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Question 781Question →

A company hosts its static website assets, such as images and stylesheets, in an Amazon S3 bucket. The company wants to reduce latency for global users accessing these assets and reduce the request load on the S3 bucket.

Which solution should a solutions architect recommend to meet these requirements?

Show answer & explanation

Answer: Deploy an Amazon CloudFront distribution with the Amazon S3 bucket configured as the origin and default caching enabled.

Answer

Deploy an Amazon CloudFront distribution with the Amazon S3 bucket configured as the origin and default caching enabled.
Deploying an Amazon CloudFront distribution with default caching enabled leverages AWS edge locations globally to cache static assets close to users. This reduces retrieval latency and significantly lowers the number of direct requests to the Amazon S3 origin bucket.

Step-by-Step Solution

1
Analyze the workload characteristics and requirements.
The workload consists of static website assets (images and stylesheets) stored in S3 that must be delivered with low latency globally while minimizing request load on the S3 origin.
Static assets are ideal candidates for edge caching, which solves both latency and origin load requirements.
2
Select the appropriate edge caching service.
Amazon CloudFront is chosen because it caches content at global edge locations.
By serving cached content directly from edge locations, CloudFront reduces the distance data travels to the user and prevents requests from reaching the S3 bucket.
3
Verify cache settings for performance optimization.
Default caching must be enabled to ensure static assets are stored in edge caches.
Setting the Time to Live (TTL) settings to 00 seconds would disable caching, forcing all traffic back to S3.

Key Concept

Caching static assets at edge locations using Amazon CloudFront to reduce origin load and latency
Question 782Question →

A company hosts a critical web application on Amazon EC2 instances behind an Application Load Balancer (ALB) in the us-west-2 Region. The application queries a Multi-AZ Amazon RDS DB instance. The company wants to implement an active-passive disaster recovery (DR) strategy with a Recovery Time Objective (RTO) of under 2 minutes. The passive site consists of a static maintenance website hosted in an Amazon S3 bucket in the us-east-1 Region. The system must automatically redirect users to the maintenance page if the primary application servers or the database fail. Which configuration should a solutions architect implement to meet these requirements?

Show answer & explanation

Answer: Configure an HTTPS Route 53 health check to monitor a custom application endpoint that verifies database connectivity. Create a primary Failover Alias A record pointing to the ALB, associate it with the health check, and set Evaluate Target Health to Yes. Create a secondary Failover Alias A record pointing to the S3 static website endpoint.

Answer

Configure an HTTPS Route 53 health check to monitor a custom application endpoint that verifies database connectivity. Create a primary Failover Alias A record pointing to the ALB, associate it with the health check, and set Evaluate Target Health to Yes. Create a secondary Failover Alias A record pointing to the S3 static website endpoint.
The correct configuration uses an HTTPS health check pointing to a custom endpoint on the application that actively verifies connection to the database. This deep health check ensures that database-level issues trigger a failover. By using an Alias A record for the primary ALB with Failover routing and Evaluate Target Health set to Yes, Route 53 can evaluate both the ALB health and the associated custom health check. Setting up the secondary Failover Alias record pointing to the S3 website endpoint ensures that Route 53 immediately routes traffic to the static maintenance page upon detecting primary failure, meeting the tight RTO requirement.

Step-by-Step Solution

1
Set up a deep health check endpoint on the web application.
An endpoint (such as /health) is created that checks connection to the Amazon RDS database and returns an HTTP 200 OK status only when the web servers and database are functional.
Standard ALB health checks only evaluate the ALB or the web servers, not downstream dependencies like the database. A deep health check ensures database failures trigger a failover.
2
Create a Route 53 health check.
A Route 53 HTTP/HTTPS health check is created pointing to the domain and the custom deep health endpoint, checking it at regular intervals.
This health check will report unhealthy if either the application servers or the database fails, providing the trigger for Route 53 to swap DNS targets.
3
Create the primary Route 53 Failover record.
An Alias A record pointing to the ALB is created with the Failover routing policy, associated with the Route 53 health check, and 'Evaluate Target Health' set to Yes.
Alias records allow the 'Evaluate Target Health' option to be set to Yes, which integrates the load balancer's health status with Route 53's routing decision.
4
Create the secondary Route 53 Failover record.
An Alias A record pointing to the S3 static website endpoint is created with the Failover routing policy.
When the primary record is determined to be unhealthy, Route 53 automatically fails over to the secondary record, pointing users to the maintenance page.

