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1462 questions

Question 721Question

A company hosts a media portal on AWS that serves high-traffic static images and video clips. The static assets are stored in an Amazon S3 bucket in the us-east-1 Region. To ensure maximum availability, the company replicates these assets to another S3 bucket in the us-west-2 Region. The company wants to implement a solution that caches content globally to reduce latency for users. The architecture must automatically serve assets from the backup Region if the primary S3 bucket returns server errors (HTTP 500-class). Additionally, the portal must be protected against application-layer (Layer 7) HTTP flood attacks. The solution should minimize latency and operational overhead.

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

Select all that apply

Show answer & explanation

Answer: Create an Amazon CloudFront distribution. Configure a CloudFront origin group with the primary S3 bucket as the primary origin and the backup S3 bucket as the secondary origin, setting the failover criteria to include HTTP status codes 500, 502, 503, and 504.; Create an AWS WAF web ACL with a rate-based rule to limit requests from individual client IP addresses. Associate the web ACL with the CloudFront distribution.

Answer

To meet the requirements, the solutions architect must create an Amazon CloudFront distribution and configure an origin group with the primary S3 bucket as the primary origin and the backup S3 bucket as the secondary origin (failing over on 5xx status codes). Additionally, the solutions architect must create an AWS WAF web ACL with a rate-based rule and associate it with the CloudFront distribution.
Configuring Amazon CloudFront with an origin group enables automatic failover to a backup S3 bucket in a different Region when the primary bucket returns 5xx errors, ensuring continuous availability of static content at the edge. Integrating AWS WAF with CloudFront allows the solutions architect to apply rate-limiting rules at the edge, blocking Layer 7 HTTP flood attacks before they impact the origin buckets.

Step-by-Step Solution

1
Configure global content delivery and caching using Amazon CloudFront.
Static assets are cached at CloudFront edge locations worldwide, drastically reducing latency for global users.
This establishes the core caching mechanism required to optimize performance for static assets.
2
Configure a CloudFront Origin Group with the primary S3 bucket as primary and backup S3 bucket as secondary, enabling failover on 500, 502, 503, and 504 codes.
If the primary S3 bucket becomes unavailable or returns a 5xx error, CloudFront seamlessly fetches the asset from the secondary S3 bucket without exposing the error to the user.
This configuration provides automated, low-latency origin failover to ensure content resiliency.
3
Create an AWS WAF web ACL with a rate-based rule and associate it with the CloudFront distribution.
Individual client IPs exceeding the specified request rate threshold are blocked at the CloudFront edge.
AWS WAF is the correct service for protecting the application from Layer 7 HTTP flood attacks, mitigating threats before they reach S3.

Key Concept

Using Amazon CloudFront origin groups to achieve automated, high-availability origin failover for static assets combined with AWS WAF for edge security against Layer 7 HTTP flood attacks.
Question 722Question

A manufacturing corporation needs to establish a secure, encrypted network connection between its on-premises factory control center and three VPCs in the us-west-2 Region. The network must support a sustained transfer rate of at least 2 Gbps2\text{ Gbps} to replicate real-time telemetry data to AWS. Which TWO actions should a solutions architect take to design a high-performing and scalable network architecture? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create an AWS Transit Gateway and enable Equal-Cost Multi-Path (ECMP) routing.; Establish multiple AWS Site-to-Site VPN tunnels with dynamic routing using Border Gateway Protocol (BGP) terminated on the Transit Gateway.

Answer

Create an AWS Transit Gateway and enable Equal-Cost Multi-Path (ECMP) routing, and establish multiple AWS Site-to-Site VPN tunnels with dynamic routing using Border Gateway Protocol (BGP) terminated on the Transit Gateway.
To achieve a secure connection with a throughput greater than 1.25 Gbps1.25\text{ Gbps} using AWS Site-to-Site VPN, you must deploy an AWS Transit Gateway with Equal-Cost Multi-Path (ECMP) routing enabled. Dynamic routing with Border Gateway Protocol (BGP) must be configured on the VPN tunnels terminated on the Transit Gateway. This allows the network to load balance traffic across multiple active tunnels, aggregating bandwidth to meet the 2 Gbps2\text{ Gbps} requirement.

Step-by-Step Solution

1
Analyze the network throughput requirements and identifying limitations of standard VPN connections.
A single AWS VPN tunnel caps at 1.25 Gbps1.25\text{ Gbps}, so a multi-tunnel approach with load balancing is necessary to reach 2 Gbps2\text{ Gbps}.
Understanding physical limits helps design appropriate scaling architectures.
2
Enable Equal-Cost Multi-Path (ECMP) routing on AWS Transit Gateway.
AWS Transit Gateway will distribute traffic evenly across multiple active paths that have equal routing costs.
ECMP is required to route traffic across multiple VPN tunnels simultaneously.
3
Configure dynamic routing using Border Gateway Protocol (BGP) for the VPN connections.
BGP dynamically advertises routes across multiple tunnels, allowing ECMP to balance the load.
Dynamic routing enables path state propagation and is required for ECMP on AWS Transit Gateway VPN attachments.

Key Concept

Scaling VPN bandwidth using AWS Transit Gateway and ECMP routing.
Question 723Question

An engineering firm is designing a high-performance compute (HPC) system on AWS to run tightly-coupled computational fluid dynamics (CFD) simulations. The simulations utilize Message Passing Interface (MPI) for inter-node communication and require sub-millisecond network latency and high throughput. The control interface of the worker nodes runs on a custom service bound to TCP port 8443. An Application Load Balancer (ALB) will distribute control traffic to these worker nodes. The compute cluster must dynamically scale based on demand. Which architecture satisfies these requirements while maximizing performance and ensuring high availability of the control traffic?

Show answer & explanation

Answer: Deploy the worker nodes on Amazon EC2 instances within a cluster placement group, enabling Elastic Fabric Adapter (EFA) on the network interfaces. Register the instances to an ALB target group with the health check port explicitly configured to 8443.

