx for NetApp ONTAP file system in a Multi-AZ configuration.
The correct options are the Amazon EFS file system with Elastic throughput mode and the Amazon FSx for NetApp ONTAP file system in a Multi-AZ deployment. Amazon EFS is a fully managed, shared POSIX-compliant file system that spans multiple Availability Zones. When configured with Elastic throughput mode, EFS automatically scales to meet high-throughput workloads (up to 10 GB/s10\text{ GB/s} read throughput) without manual provisioning, making it ideal for unpredictable workloads. Amazon FSx for NetApp ONTAP is another fully managed, shared storage service that supports POSIX-compliant protocols (NFS and SMB), spans multiple Availability Zones, and offers high-performance throughput of several gigabytes per second with sub-millisecond latencies using NVMe caches.

Step-by-Step Solution

1
Analyze the requirements for shared access, availability, and network reachability.
The application runs on EC2 instances across three Availability Zones and requires a shared, POSIX-compliant file system. This rules out standard single-instance EBS volumes and non-POSIX storage solutions.
This establishes that a multi-AZ shared file storage service like Amazon EFS or Amazon FSx is required to allow concurrent read-write access.
2
Evaluate Amazon EBS Multi-Attach capabilities for multi-AZ applications.
EBS volumes (both gp3 and io2) are single-AZ resources. Even with Multi-Attach enabled on io2, they cannot span across different Availability Zones.
This eliminates the EBS-based options because they cannot meet the multi-AZ network requirement.
3
Evaluate the throughput and availability requirements against Amazon EFS and Amazon FSx configurations.
Amazon EFS with Elastic throughput mode provides automatically scaling throughput up to 10 GB/s10\text{ GB/s} across multiple Availability Zones. Amazon FSx for NetApp ONTAP Multi-AZ provides high-throughput shared storage that spans multiple Availability Zones. EFS One Zone is eliminated because it does not support multi-AZ availability.
This identifies the two correct, scalable, and high-performing storage configurations.

Key Concept

High-performing shared storage solutions spanning multiple Availability Zones
Question 993Question

A game development company is designing the infrastructure for a new multiplayer mobile game on AWS. The application has the following requirements:

* A game API service that experiences highly variable, spiky traffic throughout the day.
* A matchmaking service that must run continuously 2424 hours a day, 77 days a week on Amazon EC2.
* A nightly batch processing job that processes game telemetry, is non-time-critical, can tolerate interruptions, and produces temporary output files that are deleted within 4848 hours.
* A relational database tier running on Amazon RDS for MySQL.

Which combination of hosting configurations and purchasing strategies is the most cost-effective for this workload?

Show answer & explanation

Answer: Host the game API on AWS Lambda, purchase a Compute Savings Plan for the matchmaking EC2 instances, run the batch processing job on EC2 Spot Instances storing temporary files in Amazon S3 Standard, and purchase Reserved Instances for the Amazon RDS database.

Answer

Host the game API on AWS Lambda, purchase a Compute Savings Plan for the matchmaking EC2 instances, run the batch processing job on EC2 Spot Instances storing temporary files in Amazon S3 Standard, and purchase Reserved Instances for the Amazon RDS database.
Hosting the spiky game API on AWS Lambda ensures payment is only for actual requests. The continuously running matchmaking EC2 instances are discounted using a Compute Savings Plan. The interruptible nightly batch job runs on low-cost Spot Instances, and storing its short-lived files in Amazon S3 Standard avoids the 3030-day minimum billing penalty of S3 Standard-IA. Finally, purchasing Reserved Instances for the Amazon RDS database reduces its continuous running costs.

Step-by-Step Solution

1
Analyze the workload characteristics of each application tier.
Identify the game API as spiky/variable, the matchmaking service as continuous 2424 hours a day, 77 days a week, the batch job as interruptible and short-lived (4848 hours), and the database as continuous relational (RDS).
Matching workload patterns to the correct compute and storage options is the foundation of cost optimization.
2
Select the most cost-effective hosting and purchasing model for each tier.
Assign AWS Lambda to the spiky API, EC2 with a Compute Savings Plan to the continuous matchmaking service, EC2 Spot Instances with Amazon S3 Standard to the batch job, and RDS Reserved Instances to the database.
Compute Savings Plans cover EC2 but not RDS. Spot Instances are perfect for fault-tolerant batch workloads. S3 Standard is cheaper for short-term files (less than 3030 days) than S3 Standard-IA due to minimum storage duration charges.