Key Concept

Active-passive DNS failover in Route 53 requires Alias records to support Evaluate Target Health, deep health checks to monitor backend dependencies, and a secondary resource that is immediately ready to serve traffic.
Question 783Question →

A media company hosts a containerized microservice on Amazon ECS using AWS Fargate that runs continuously to handle steady-state traffic. The company wants to optimize its compute costs over a 1-year period. Which strategy will provide the most cost-effective compute purchasing strategy for this workload?

Show answer & explanation

Answer: Purchase a Compute Savings Plan to cover the baseline Fargate compute capacity.

Answer

Purchase a Compute Savings Plan to cover the baseline Fargate compute capacity.
Purchasing a Compute Savings Plan is the most cost-effective option because Compute Savings Plans apply directly to AWS Fargate usage. They offer a discount of up to 66% in exchange for a 1-year or 3-year commitment to a consistent amount of compute usage, making them ideal for steady-state workloads.

Step-by-Step Solution

1
Analyze the workload characteristics and hosting model.
The workload is a steady-state microservice running continuously on Amazon ECS with AWS Fargate.
Understanding the host platform and traffic pattern is necessary to select the right purchasing model.
2
Evaluate the eligibility of AWS Fargate for Savings Plans.
Compute Savings Plans apply to EC2, AWS Fargate, and AWS Lambda compute usage.
This determines which commitment-based discount mechanism is applicable to the current architecture.
3
Eliminate options that misapply pricing models or services.
Amazon RDS is not eligible for Compute Savings Plans, AWS Lambda is expensive for 24/7 workloads, and AWS Cost Explorer is not a resource management tool.
This narrows down the selection to the only valid and cost-optimal choice.

Key Concept

Compute Savings Plans provide a flexible discount model that applies to Amazon EC2, AWS Fargate, and AWS Lambda usage in exchange for a commitment to a consistent amount of compute usage.
Question 784Question →

An online auction platform experiences massive spikes in read and write traffic during the final minutes of high-profile auctions. The system requires sub-millisecond latency for retrieving item details, and must handle a write rate of over 80,00080,000 updates per second for bid submissions. The database architecture must scale dynamically to handle these bursts without throttling. Which combination of database configurations will meet these performance requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Store the auction item details in Amazon DynamoDB, and deploy an Amazon DynamoDB Accelerator (DAX) cluster to cache read requests.; Implement write sharding on the bids table in Amazon DynamoDB by appending a random suffix to the partition key to distribute write operations across multiple physical partitions.

Answer

Use Amazon DynamoDB to store the auction item details with Amazon DynamoDB Accelerator (DAX) to cache read requests, and implement write sharding on the bids table by appending a random suffix to the partition key.
To achieve sub-millisecond read latency for DynamoDB, an in-memory caching layer like Amazon DynamoDB Accelerator (DAX) is required. To handle a write throughput of over 80,00080,000 updates per second without throttling, the write load must be evenly distributed across partitions. This is achieved by appending a random suffix to the partition key (write sharding) to prevent hot partitions.

Step-by-Step Solution

1
Analyze read performance requirements.
The platform requires sub-millisecond latency for retrieving item details, which is a read-heavy operation.
Standard DynamoDB queries operate in the single-digit millisecond range. To achieve sub-millisecond (microsecond) latency, an in-memory cache such as Amazon DynamoDB Accelerator (DAX) must be deployed.
2
Analyze write performance requirements.
The platform must support over 80,00080,000 write updates per second for bids.
To prevent write bottlenecks and physical partition limits, write sharding should be implemented. Appending a random suffix to the partition key distributes the workload across multiple partition keys.
3
Evaluate the architectural feasibility of the options.
Combining DynamoDB with DAX for reads and using a sharded partition key for writes meets the low-latency read and high-throughput write requirements without hitches.
Alternative relational database configurations like RDS fail to meet the performance scale and introduce misconfigured patterns like using read replicas for failover or writing to reader instances.