Answer

Deploy the worker nodes on Amazon EC2 instances within a cluster placement group, enabling Elastic Fabric Adapter (EFA) on the network interfaces. Register the instances to an ALB target group with the health check port explicitly configured to 8443.
The correct architecture uses a cluster placement group to achieve low-latency node-to-node communication, which is necessary for tightly-coupled MPI applications. Elastic Fabric Adapter (EFA) is enabled to provide OS-bypass for MPI, which significantly improves latency and throughput. Finally, the Application Load Balancer target group's health check port must be explicitly configured to 8443 because the control interface is bound to that custom TCP port. This prevents the health checks from failing, which would otherwise happen if left on the default port 80.

Step-by-Step Solution

1
Analyze the networking latency requirements of the HPC workload.
Tightly-coupled fluid dynamics simulations using MPI require sub-millisecond node-to-node latency.
This requirement determines the selection of placement groups and network adapters.
2
Choose the optimal EC2 placement group and network adapter configuration.
Select a cluster placement group to group instances close together, and enable Elastic Fabric Adapter (EFA).
Cluster placement groups minimize physical network distance. EFA provides OS-bypass capabilities, bypassing the operating system kernel to accelerate MPI communications.
3
Evaluate the Application Load Balancer health check configuration for the custom application port.
Configure the target group's health check port to explicitly use 8443.
The control traffic on the worker nodes listens on port 8443. Leaving the health check at default would query port 80, causing health checks to fail and making the backend instances unavailable.

Key Concept

High-Performance Compute Clustering and Load Balancer Health Check Ports
Question 724Question

A financial technology firm runs a high-frequency risk assessment platform on AWS. The platform consists of the following components:

* A web portal and API gateway hosted on AWS Fargate that requires a continuous baseline capacity of 25 vCPUs25\text{ vCPUs} and 50 GB50\text{ GB} of RAM 24/724/7 to handle dashboard queries.
* A backend distributed processing grid of Amazon EC2 instances that runs short-lived, containerized simulation jobs. These jobs are highly parallel, run for 22 to 33 hours at a time, write checkpoints to an external storage layer, and must complete within a flexible 1818-hour daily window.
* A database layer hosted on Amazon DynamoDB that stores user session tokens and transaction metadata. The database experiences sudden, unpredictable traffic spikes up to 10,000 requests per second10,000\text{ requests per second}, followed by several hours of idle state.

The firm wants to optimize its architecture to achieve the maximum possible cost savings for its compute resources. Which two options should the solutions architect recommend to satisfy these requirements at the lowest cost?

Select all that apply

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the baseline usage of the AWS Fargate tasks.; Provision the distributed grid of Amazon EC2 instances using Spot Instances.

Answer

The correct recommendations are to purchase a Compute Savings Plan to cover the baseline AWS Fargate API gateway usage, and to provision the EC2 distributed simulation grid using Spot Instances.
The correct configuration is to purchase a Compute Savings Plan to cover the steady-state, continuous AWS Fargate tasks (since Fargate is covered by Compute Savings Plans) and to use Spot Instances for the short-lived, checkpointed, and flexible EC2 simulation jobs (which can handle interruptions without losing progress).

Step-by-Step Solution

1
Analyze the web portal and API gateway compute requirements.
The API gateway has a continuous baseline capacity of 25 vCPUs25\text{ vCPUs} and 50 GB50\text{ GB} of RAM running 24/724/7. This constant workload is best optimized using a Compute Savings Plan, which applies to Fargate and offers significant discounts over On-Demand pricing.
Baseline, predictable compute workloads running continuously should be covered by a purchasing commitment tool like Savings Plans.
2
Analyze the backend simulation processing grid compute requirements.
The simulation jobs are short-lived (22 to 33 hours), stateless/checkpointed, and can tolerate interruptions within an 1818-hour daily processing window. This profile is ideal for Spot Instances, which provide up to 90%90\% savings compared to On-Demand pricing.
Fault-tolerant, stateless, and time-flexible workloads should be run on Spot Instances to minimize compute costs.
3
Analyze the database layer performance and load characteristics.
The DynamoDB database has highly spiky, unpredictable workloads (10,000 requests per second10,000\text{ requests per second} peak down to 00 for hours). Using On-Demand capacity mode avoids over-provisioning during idle hours, while Compute Savings Plans cannot be applied to DynamoDB.
Compute Savings Plans only apply to EC2, Fargate, and Lambda, not database services like DynamoDB, RDS, or Redshift.

Key Concept

Selecting the optimal AWS compute purchasing models (Compute Savings Plans, Spot Instances, On-Demand) and database capacity modes to minimize overall cloud architecture cost.
Question 725Question

A manufacturing facility has 20,00020,000 IoT sensors monitoring machine vibrations. Every 1010 seconds, each sensor generates a 5 KB5 \text{ KB} JSON payload containing health telemetry. A solutions architect needs to design a high-performing ingestion pipeline that buffers this data, converts the JSON format to Apache Parquet, and writes the transformed data to Amazon S3. The solution must handle spikes up to three times the average ingestion rate and minimize operational overhead. Which solution meets these requirements with the lowest operational overhead?

Show answer & explanation

Answer: Configure Amazon Data Firehose to receive the telemetry data directly, configure it to convert the format to Apache Parquet using an AWS Glue schema table, and specify an Amazon S3 bucket as the destination.

Answer

Configure Amazon Data Firehose to receive the telemetry data directly, configure it to convert the format to Apache Parquet using an AWS Glue schema table, and specify an Amazon S3 bucket as the destination.
The correct solution uses Amazon Data Firehose to directly ingest the data and convert the JSON payloads to Apache Parquet format using an AWS Glue schema table. Amazon Data Firehose is a fully managed, serverless stream delivery service that automatically scales to handle high-throughput workloads and spikes (up to 30 MB/s30 \text{ MB/s} in this scenario) without manual shard management. Its built-in format conversion capability eliminates the need to develop, scale, and maintain custom transformation code in AWS Lambda or Amazon ECS.