Key Concept

Workload-based compute selection, Savings Plans scope, Spot Instances, and S3 storage tier cost structures.
Question 994Question

A company's finance team wants to establish a proactive cost management strategy for its AWS multi-account environment. The strategy must alert the operations team before the monthly AWS spend exceeds the budget, and also identify sudden, irregular cost spikes across services without manual intervention. Which combination of actions should a Solutions Architect recommend to meet these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure AWS Cost Anomaly Detection to monitor AWS services, using machine learning models to detect unusual spend spikes, and send alerts through Amazon Simple Notification Service (Amazon SNS).; Create an AWS Budget that monitors monthly costs and set up alert thresholds based on forecasted costs to notify the team before the budget is exceeded.

Answer

The correct actions are configuring AWS Cost Anomaly Detection to monitor services using machine learning models for anomaly alerts, and creating an AWS Budget with alert thresholds based on forecasted costs.
The correct actions are implementing AWS Cost Anomaly Detection to dynamically identify unexpected spikes using machine learning models, and creating AWS Budgets with alerts based on forecasted spend to proactively notify the operations team prior to exceeding thresholds.

Step-by-Step Solution

1
Identify the requirement for detecting sudden, irregular cost spikes without manual intervention.
Determine that AWS Cost Anomaly Detection uses machine learning models to dynamically establish baselines and identify anomalies.
Cost anomalies are unexpected and variable, making static threshold-based alerts ineffective compared to machine learning.
2
Identify the requirement for alerting the team before the monthly budget is exceeded.
Determine that AWS Budgets supports alert thresholds based on forecasted monthly costs.
Evaluating forecasted costs allows the operations team to receive notifications proactively before the limit is reached, unlike actual spend alerts.

Key Concept

Distinguishing between proactive planning tools (AWS Budgets), reactive/retrospective analysis tools (AWS Cost Explorer), and automated monitoring tools (AWS Cost Anomaly Detection).
Estimated Time:1m 30s
Question 995Question

A digital learning platform distributes high-definition video-on-demand training courses and accompanying presentation slide decks (PDF files) to a global user base. The media content and slides are stored in an Amazon S3 bucket. The videos are streamed using HTTP Live Streaming (HLS). The slide decks are updated daily, and students must have immediate access to the updated versions. The company wants to optimize content delivery latency, reduce load on the S3 origin, and minimize data transfer costs. Which two actions should a solutions architect recommend to meet these requirements?

Select all that apply

Show answer & explanation

Answer: Use unique versioned filenames for the slide decks and update the application links when slides are modified.; Configure an Amazon CloudFront distribution to serve both the HLS video segments and the slide decks from the Amazon S3 origin.

Answer

Using unique versioned filenames for the slides and configuring Amazon CloudFront to serve both the video segments and the slides from the Amazon S3 origin.
Caching static media and video streams using Amazon CloudFront reduces global delivery latency and limits requests hitting S3. When files are updated frequently, using unique versioned filenames is the industry best practice; it forces clients to request the new path immediately, ensuring content consistency without sacrificing caching benefits or incurring invalidation fees.

Step-by-Step Solution

1
Integrate Amazon CloudFront with the S3 origin.
Assets (HLS segments and slides) are cached at regional edge caches, reducing retrieval latency and origin load.
CloudFront speeds up delivery and reduces S3 data egress costs by caching content closer to the users.
2
Implement a file versioning mechanism for the daily slide updates.
Each slide deck update uses a new filename (e.g. slide_v2.pdf), forcing the CDN to fetch the new version immediately upon update.
This guarantees that users get the newest slides instantly while allowing the CDN to maintain long cache lifetimes, avoiding origin overload.

Key Concept

Caching static and dynamic streaming media using Amazon CloudFront while managing cache validation via versioning rather than disabling cache lifetimes.
Question 996Question

A logistics enterprise is designing a real-time fleet tracking system that must ingest and process location updates from over 5000050{}000 active delivery vehicles. The system expects a write workload of up to 2000020{}000 writes per second. Customer dashboards require real-time tracking updates with read latency under 10 ms10\text{ ms}. The database must scale automatically to handle peak traffic during holiday seasons without performance degradation or partition bottlenecks. Which database architecture provides the highest performance and scalability for this application?

Show answer & explanation

Answer: Amazon DynamoDB with `delivery_id` as the partition key and `status_timestamp` as the sort key, utilizing DynamoDB Auto Scaling to manage capacity.

Answer

Use Amazon DynamoDB with `delivery_id` as the partition key and `status_timestamp` as the sort key, utilizing DynamoDB Auto Scaling to manage capacity.
The correct design uses Amazon DynamoDB with a high-cardinality partition key (`delivery_id`) and a sort key (`status_timestamp`). This distributes writes evenly across multiple physical partitions, preventing write throughput bottlenecks. DynamoDB Auto Scaling dynamically adjusts provisioned throughput based on traffic, satisfying both high-throughput write and low-latency read requirements.