Key Concept

Scaling read and write performance in DynamoDB using DAX and write sharding.
Question 785Question →

A financial analytics firm is deploying a high-frequency risk assessment platform on AWS. The application uses a distributed, memory-intensive computing grid on Amazon EC2 instances to process real-time market feeds. The compute nodes must exchange state data with node-to-node network latency of less than 10 microseconds. The platform receives incoming market data streams via UDP on port 9999. A Network Load Balancer (NLB) is used to distribute the incoming UDP traffic across the EC2 instances. To monitor the health of the risk assessment application, a management service runs on TCP port 8080 on each instance. The instance fleet must scale dynamically based on CPU utilization.

Which combination of placement strategy and load balancer configuration should a Solutions Architect recommend to satisfy these requirements?

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group within a single Availability Zone. Configure the NLB target group to use the UDP protocol on port 9999, and override the health check configuration to use the TCP protocol on port 8080.

Answer

The correct configuration is to launch the EC2 instances in a cluster placement group within a single Availability Zone, configure the NLB target group to use the UDP protocol on port 9999, and override the health check configuration to use the TCP protocol on port 8080.
The correct solution involves launching the EC2 instances in a cluster placement group within a single Availability Zone, configuring the NLB target group to use the UDP protocol on port 9999, and overriding the health check configuration to use the TCP protocol on port 8080. A cluster placement group enables the low-latency, high-throughput node-to-node networking required by the distributed memory-intensive computing grid. Furthermore, because the health monitoring agent runs on a different port (TCP 8080) than the application data stream (UDP 9999), overriding the target group health check settings ensures that the NLB correctly monitors the nodes' actual health status without generating false negatives.

Step-by-Step Solution

1
Analyze the network latency requirement.
Since the distributed computing grid requires sub-10 microsecond node-to-node latency, a cluster placement group must be used to pack the instances close together on the physical hardware within a single Availability Zone.
Other placement strategies (spread, partition) are optimized for high availability and fault isolation, not low-latency node-to-node communication.
2
Identify the protocol and port for client traffic routing.
The platform receives market data streams via UDP on port 9999, which requires configuring the Network Load Balancer target group with the UDP protocol on port 9999.
Selecting the incorrect protocol (such as TCP) will prevent the NLB from properly routing the incoming UDP market data streams.
3
Determine the correct health check configuration for the target group.
Because the health monitoring daemon runs on TCP port 8080, the health check configuration must be overridden to TCP port 8080.
Leaving the health check on the default traffic port would cause the NLB to attempt to query UDP port 9999, which does not run the health service and would cause the NLB to mark all healthy instances as unhealthy.

Key Concept

High-performance EC2 networking using cluster placement groups combined with Network Load Balancer target group health check port overrides for non-standard port applications.
Question 786Question →

A logistics company uploads daily vehicle telemetry reports averaging 10 MB10\text{ MB} to an Amazon S3 Standard bucket. The files are accessed frequently for the first 3030 days, after which they are rarely accessed. The reports must be retained for a total of 9090 days and can then be deleted.

Which TWO configurations should a solutions architect implement in an S3 Lifecycle policy to meet these requirements cost-effectively?

Select all that apply

Show answer & explanation

Answer: Transition the reports to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 3030 days after creation; Configure the lifecycle policy to expire the reports 9090 days after creation

Answer

Configure the lifecycle policy to transition the reports to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 3030 days after creation and expire the reports 9090 days after creation.
Transitioning the reports to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) 3030 days after creation matches the access pattern, as the files become rarely accessed after 3030 days. Because they are deleted 9090 days after creation, they reside in S3 Standard-IA for 6060 days, which exceeds the 3030-day minimum storage duration requirement. Expiring the reports 9090 days after creation satisfies the data retention requirement and stops further storage charges.