Step-by-Step Solution

1
Calculate the average and peak ingestion throughput.
The average ingestion rate is 20,000 sensors×5 KB/10 seconds=10 MB/s20,000 \text{ sensors} \times 5 \text{ KB} / 10 \text{ seconds} = 10 \text{ MB/s} (at 2,0002,000 records/second). The peak ingestion rate is 3×10 MB/s=30 MB/s3 \times 10 \text{ MB/s} = 30 \text{ MB/s} (at 6,0006,000 records/second).
Determining throughput requirements is necessary to evaluate service scaling limits.
2
Evaluate the scaling capacity of the ingestion services.
A provisioned Kinesis Data Stream with 10 shards can only ingest up to 10 MB/s10 \text{ MB/s} (1 MB/s per shard), which will fail during peak spikes. Amazon Data Firehose automatically scales up to the required 30 MB/s30 \text{ MB/s} without manual shard configuration.
The ingestion tier must scale dynamically to handle spike workloads without throwing errors.
3
Identify the solution with the lowest operational overhead that performs transformation.
Amazon Data Firehose natively integrates with AWS Glue to perform serverless JSON-to-Parquet conversion, outputting directly to Amazon S3 without requiring custom EC2, ECS, or Lambda compute infrastructure.
Minimizing operational overhead involves leveraging native, managed service features instead of writing custom batching and transformation logic.

Key Concept

Serverless high-throughput data ingestion, buffering, and format transformation using Amazon Data Firehose and AWS Glue.
Question 726Question

A media company is migrating its video processing pipeline to AWS. The architecture consists of the following components:

1. An orchestration and management web portal that runs continuously 24/7 on Amazon ECS Fargate, requiring a stable baseline of compute resources.
2. A batch video transcoding workload that runs containerized tasks on Amazon ECS Fargate. Individual transcoding jobs can run for several hours, are designed to be fault-tolerant, and can be retried if interrupted.
3. A metadata backend database running on an Amazon Aurora PostgreSQL DB cluster that must be available 24/7.

Which combination of configurations and purchasing strategies is the MOST cost-effective to meet these requirements?

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the baseline ECS Fargate orchestration service, run the batch video transcoding workload on Amazon ECS Fargate Spot instances, and purchase Amazon Aurora Reserved DB Instances for the database cluster.

Answer

Purchase a Compute Savings Plan to cover the baseline ECS Fargate orchestration service, run the batch video transcoding workload on Amazon ECS Fargate Spot instances, and purchase Amazon Aurora Reserved DB Instances for the database cluster.
The option recommending a Compute Savings Plan for the 24/7 orchestration service, ECS Fargate Spot for the batch video transcoding workload, and Amazon Aurora Reserved DB Instances for the database cluster is correct. It properly identifies that the predictable ECS Fargate orchestration service benefits from a Compute Savings Plan commitment, the interruptible batch processing tier is best suited for the low-cost Fargate Spot tier, and the 24/7 database cluster is optimized using DB-specific Reserved Instances.

Step-by-Step Solution

1
Analyze the compute characteristics of the 24/7 orchestration service.
Determine that since it runs continuously on Amazon ECS Fargate, it requires a committed compute purchasing model.
A Compute Savings Plan provides up to a 66% discount on ECS Fargate usage in exchange for a 1-year or 3-year commitment, offering the best cost savings for a stable compute baseline.
2
Evaluate the requirements and constraints of the batch video transcoding workload.
Select ECS Fargate Spot instances rather than On-Demand or AWS Lambda.
Transcoding jobs run for hours, ruling out AWS Lambda due to its 15-minute maximum timeout. Because the jobs are fault-tolerant and retryable, Fargate Spot provides discounts up to 70% compared to On-Demand with the trade-off of potential interruption.
3
Analyze the backend database workload.
Select Amazon Aurora Reserved DB Instances for the 24/7 Aurora PostgreSQL cluster.
Database instances that run continuously 24/7 are not covered by Compute Savings Plans, but purchasing Reserved DB Instances provides significant discounts for the database tier.

Key Concept

Matching different AWS workloads with the most cost-effective compute purchasing models (Compute Savings Plans, Fargate Spot, and Reserved DB Instances) based on execution duration, predictability, and fault tolerance.
Estimated Time:2m 30s
Question 727Question

A company hosts a database on a single Amazon EC2 instance. The database requires a 200200 GiB storage volume that must deliver a consistent performance of 10,00010,000 IOPS and 300300 MiB/s throughput. Which TWO actions should a solutions architect take to meet these performance requirements at the lowest cost?

Select all that apply

Show answer & explanation

Answer: Use an Amazon EBS General Purpose SSD (gp3) volume.; Provision the required 10,00010,000 IOPS and 300300 MiB/s throughput settings directly on the volume.

Answer

Use an Amazon EBS General Purpose SSD (gp3) volume and provision the required 10,00010,000 IOPS and 300300 MiB/s throughput settings directly on the volume.
The correct options are to use an Amazon EBS General Purpose SSD (gp3) volume and to provision the required 10,00010,000 IOPS and 300300 MiB/s throughput settings directly on the volume. Amazon EBS gp3 volumes allow customers to provision storage capacity, IOPS, and throughput independently. This is ideal for workloads like databases that need high IOPS and throughput but have a small data footprint, providing the lowest cost solution.

Step-by-Step Solution

1
Analyze the storage requirements.
The database needs 200200 GiB capacity, 10,00010,000 IOPS, and 300300 MiB/s throughput.
Understanding capacity and performance needs helps in identifying appropriate volume types.
2
Compare Amazon EBS volume types.
Amazon EBS gp3 volumes support independent configuration of capacity, IOPS, and throughput, whereas gp2 ties IOPS directly to capacity (33 IOPS per GiB).
Selecting gp3 allows meeting the high performance target without over-provisioning storage capacity, minimizing costs.
3
Select the correct configuration options.
Configure a gp3 volume with 200200 GiB size, and provision 10,00010,000 IOPS and 300300 MiB/s throughput directly on it.
This configuration satisfies all technical requirements at the lowest cost.

Key Concept

Amazon EBS gp3 volumes support independent scaling of storage capacity, IOPS, and throughput, allowing cost-effective tuning of workloads.
Question 728Question

A media company is launching a live interactive voting platform for a popular television tournament. During a 10-minute voting window, the platform must ingest a sudden burst of up to 100,000100,000 votes per second. Each vote payload consists of a unique `VoteID`, the `ContestantID` (representing one of 5 finalists), and a `Timestamp`. The platform requires microsecond-level latency to query real-time leaderboard statistics by `ContestantID` for display on the live broadcast. Which database architecture should a solutions architect recommend to support this scale and performance while preventing write throttling?