Step-by-Step Solution

1
Analyze the workload requirements for read/write scaling.
The workload requires scaling to 2000020{}000 writes per second with sub-10 ms10\text{ ms} read latency, which exceeds typical single-instance relational database limits and points to a NoSQL database.
Amazon DynamoDB handles horizontal scaling and low-latency reads/writes natively.
2
Design the partition key to avoid hot partition bottlenecks.
Select a high-cardinality attribute like `delivery_id` as the partition key to distribute write operations evenly across DynamoDB partitions.
Low-cardinality keys like date strings concentrate writes on a single partition, leading to throttling.
3
Select the appropriate scaling mechanism for seasonal traffic.
Enable DynamoDB Auto Scaling (or On-Demand mode) to adjust capacity dynamically to workload demand.
Auto Scaling prevents throttling during traffic spikes and optimizes cost during low-use periods.

Key Concept

Amazon DynamoDB partition key design and scaling for high-throughput write workloads.
Estimated Time:2m 0s
Question 997Question

A hospitality company manages its central reservation system using an Amazon Aurora MySQL-Compatible Edition database cluster in the us-east-1 Region. To ensure business continuity, the company requires a disaster recovery (DR) solution in the us-west-2 Region. The DR architecture must support a Recovery Point Objective (RPO) of less than 11 second and a Recovery Time Objective (RTO) of less than 11 minute. Which database configuration meets these requirements?

Show answer & explanation

Answer: Create an Amazon Aurora Global Database with the primary cluster in the us-east-1 Region and a secondary cluster in the us-west-2 Region, and promote the secondary cluster in the event of a disaster.

Answer

Create an Amazon Aurora Global Database with the primary cluster in the us-east-1 Region and a secondary cluster in the us-west-2 Region, and promote the secondary cluster in the event of a disaster.
The correct option is to create an Amazon Aurora Global Database. Aurora Global Database uses storage-based, asynchronous replication that achieves a lag of less than 11 second. If the primary region experiences an outage, the secondary cluster in the target region can be promoted to a standalone primary cluster with read and write capabilities in less than 11 minute, satisfying both the RPO and RTO requirements.

Step-by-Step Solution

1
Analyze recovery objectives and replication constraints.
The requirements dictate a Recovery Point Objective (RPO) of less than 11 second (near-real-time replication) and a Recovery Time Objective (RTO) of less than 11 minute (extremely fast failover or promotion).
Understanding the RTO and RPO limits helps filter out backup-and-restore or slow replication methods.
2
Evaluate cross-region replication technologies for Amazon Aurora.
Amazon Aurora Global Database utilizes dedicated infrastructure at the storage layer to replicate data to secondary regions with typical latencies of less than 11 second, and supporting promotion of the secondary cluster within 11 minute.
Selecting the native high-availability cross-region feature that directly maps to sub-second RPO and sub-minute RTO.
3
Eliminate incorrect options that fail the target recovery metrics or are architecturally invalid.
Route 53 latency routing cannot handle write synchronization; RDS MySQL read replicas do not support automated cross-region failover; hourly snapshots fail both the 11-second RPO and 11-minute RTO constraints.
Ensuring the selected solution is the only one that meets all constraints while avoiding common operational misconceptions.

Key Concept

Cross-Region Disaster Recovery with Amazon Aurora Global Database
Question 998Question

A solutions architect is designing a cost-optimized database solution for two workloads. The first workload is a non-relational database with highly unpredictable, spiky traffic. The second workload is a relational database that requires high availability and automatic failover in the event of an infrastructure failure. Which database configurations will meet these requirements in the most cost-effective manner? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Configure the non-relational database using Amazon DynamoDB in on-demand capacity mode; Configure the relational database using Amazon RDS with a Multi-AZ deployment

Answer

The correct configurations are to use Amazon DynamoDB in on-demand capacity mode for the spiky non-relational database, and Amazon RDS in a Multi-AZ deployment for the relational database requiring high availability.
The correct database configurations use Amazon DynamoDB in on-demand capacity mode to cost-effectively handle highly unpredictable, spiky traffic, and Amazon RDS in a Multi-AZ deployment to provide automatic failover and high availability.

Step-by-Step Solution

1
Analyze the capacity requirements for the non-relational database workload.
The workload has spiky and highly unpredictable traffic patterns.
Unpredictable workloads are best suited for on-demand capacity modes to avoid over-provisioning and minimize costs.
2
Analyze the resilience and availability requirements for the relational database.
The relational database requires high availability and automatic failover capabilities.
Amazon RDS Multi-AZ deployments provide automatic failover and synchronous replication across Availability Zones.