Step-by-Step Solution

1
Analyze the data access pattern and retention period
Reports are frequently accessed for the first 3030 days, rarely accessed from day 3131 to 9090, and deleted at day 9090.
This establishes the timeline for storage transitions and expiration.
2
Determine the optimal storage tier for the rarely accessed phase
S3 Standard-IA is ideal for rarely accessed data that requires immediate retrieval, provided the objects are stored there for at least 3030 days.
S3 Standard-IA offers lower storage costs than S3 Standard but charges for data retrieval and requires a 3030-day minimum storage period.
3
Select the lifecycle transitions and expiration rules that minimize cost without penalties
Transition to S3 Standard-IA on day 3030. The objects reside in S3 Standard-IA for 6060 days (until day 9090), avoiding the 3030-day minimum duration penalty. Expire the objects on day 9090 to stop storage charges completely.
This configuration aligns perfectly with the access pattern and avoids the minimum duration charge penalty.

Key Concept

Amazon S3 Lifecycle policies optimize storage costs by transitioning data to cheaper storage tiers or deleting it based on age, while taking into account minimum storage duration rules for tiers like S3 Standard-IA.
Question 787Question →

A company runs a web application in the us−west−2us-west-2 Region that serves global clients. Users in Europe are experiencing high latency due to TCP handshake overhead across the public internet. Additionally, the company's on-premises data center needs to transfer daily database backups of approximately 5 GB5\text{ GB} to an Amazon S3 bucket in the same Region. The on-premises transfer requires a minimum of 2 Gbps2\text{ Gbps} bandwidth over a secure connection, but the current network setup is unable to scale beyond 1.25 Gbps1.25\text{ Gbps} throughput. Which TWO configurations should a solutions architect implement to address the user latency and network throughput requirements?

Select all that apply

Show answer & explanation

Answer: Configure AWS Global Accelerator to route traffic to the application endpoint in the AWS Region, utilizing the AWS global network to reduce TCP connection latency for European users.; Deploy an AWS Transit Gateway and establish multiple AWS Site-to-Site VPN connections, enabling Equal-Cost Multi-Path (ECMP) routing to scale the tunnel throughput for backup transfers.

Answer

Implementing AWS Global Accelerator to route European user traffic over the AWS global network, and deploying AWS Transit Gateway with multiple AWS Site-to-Site VPN connections enabling Equal-Cost Multi-Path (ECMP) routing to scale backup throughput.
The correct configuration combines AWS Global Accelerator and AWS Transit Gateway with ECMP. AWS Global Accelerator terminates TCP connections at edge locations close to European users and routes their traffic over the low-latency AWS global network, reducing latency and connection establishment time. AWS Transit Gateway enables Equal-Cost Multi-Path (ECMP) routing, allowing the aggregation of multiple VPN tunnels to scale network throughput beyond the standard 1.25 Gbps1.25\text{ Gbps} limit to meet the 2 Gbps2\text{ Gbps} requirement.

Step-by-Step Solution

1
Analyze the latency requirements for global users.
Identify that European users face TCP handshake latency over the public internet to the application in the primary Region.
Since the application is in a single Region, a service that terminates TCP connections at edge locations closer to the users, such as AWS Global Accelerator, is required to reduce latency.
2
Analyze the hybrid network connection throughput requirements.
Determine that the required throughput is at least 2 Gbps2\text{ Gbps} for database backups, which exceeds the 1.25 Gbps1.25\text{ Gbps} limit of a single VPN tunnel.
To scale VPN throughput beyond the single-tunnel limit, a transit gateway must be deployed to enable Equal-Cost Multi-Path (ECMP) routing across multiple active VPN tunnels.
3
Select the correct combination of network services.
Combine AWS Global Accelerator for global traffic acceleration and AWS Transit Gateway with multiple VPN connections for ECMP routing.
This configuration addresses the latency bottleneck for global clients and the performance constraint on the hybrid connection.

Key Concept

AWS network optimization using Global Accelerator and Transit Gateway ECMP routing to scale throughput and reduce global user latency.
Estimated Time:2m 0s
Question 788Question →

A solutions architect is designing a high-performance computing (HPC) database application on Amazon EC2. The database nodes require maximum network performance and the lowest possible latency for node-to-node communication. Which configuration should the solutions architect use to meet these network performance requirements?

Show answer & explanation

Answer: Deploy the EC2 instances in a cluster placement group.

Answer

Deploy the EC2 instances in a cluster placement group.
Deploying the EC2 instances in a cluster placement group is correct because it logically groups instances within a single Availability Zone. This configuration provides the lowest possible network latency and highest packet-per-second performance for tightly-coupled node-to-node communication.