Show answer & explanation

Answer: Amazon DynamoDB in on-demand capacity mode, using a composite primary key consisting of a partition key of `ContestantID` appended with a random integer suffix and a sort key of `Timestamp`, with Amazon DynamoDB Accelerator (DAX) deployed to cache read queries.

Answer

Amazon DynamoDB in on-demand capacity mode, using a composite primary key consisting of a partition key of ContestantID appended with a random integer suffix and a sort key of Timestamp, with Amazon DynamoDB Accelerator (DAX) deployed to cache read queries.
The correct architecture uses Amazon DynamoDB in on-demand capacity mode to handle the sudden, massive voting spikes that auto scaling with provisioned capacity cannot adapt to fast enough. Because there are only 5 contestants, using ContestantID directly as the partition key would limit writes to only 5 partitions. A single DynamoDB partition supports a maximum of 1,0001,000 write capacity units (WCUs) per second, so 100,000100,000 writes per second would cause massive throttling. Appending a random integer suffix (e.g., ContestantID_1 through ContestantID_100) shards the partition key, distributing the writes across many physical partitions to handle the throughput. Amazon DynamoDB Accelerator (DAX) is natively integrated with DynamoDB to provide microsecond-level read latency for the leaderboard queries.

Step-by-Step Solution

1
Analyze the capacity requirement for a sudden 10-minute window spike.
Identify that provisioned capacity mode with auto scaling is insufficient because auto scaling reacts slowly and cannot scale up instantaneously to handle a spike from zero to 100,000100,000 requests per second. On-demand capacity mode is required.
On-demand mode instantly accommodates rapid traffic spikes without needing to pre-provision capacity.
2
Analyze the database partition key structure to avoid hot partitions.
Recognize that with only 5 contestants, using ContestantID directly as the partition key concentrates all writes onto 5 physical partitions. Because DynamoDB limits write throughput to 1,0001,000 write capacity units (WCUs) per second per physical partition, the total write limit would be capped at 5,0005,000 WCUs/sec, causing massive throttling at 100,000100,000 writes/sec. Suffixing the ContestantID with a random integer (e.g., ContestantID_1 to ContestantID_100) distributes the writes across 500 physical partitions.
Write sharding (partition key suffixing) is the standard technique to distribute write traffic for low-cardinality keys.
3
Select the caching solution to meet microsecond read latency.
Amazon DynamoDB Accelerator (DAX) is selected because it is a fully managed, highly available, in-memory cache for DynamoDB that delivers microsecond response times for read operations.
DAX integrates natively with DynamoDB, eliminating the need to write custom caching logic.
4
Evaluate and discard alternative database architectures.
Discard the Amazon RDS option because RDS read replicas are read-only and cannot accept write traffic, and discard the LSI option due to the 10 GB item collection limit.
Correct database write patterns and resource limits must be respected.

Key Concept

DynamoDB partition key sharding (write-sharding) and capacity mode selection for highly spiky, high-throughput database workloads.
Question 729Question

A healthcare provider is deploying a patient prescription tracking application across two AWS Regions: useast1us-east-1 (Primary) and uswest2us-west-2 (Secondary). The application backend uses an Amazon Aurora PostgreSQL DB cluster. The solutions architect must design a highly resilient architecture that meets a Recovery Time Objective (RTO) of under 55 minutes and a Recovery Point Objective (RPO) of under 11 minute for regional disaster recovery, while also providing high availability within the primary region (RTO of under 6060 seconds, RPO of 00) and scaling read traffic locally in uswest2us-west-2. Which two configurations should the solutions architect implement to meet these requirements? (Select two.)

Select all that apply

Show answer & explanation

Answer: Deploy the primary Amazon Aurora PostgreSQL DB cluster in useast1us-east-1 across multiple Availability Zones, ensuring at least one reader instance is running in a different Availability Zone than the writer instance.; Configure an Amazon Aurora Global Database with the primary cluster in useast1us-east-1 and a secondary cluster in uswest2us-west-2, provisioning at least one reader instance in the secondary cluster.

Answer

Deploying the primary Amazon Aurora PostgreSQL DB cluster in us-east-1 across multiple Availability Zones, ensuring at least one reader instance is running in a different Availability Zone than the writer instance, and configuring an Amazon Aurora Global Database with the primary cluster in us-east-1 and a secondary cluster in us-west-2 containing at least one reader instance.
Deploying the primary Amazon Aurora PostgreSQL database cluster in a Multi-AZ configuration ensures that the storage layer replicates data across multiple Availability Zones synchronously. If the primary writer instance fails, Aurora automatically promotes a reader instance in a different Availability Zone in under 60 seconds with zero data loss, achieving the local high availability goals. Meanwhile, configuring an Amazon Aurora Global Database creates a dedicated cross-region replication channel. The database is replicated asynchronously at the physical storage level with replication lag typically under 1 second, meeting the 1-minute RPO. The secondary cluster reader instance in the secondary region scales local reads and can be promoted to a writer within minutes to meet the 5-minute RTO during a disaster recovery scenario.

Step-by-Step Solution

1
Evaluate the high availability (HA) constraints in the primary region (useast1us-east-1).
An RTO of under 6060 seconds and RPO of 00 is identified. To meet this, the primary database must be deployed across multiple Availability Zones with synchronous storage replication and a standby reader instance ready for automatic failover.
Ensures that local infrastructure or AZ failure triggers automated failover with no data loss.
2
Evaluate the regional disaster recovery (DR) and read-scaling requirements in the secondary region (uswest2us-west-2).
An RTO of under 55 minutes and RPO of under 11 minute is identified, along with the need to serve local reads. A cross-region replication mechanism with sub-minute lag is required.
Determines the target disaster recovery mechanism that also allows active read workloads.
3
Select the correct combination of Amazon Aurora features to satisfy the constraints.
Aurora Multi-AZ deployment is selected for primary region HA, and Aurora Global Database with a secondary reader in us-west-2 is selected for regional DR and local read scaling.
Aurora Global Database replicates data storage-to-storage with a typical lag of under 11 second and supports seamless, low-RTO failovers.