Key Concept

Selecting the cost-optimal capacity mode for unpredictable workloads (DynamoDB on-demand) and configuring appropriate high-availability options (RDS Multi-AZ) for relational databases.
Question 999Question

A software development company uses Amazon S3 to store temporary build artifacts generated by their continuous integration (CI) pipeline. The average size of these artifacts is 450 KB450\text{ KB}. These files are frequently accessed during the first 10 days for verification and testing. The company wants to minimize storage costs and must permanently delete the artifacts exactly 25 days after creation. Which of the following lifecycle configurations is the most cost-effective solution?

Show answer & explanation

Answer: Store the artifacts in Amazon S3 Standard, and configure an S3 Lifecycle rule to expire the objects after 25 days.

Answer

Store the artifacts in Amazon S3 Standard, and configure an S3 Lifecycle rule to expire the objects after 25 days.
Storing the artifacts in Amazon S3 Standard for the entire 25 days and then expiring them is the most cost-effective option. Because the artifacts are permanently deleted at day 25, transitioning them to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) at day 10 would result in only 15 days of storage in that tier. Since S3 Standard-IA has a 30-day minimum storage charge, transitioning would result in being billed for 30 days of storage, making it more expensive than keeping the objects in S3 Standard.

Step-by-Step Solution

1
Analyze the access pattern and retention requirements.
The files are accessed frequently for the first 10 days and must be deleted at day 25. The total lifecycle of the objects is 25 days.
Understanding the timeline is necessary to evaluate the minimum duration requirements of different S3 storage classes.
2
Evaluate S3 Standard-IA and S3 Glacier minimum storage duration constraints.
S3 Standard-IA has a minimum storage duration of 30 days. S3 Glacier Flexible Retrieval has a minimum storage duration of 90 days.
Transitioning objects that will be deleted before these minimum durations will incur prorated charges for the remaining days.
3
Compare the cost of keeping the artifacts in S3 Standard versus transitioning them.
Storing in S3 Standard for 25 days is cheaper than transitioning them to S3 Standard-IA at day 10 (which charges for 30 days of IA storage plus transition fees) or transitioning to Glacier (which charges for 90 days).
This determines the absolute most cost-effective storage strategy.

Key Concept

Understanding minimum storage duration policies for Amazon S3 tiers (Standard-IA and Glacier) when designing cost-optimized lifecycle rules.
Estimated Time:1m 30s
Question 1000Question

A software company runs multiple development workloads on AWS, with all resources tagged by Environment. The company's platform team wants to detect sudden, unexpected spend increases in these environments as quickly as possible. The solution must automatically identify anomalies based on historical spending patterns without requiring the team to define static thresholds, and it must send alerts to an Amazon SNS topic for immediate investigation. Which solution will meet these requirements?

Show answer & explanation

Answer: Configure AWS Cost Anomaly Detection with an Environment tag-based monitor, and create an alert subscription to send anomaly notifications to the Amazon SNS topic.

Answer

Configure AWS Cost Anomaly Detection with an Environment tag-based monitor, and create an alert subscription to send anomaly notifications to the Amazon SNS topic.
AWS Cost Anomaly Detection is a free service that uses machine learning to continuously monitor cost and usage to detect unusual spend. By using a cost allocation tag-based monitor (such as the Environment tag), it can isolate anomalies to specific environments and dynamically adjust thresholds based on historical patterns. Integrating it with Amazon Simple Notification Service (SNS) allows immediate alerting for near-real-time incident response.

Step-by-Step Solution

1
Identify the primary requirement: detecting sudden, unexpected cost spikes without setting static thresholds.
Dynamic anomaly detection is needed, which rules out static tools like AWS Budgets.
AWS Budgets requires manually defining specific cost thresholds or forecasts rather than dynamically determining anomalies.
2
Evaluate the requirement for near-real-time alerting.
Batch report mechanisms are eliminated.
AWS Cost Explorer reports are retrospective and run on schedules, which introduces delays and fails to prevent active runaway costs.
3
Select the service designed for tag-based, machine learning-driven anomaly detection and alerting.
AWS Cost Anomaly Detection with a tag monitor and Amazon SNS subscription meets all criteria.
It uses machine learning to automatically establish baseline spending, detects anomalies for specific tags, and alerts via SNS immediately.

Key Concept

Using AWS Cost Anomaly Detection for tag-based, real-time alert notifications powered by machine learning, rather than using retrospective reporting tools or static budgets.
Estimated Time:1m 30s
PreviousPage 50 / 74Next
All practice questions — AWS Certified Solutions Architect - Associate | Examkin