Step-by-Step Solution

1
Identify the primary performance requirement of the workload.
The database application requires maximum network performance and the lowest possible latency for node-to-node communication.
Understanding the workload constraints is necessary to select the correct compute placement strategy.
2
Evaluate the available EC2 placement options against the low-latency requirement.
Cluster placement groups place instances close together inside a single Availability Zone, which minimizes latency and maximizes throughput.
Selecting the correct placement group configuration guarantees the hardware-level performance characteristics needed for the application.
3
Eliminate configurations designed for high availability or load balancing.
Spread placement groups, partition placement groups, and Network Load Balancers do not provide the low-latency node-to-node network characteristics requested.
Spread and partition groups prioritize fault tolerance and failure isolation, while load balancers handle external traffic distribution rather than internal node clustering.

Key Concept

Selecting the appropriate EC2 placement group for high-performance network workloads.
Estimated Time:1m 0s
Question 789Question →

A solutions architect is designing a database solution for a web application that displays real-world financial indices. The application runs on Amazon RDS for MySQL. The database workload is highly read-heavy, with a read-to-write ratio of 10:110:1, and requires read latencies of less than 1010 milliseconds. During peak market hours, the CPU utilization on the database instance reaches 90%90\%, resulting in latency spikes. Which action should the solutions architect take to improve database read performance?

Show answer & explanation

Answer: Create Amazon RDS read replicas to offload read traffic from the primary DB instance, and configure the application to route read queries to the replica endpoints.

Answer

Create Amazon RDS read replicas to offload read traffic from the primary DB instance, and configure the application to route read queries to the replica endpoints.
Creating Amazon RDS read replicas is the standard method to scale database read performance. By routing read queries to the replica endpoints, the solutions architect offloads the CPU-intensive read workload from the primary DB instance, resolving the bottleneck while keeping write capacity on the primary intact.

Step-by-Step Solution

1
Analyze the database workload requirements.
The workload has a high read-to-write ratio of 10:110:1 and requires low-latency reads under heavy load.
Identifying that the performance bottleneck is caused by read queries helps target read-scaling solutions.
2
Evaluate read-scaling mechanisms in Amazon RDS.
Amazon RDS read replicas can asynchronously replicate data from the primary DB instance to one or more read replicas.
Read replicas are designed specifically to handle read-heavy workloads by offloading read queries from the primary instance.
3
Configure the application to utilize the read replica endpoints.
Read traffic is routed to the read replicas, while write traffic continues to be sent to the primary DB instance.
This separation reduces CPU load on the primary database, lowering read latency and maintaining high performance.

Key Concept

Offloading read traffic using Amazon RDS read replicas
Question 790Question →

A company is migrating a legacy application from an on-premises data center to Amazon EC2 instances. The application consists of both Linux and Windows servers that must concurrently access a shared file system. The application requires support for both NFS and SMB protocols, along with block-level storage access via iSCSI. The storage solution must deliver sub-millisecond latency and support thin provisioning to optimize storage usage. 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 FSx for NetApp ONTAP file system.; Mount the file system using NFS for the Linux instances, SMB for the Windows instances, and configure iSCSI logical unit numbers (LUNs) for block storage.

Answer

Create an Amazon FSx for NetApp ONTAP file system and mount the file system using NFS for the Linux instances, SMB for the Windows instances, and configure iSCSI logical unit numbers (LUNs) for block storage.
Amazon FSx for NetApp ONTAP is a fully managed service that supports NFS, SMB, and iSCSI protocols, allowing concurrent access from Linux and Windows instances, as well as block-level storage. It natively supports storage efficiency features like thin provisioning, deduplication, and compression.

Step-by-Step Solution

1
Analyze the file system requirements.
The file system must support NFS, SMB, and iSCSI protocols, support both Linux and Windows concurrent access, provide sub-millisecond latencies, and support thin provisioning.
This establishes the constraints for selecting the AWS storage service.
2
Evaluate the storage services against the protocols.
Amazon FSx for NetApp ONTAP natively supports NFS, SMB, and iSCSI protocols along with advanced ONTAP features like thin provisioning.
EFS lacks SMB and iSCSI support, while FSx for Windows lacks iSCSI support.
3
Formulate the mounting strategy.
Configure NFS exports for Linux, SMB shares for Windows, and provision iSCSI LUNs for block storage access.
This provides the required multi-protocol connectivity to the shared storage.