Key Concept

Combining Amazon Aurora Multi-AZ deployment for local high availability with Amazon Aurora Global Database for cross-region disaster recovery and local read scaling.
Question 730Question

A solutions architect is configuring an active-passive disaster recovery (DR) strategy for a simple web application using Amazon Route 53. The primary endpoint is located in the us-east-1 Region, and the secondary backup endpoint is located in the us-west-2 Region. Which two configurations are required to set up this active-passive failover? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Create a primary failover record pointing to the primary endpoint in us-east-1 and associate a Route 53 health check with it; Create a secondary failover record pointing to the secondary endpoint in us-west-2

Answer

To configure Route 53 active-passive failover, you must create a primary failover record pointing to the primary endpoint in us-east-1 and associate a Route 53 health check with it, and also create a secondary failover record pointing to the secondary endpoint in us-west-2.
For an active-passive failover configuration, a primary failover record pointing to the primary endpoint must be created and linked to a Route 53 health check. This health check monitors the primary site. A secondary failover record pointing to the standby endpoint must also be configured. When the health check determines the primary site is down, Route 53 stops sending traffic to the primary record and starts resolving queries to the secondary record.

Step-by-Step Solution

1
Identify the primary active endpoint and the backup standby endpoint for the disaster recovery strategy.
The primary active endpoint is in us-east-1, and the secondary standby endpoint is in us-west-2.
This establishes the active-passive relationship where all normal traffic goes to us-east-1, and traffic only switches to us-west-2 during a disaster.
2
Create and configure a primary failover record in Route 53.
A record pointing to the us-east-1 endpoint with its routing policy set to Failover and failover type set to Primary, associated with a Route 53 health check.
Route 53 needs a health check associated with the primary record to monitor its availability and determine when to trigger failover.
3
Create and configure a secondary failover record in Route 53.
A record pointing to the us-west-2 endpoint with its routing policy set to Failover and failover type set to Secondary.
This record serves as the backup target. Route 53 only resolves queries to this record when the primary endpoint is determined to be unhealthy.

Key Concept

Route 53 Failover Routing Policy (Active-Passive)
Estimated Time:1m 0s
Question 731Question

A financial modeling firm is deploying a high-performance computing (HPC) application on a cluster of Amazon EC2 instances. The application requires concurrent, shared access to a POSIX-compliant file system. The file system must deliver sub-millisecond latencies and support parallel input/output (I/O) operations with a combined throughput of at least 8 GB/s8\text{ GB/s}. 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 FSx for Lustre persistent SSD file system to store the active datasets.; Mount the file system on the EC2 instances using the open-source Lustre client.

Answer

Create an Amazon FSx for Lustre persistent SSD file system to store the active datasets, and mount the file system on the EC2 instances using the open-source Lustre client.
The correct solution involves creating an Amazon FSx for Lustre file system using SSD storage and mounting it via the Lustre client. FSx for Lustre is built specifically for HPC workloads, providing sub-millisecond latencies, high parallel throughput, and native POSIX compliance. The Lustre client enables parallel data transfer channels from the EC2 instances to the storage nodes.

Step-by-Step Solution

1
Analyze the workload requirements: concurrent shared access, POSIX-compliant file system, sub-millisecond latencies, and high parallel throughput (8 GB/s8\text{ GB/s}).
Identify that Amazon FSx for Lustre is the AWS service designed specifically for high-performance computing (HPC) and parallel file access with SSD-backed sub-millisecond latency.
General-purpose shared storage like Amazon EFS does not meet the performance characteristics required for 8 GB/s8\text{ GB/s} parallel processing.
2
Evaluate configuration steps for FSx for Lustre.
Determine that a persistent SSD deployment is required for active, long-lived datasets, and that client EC2 instances must use the Lustre client to mount the file system.
This establishes high-speed, parallel access from the EC2 cluster to the FSx storage.
3
Eliminate invalid options based on block storage limits and file system corruption risks.
Discard options suggesting EBS gp3 Multi-Attach (not supported) and EBS io2 Multi-Attach with ext4 (corrupts data without a clustered file system).
Ensures the selected architecture is both supported by AWS and structurally sound.

Key Concept

Selecting high-performance, scalable, parallel shared file systems for high-performance computing (HPC) workloads on AWS.
Estimated Time:2m 0s
Question 732Question

An enterprise document management SaaS provider stores customer contract PDFs in an Amazon S3 bucket. The business requirements specify that:

- Files must be highly available and resilient against a single Availability Zone (AZ) failure, with a Recovery Time Objective (RTO) of less than 11 minute.
- Any files deleted or modified must be recoverable immediately.
- For disaster recovery, a copy of the contracts must be replicated to a secondary AWS Region with an RTO of less than 1515 minutes and a Recovery Point Objective (RPO) of less than 1515 minutes.
- Contracts are highly active for the first 4545 days and must be transitioned to a cheaper storage class afterward, while remaining instantly accessible.

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

Select all that apply

Show answer & explanation

Answer: Enable Amazon S3 Versioning on the bucket and configure Amazon S3 Cross-Region Replication (CRR) with S3 Replication Time Control (S3 RTC) enabled to the secondary Region.; Configure an S3 Lifecycle policy to transition objects from Amazon S3 Standard to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 4545 days.

Answer

Enable Amazon S3 Versioning on the bucket, configure Amazon S3 Cross-Region Replication (CRR) with S3 Replication Time Control (S3 RTC) enabled to the secondary Region, and configure an S3 Lifecycle policy to transition objects from Amazon S3 Standard to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 45 days.
The solution requires a combination of Amazon S3 Versioning, S3 Cross-Region Replication (CRR) with Replication Time Control (S3 RTC), and S3 Standard-IA lifecycle transition. S3 Versioning ensures that any overwrite or deletion can be immediately rolled back, fulfilling the immediate recovery requirement. S3 RTC guarantees that 99.9% of new uploads are replicated to the secondary Region within 15 minutes, satisfying the disaster recovery RPO and RTO. Transitioning objects to S3 Standard-IA after 45 days is optimal because S3 Standard-IA stores data redundantly across multiple Availability Zones (maintaining high availability and sub-minute RTO) and supports millisecond retrieval times (instant access) while saving cost.

Step-by-Step Solution

1
Select a mechanism for immediate file recovery.
Amazon S3 Versioning is enabled to store multiple versions of objects, allowing instant recovery of deleted or overwritten documents.
This directly satisfies the requirement that deleted or modified files must be recoverable immediately.
2
Select a replication mechanism that meets the disaster recovery constraints.
Amazon S3 Cross-Region Replication (CRR) with S3 Replication Time Control (S3 RTC) is selected.
S3 RTC guarantees replication of 99.9% of objects within 15 minutes, fulfilling the secondary Region's RPO/RTO constraints of under 15 minutes.
3
Determine the appropriate cost-effective storage tier transition.
A lifecycle policy transitions files to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 45 days.
S3 Standard-IA maintains multi-AZ resilience and provides millisecond (instantaneous) retrieval times, keeping files available under the 1-minute RTO, while reducing cost after the active 45-day period.

Key Concept

Designing a resilient, multi-AZ and multi-region storage architecture using Amazon S3 features including versioning, lifecycle transitions to Standard-IA, and Replication Time Control (S3 RTC).
Question 733Question

A multinational e-commerce company serves a dynamic product recommendation API to users globally. The API is hosted on Application Load Balancers (ALBs) in two AWS Regions: a primary region in eu-west-1 and a secondary standby region in us-east-1. The company uses an Amazon CloudFront distribution to cache recommendation payloads at edge locations. To ensure high availability, the company needs to configure automated failover to the secondary region's ALB only if the primary ALB returns HTTP 502 (Bad Gateway) or HTTP 503 (Service Unavailable) status codes. The solution must minimize operational complexity, maintain edge caching benefits, and avoid client-side DNS propagation delays. Which architecture should a solutions architect recommend to meet these requirements?

Show answer & explanation

Answer: Configure a CloudFront Origin Group containing the primary ALB as the primary origin and the secondary ALB as the failover origin, setting the failover criteria to include HTTP 502 and 503 status codes.

Answer

Configure a CloudFront Origin Group containing the primary ALB as the primary origin and the secondary ALB as the failover origin, setting the failover criteria to include HTTP 502 and 503 status codes.
The correct solution uses an Amazon CloudFront Origin Group containing the primary ALB as the primary origin and the secondary ALB as the failover origin, configured to trigger failover on 502 and 503 status codes. This ensures that CloudFront automatically and transparently redirects requests to the secondary region if the primary region encounters server issues, maintaining edge caching for successful requests and avoiding DNS-based failover delays.

Step-by-Step Solution

1
Analyze the requirements for low-latency delivery, high availability, edge caching preservation, and failover based on backend HTTP 502/503 status codes.
The solution must leverage CloudFront to maintain edge caching and fail over to a backup ALB without relying on DNS-based redirection, which introduces propagation delays.
This establishes the architectural boundary where caching must remain active at the edge, and failover must occur dynamically at the CDN layer.
2
Evaluate Amazon CloudFront Origin Groups as the native mechanism for origin failover.
An Origin Group allows grouping a primary origin (primary ALB) and a secondary origin (secondary ALB), with automated failover triggered when the primary origin returns specific HTTP status codes.
This directly meets the failover criteria (HTTP 502 and 503) with minimal operational overhead and near-instant failover.
3
Assess and eliminate alternative solutions such as Route 53 routing, AWS WAF, and TTL reduction.
Route 53 bypasses CloudFront caching; AWS WAF cannot route origins; TTL reduction to zero disables edge caching completely.
This confirms that only the Origin Group option achieves all design constraints (resiliency, performance, and low operational complexity).

Key Concept

Implementing Amazon CloudFront Origin Groups for automated origin-level failover of dynamic HTTP endpoints.
Question 734Question

A financial modeling firm is deploying a tightly coupled High-Performance Computing (HPC) simulation workload on Amazon EC2 instances in a single AWS Region. The application relies on the Message Passing Interface (MPI) library for node-to-node communication. To achieve maximum performance, the network architecture must support low latency and inter-instance throughput of up to 100 Gbps100\text{ Gbps}. Which combination of network configuration and instance placement will meet these requirements?

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group and configure them to use Elastic Fabric Adapter (EFA) interfaces.

Answer

Launch the EC2 instances in a cluster placement group and configure them to use Elastic Fabric Adapter (EFA) interfaces.
The correct answer is launching the instances in a cluster placement group and utilizing Elastic Fabric Adapter (EFA). A cluster placement group places instances logically close to each other within a single Availability Zone, enabling low-latency and high-throughput networking. EFA provides OS-bypass (bypassing the operating system kernel) which is necessary to achieve high throughput (up to 100 Gbps100\text{ Gbps}) and low latency for tightly coupled Message Passing Interface (MPI) applications.

Step-by-Step Solution

1
Identify the workload requirements.
Tightly coupled HPC simulation using MPI, requiring sub-millisecond node-to-node latency and up to 100 Gbps100\text{ Gbps} throughput.
Selecting the correct network interface and placement group requires matching the workload's specific performance profile.
2
Determine the appropriate network interface for MPI workloads.
Select Elastic Fabric Adapter (EFA) instead of a standard Elastic Network Interface (ENI).
EFA supports OS-bypass capabilities, allowing MPI applications to communicate directly with the network interface hardware, reducing latency and maximizing throughput.
3
Determine the optimal instance placement strategy.
Launch instances within a cluster placement group in a single Availability Zone.
Cluster placement groups ensure instances are physically close on the same network hardware fabric, minimizing latency and supporting high-throughput node-to-node communication.

Key Concept

Elastic Fabric Adapter (EFA) and Cluster Placement Groups for HPC Workloads
Estimated Time:1m 30s
Question 735Question

A global news website serves static web assets (images, CSS, JS files) stored in an Amazon S3 bucket, and dynamic personalized articles generated by an API running on Amazon EC2 instances in private subnets behind an Application Load Balancer (ALB). The API retrieves article content from an Amazon RDS database. During breaking news events, the website experiences significant latency, and both the ALB and the S3 bucket are overloaded by traffic spikes. Additionally, the application generates transient log files that must be stored in Amazon S3 for exactly 7 days before deletion. Which TWO solutions should a solutions architect implement to optimize content delivery performance and minimize operational costs?

Select all that apply

Show answer & explanation

Answer: Configure an Amazon CloudFront distribution with the ALB as the origin for dynamic content paths and the S3 bucket as the origin for static content paths. Enable caching on the static behaviors to offload requests from the S3 bucket.; Deploy Amazon ElastiCache in front of the Amazon RDS database to cache frequently requested article data in memory, reducing load on the database and decreasing API response times.

Answer

Configure an Amazon CloudFront distribution with the ALB as the origin for dynamic content paths and the Amazon S3 bucket as the origin for static content paths, caching only the static assets. Simultaneously, deploy Amazon ElastiCache in front of the database to cache frequently accessed dynamic query results.
Configuring Amazon CloudFront with separate behaviors ensures that static content is cached close to users, reducing latency and offloading the S3 bucket. Dynamic routes are passed through directly to the Application Load Balancer to guarantee personalization. At the database tier, Amazon ElastiCache caches recurrent query results in memory, drastically reducing the load on the RDS instance and accelerating the overall API response time.

Step-by-Step Solution

1
Analyze the static asset delivery flow.
Identify that CloudFront can cache S3 objects at edge locations, offloading the S3 bucket and reducing latency.
Serving static files directly from CloudFront caches prevents traffic spikes from overwhelming the S3 origin.
2
Analyze database and dynamic API performance.
Identify that deploying Amazon ElastiCache in front of Amazon RDS caches frequent read queries.
An in-memory cache reduces RDS processor utilization and speeds up dynamic data retrieval.
3
Evaluate the transient log storage requirement.
Recognize that S3 Standard-IA has a 30-day minimum storage billing period. Deleting logs in 7 days incurs a cost penalty, so S3 Standard should be used instead.
Avoids unnecessary charges associated with early deletion in infrequent access tiers.
4
Evaluate routing mechanisms.
Verify that using a NAT Gateway for high-volume internal data transfers is highly expensive compared to using a free Gateway VPC Endpoint for S3.
Minimizes data processing costs and enhances internal performance.

Key Concept

Multi-tier caching using Amazon CloudFront and Amazon ElastiCache, alongside cost-optimization strategies for short-term storage and internal network routing.
Question 736Question

A company is deploying a media rendering application on a fleet of Amazon EC2 instances distributed across three Availability Zones. The instances require concurrent, shared access to a 500 GB500\text{ GB} dataset. The workload is highly read-heavy and requires low-latency shared file access. The shared files are temporary and are deleted within 10 days10\text{ days} of creation. Which storage solution should a solutions architect recommend to achieve the highest performance and cost-efficiency?

Show answer & explanation

Answer: Provision an Amazon EFS file system using Elastic throughput and mount it on the EC2 instances across the Availability Zones.

Answer

Provision an Amazon EFS file system using Elastic throughput and mount it on the EC2 instances across the Availability Zones.
Amazon EFS is a managed file storage service that natively supports concurrent, shared access across multiple Availability Zones in a Region. Using Elastic throughput ensures that the file system dynamically handles high-throughput spikes associated with read-heavy rendering. Furthermore, EFS has no minimum storage duration fee, making it the most cost-effective choice for a dataset that is deleted after 10 days10\text{ days}.

Step-by-Step Solution

1
Evaluate the architectural need for concurrent, multi-AZ shared access.
Amazon EBS is ruled out because standard volumes cannot span multiple Availability Zones, and EBS Multi-Attach is limited to a single Availability Zone.
Ensure that the proposed storage layer can physically connect to compute instances across three different Availability Zones.
2
Evaluate Amazon EFS throughput configuration options against a read-heavy workload.
Amazon EFS natively supports multi-AZ mount targets. Configuring it with Elastic throughput ensures that performance scales dynamically to handle the read-heavy demands of the rendering application.
Select a performance configuration that matches the access behavior of the application without unnecessary over-provisioning.
3
Calculate the cost implications of temporary data retention (10 days10\text{ days}) across EFS and S3 storage classes.
EFS charges only for the actual storage used with no minimum storage duration. Amazon S3 Standard-IA imposes a 30 day30\text{ day} minimum charge and data retrieval fees, resulting in higher costs for short-lived, frequently read datasets.
Compare storage class lifecycle rules and data access charges to select the most cost-effective solution.

Key Concept

Selecting shared, multi-AZ storage that matches high-throughput read requirements and optimizes cost for short-term workloads.
Question 737Question

A logistics company operates a fleet tracking platform on AWS. The application architecture consists of:
1. An API ingest layer hosted on Amazon ECS with AWS Fargate that requires a continuous, predictable baseline of 1616 vCPUs and 32 GB32\text{ GB} of memory 24/724/7.
2. A batch processing workload on Amazon ECS with AWS Fargate that analyzes historical telemetry data. These tasks are triggered daily, can tolerate interruptions, and do not have strict completion SLAs.
3. An Amazon DynamoDB table used to store real-time location metrics, which experiences sudden, highly unpredictable spikes in write traffic.

The company wants to optimize its compute and database costs over a 33-year period.

Which two strategies should a solutions architect recommend to achieve the most cost-effective architecture? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Purchase a 33-year Compute Savings Plan to cover the baseline API ingest layer running on AWS Fargate.; Configure the batch processing workload on ECS to run using the Fargate Spot capacity provider.

Answer

The correct strategies are purchasing a 33-year Compute Savings Plan for the baseline API ingest layer and configuring the batch processing workload to use the Fargate Spot capacity provider.
The correct strategies are purchasing a 33-year Compute Savings Plan for the baseline API ingest layer and configuring the batch processing workload to use the Fargate Spot capacity provider. A Compute Savings Plan provides significant savings for baseline, predictable compute usage on AWS Fargate. Fargate Spot is the most cost-effective option for non-urgent, interruptible batch processing tasks because it offers up to a 70%70\% discount without requiring any long-term commitments.

Step-by-Step Solution

1
Analyze the API ingest layer's compute requirements on AWS Fargate.
The workload requires a continuous, steady-state baseline of 1616 vCPUs and 32 GB32\text{ GB} of memory 24/724/7.
For baseline, predictable compute usage over a long duration, a 33-year Compute Savings Plan offers the highest discount (up to 66%66\%) and applies directly to AWS Fargate compute usage.
2
Evaluate the batch processing workload's operational profile.
The batch tasks can tolerate interruptions, run daily, and lack strict completion SLAs.
ECS Fargate Spot is the most cost-effective purchasing option for interruptible workloads, providing up to a 70%70\% discount compared to standard On-Demand Fargate pricing without requiring long-term commitments.
3
Determine the cost-optimization strategy for the Amazon DynamoDB table.
The table experiences sudden, unpredictable spikes in write traffic, making it unsuitable for provisioned capacity or compute savings plans.
For unpredictable, spiky database workloads, DynamoDB On-Demand capacity mode is the most cost-effective choice because it charges per request, avoiding over-provisioning costs or throttling. Compute Savings Plans do not apply to DynamoDB.

Key Concept

Selecting cost-optimized compute hosting models and purchasing strategies (Compute Savings Plans, Fargate Spot) while understanding their scopes and limits.
Question 738Question

A company is building an online auction application where users submit bids in real time. The backend database experiences write performance issues during high-traffic spikes at the end of auctions. To ensure fairness, the bids must be processed in the exact order they are received by the application. A solutions architect needs to design a decoupled architecture that buffers incoming bids and processes them sequentially. Which solution meets these requirements with the least operational overhead?

Show answer & explanation

Answer: Send the incoming bids to an Amazon SQS FIFO queue and configure the backend service to consume and process the messages sequentially.

Answer

Send the incoming bids to an Amazon SQS FIFO queue and configure the backend service to consume and process the messages sequentially.
The correct solution uses an Amazon SQS FIFO queue to buffer incoming write requests (bids) and ensure they are processed in the exact order they are received. SQS FIFO queues guarantee first-in, first-out delivery and exactly-once processing, which is required for determining the auction winner fairly, while decoupling the frontend from the database to handle traffic spikes.

Step-by-Step Solution

1
Identify the key requirements from the scenario.
The solution must decouple the application from the database, handle write spikes, and process bids in the exact order they are received.
This establishes the constraints that any valid architecture must satisfy.
2
Evaluate the messaging service that provides decoupling and order preservation.
Amazon SQS FIFO (First-In-First-Out) queues guarantee that messages are processed in the exact order they are sent, while also providing a buffer to decouple the frontend from the backend database.
Standard queues do not guarantee ordering, making FIFO the correct choice for ordering requirements.
3
Eliminate options that do not support writes or decouple the architecture effectively.
RDS Read Replicas cannot accept writes, and a continuous Lambda function violates serverless design principles and fails to provide reliable buffer-based decoupling.
This confirms that the SQS FIFO queue is the only solution that meets all constraints with minimal operational overhead.

Key Concept

Decoupling with ordered messaging using Amazon SQS FIFO queues
Question 739Question

A financial technology company is migrating a critical, single-instance relational database to AWS. The database requires a single 2 TB2\text{ TB} volume that must sustain a consistent write throughput of 3,200 MB/s3,200\text{ MB/s} and 120,000 IOPS120,000\text{ IOPS} with sub-millisecond storage latency. Which Amazon EBS volume configuration should a solutions architect recommend to meet these performance requirements?

Show answer & explanation

Answer: A single Amazon EBS io2\text{io2} volume attached to an Amazon EC2 instance type that supports EBS Block Express

Answer

A single Amazon EBS io2 volume attached to an Amazon EC2 instance type that supports EBS Block Express
The correct answer is a single Amazon EBS io2 volume attached to an Amazon EC2 instance type that supports EBS Block Express. Amazon EBS io2 Block Express volumes are designed for the most demanding I/O-intensive workloads, providing up to 256,000 IOPS256,000\text{ IOPS}, 4,000 MB/s4,000\text{ MB/s} throughput, and sub-millisecond latency per volume. This meets all specified constraints (120,000 IOPS120,000\text{ IOPS} and 3,200 MB/s3,200\text{ MB/s} throughput) on a single volume.

Step-by-Step Solution

1
Analyze the workload's storage volume requirements.
The requirements are a single volume of 2 TB2\text{ TB} size, needing 120,000 IOPS120,000\text{ IOPS}, 3,200 MB/s3,200\text{ MB/s} throughput, and consistent sub-millisecond latency.
This establishes the minimum baseline limits for selecting the EBS volume type.
2
Evaluate EBS gp3, io1, and standard io2 limits.
gp3 limits are 16,000 IOPS16,000\text{ IOPS} and 1,000 MB/s1,000\text{ MB/s}. io1 and standard io2 limits are 64,000 IOPS64,000\text{ IOPS} and 1,000 MB/s1,000\text{ MB/s}. None of these configurations meet the 120,000 IOPS120,000\text{ IOPS} and 3,200 MB/s3,200\text{ MB/s} throughput target on a single volume.
Understanding the limits of standard EBS volumes eliminates options that cannot scale to the required performance.
3
Identify EBS io2 Block Express capability.
EBS io2 Block Express volumes (available when io2 is attached to supported EC2 instances) support up to 256,000 IOPS256,000\text{ IOPS}, 4,000 MB/s4,000\text{ MB/s} throughput, and sub-millisecond latency on a single volume.
io2 Block Express provides the required IOPS, throughput, and sub-millisecond latency natively without the complexity of RAID configurations.

Key Concept

Amazon EBS io2 Block Express architecture and performance limits
Estimated Time:2m 0s
Question 740Question

A solutions architect is designing a web application that uses an Amazon RDS for PostgreSQL database. The application is experiencing high read latency due to a large volume of read requests. Which solution will most effectively reduce read latency for frequently accessed data to sub-milliseconds?

Show answer & explanation

Answer: Deploy Amazon ElastiCache in front of the Amazon RDS database to cache frequently accessed read queries.

Answer

Deploy Amazon ElastiCache in front of the Amazon RDS database to cache frequently accessed read queries.
Deploying Amazon ElastiCache in front of the Amazon RDS database is the standard high-performance architecture pattern to serve read requests from memory, reducing query response times to sub-milliseconds.

Step-by-Step Solution

1
Identify the latency and access requirements from the scenario.
The application requires sub-millisecond read latency for frequently accessed database queries.
Recognizing that the target is sub-millisecond reads narrows down the choice to in-memory caching solutions.
2
Evaluate the caching capabilities of Amazon ElastiCache vs other database configurations.
Amazon ElastiCache caches RDS query results in memory, offering sub-millisecond retrieval speeds.
Standard RDS configurations (including read replicas) cannot guarantee sub-millisecond latencies for arbitrary query patterns.

Key Concept

Using an in-memory cache like Amazon ElastiCache to offload read queries from a relational database and achieve sub-millisecond latencies.
Estimated Time:1m 0s
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