Key Concept

Multi-protocol shared storage using Amazon FSx for NetApp ONTAP
Estimated Time:2m 0s
Question 791Question →

A healthcare provider is designing a data ingestion pipeline to collect real-time health metrics from 20,00020,000 patient monitoring devices. Each device transmits a 4 KB4\text{ KB} JSON payload every 10 seconds10\text{ seconds} to a public endpoint. The provider needs to ingest these payloads, transform them into Apache Parquet format, and save them to Amazon S3. The data must be queryable in Amazon Athena within 3 minutes3\text{ minutes} of generation. Additionally, the chronological order of records sent by each device must be maintained during ingestion to ensure clinical analysis accuracy. The architecture must scale dynamically to handle peak traffic with minimal management overhead. Which architecture meets these requirements?

Show answer & explanation

Answer: Use Amazon Kinesis Data Firehose (Amazon Data Firehose) to ingest the stream. Configure Firehose to perform inline format conversion to Apache Parquet using an AWS Glue Data Catalog schema, and deliver the files to Amazon S3 with a buffer interval of 120 seconds120\text{ seconds}.

Answer

The correct architecture uses Amazon Kinesis Data Firehose to ingest the data, performs format conversion to Apache Parquet using AWS Glue, and buffers the data for 120 seconds before writing to Amazon S3.
The ingestion architecture using Amazon Kinesis Data Firehose (Amazon Data Firehose) with AWS Glue inline format conversion is the most operationally efficient. Amazon Data Firehose is a fully managed, serverless service that automatically scales to handle incoming throughput without shard provisioning. It natively integrates with the AWS Glue Data Catalog to perform real-time format conversion from JSON to Apache Parquet before writing to Amazon S3. A buffer interval of 120 seconds120\text{ seconds} ensures that the files are delivered to S3 within the 3 minutes3\text{ minutes} requirement. Furthermore, Firehose preserves the ingestion order of incoming records.

Step-by-Step Solution

1
Calculate the required ingestion throughput of the pipeline.
20,000 devices×(1 payload/10 seconds)=2,000 records/second20,000\text{ devices} \times (1\text{ payload} / 10\text{ seconds}) = 2,000\text{ records/second}. Total throughput is 2,000 records/second×4 KB/record=8,000 KB/second=8 MB/s2,000\text{ records/second} \times 4\text{ KB/record} = 8,000\text{ KB/second} = 8\text{ MB/s}.
Determining the aggregate message rate and bandwidth is essential to select and size the ingestion service appropriately.
2
Evaluate the capability of Kinesis Data Streams shard configurations against the calculated throughput.
A single Kinesis Data Streams shard supports a maximum write capacity of 1 MB/s1\text{ MB/s} or 1,000 records/sec1,000\text{ records/sec}. Standard configurations with 22 or 44 shards only support 2 MB/s2\text{ MB/s} and 4 MB/s4\text{ MB/s} respectively, resulting in throttling under the 8 MB/s8\text{ MB/s} load.
To ensure data ingestion reliability, the service capacity must meet or exceed the peak ingestion throughput.
3
Assess the ordering and operational overhead constraints of the ingestion options.
Standard SQS queues do not guarantee message ordering, violating the chronological constraint. Using Amazon Data Firehose with inline AWS Glue format conversion provides automatic scaling up to the required throughput, preserves record order, and transforms JSON to Parquet natively without custom code or infrastructure management.
Selecting the service that satisfies both structural constraints (ordering) and operational principles (minimal overhead) leads to the optimal AWS architecture.

Key Concept

Serverless streaming ingestion and inline format conversion using Amazon Kinesis Data Firehose and AWS Glue.
Estimated Time:2m 0s
Question 792Question →

A company has a hybrid network architecture where their on-premises database must replicate data to multiple Amazon RDS instances deployed across three VPCs in the AWS Cloud. The replication workload requires a continuous, low-latency connection with a guaranteed throughput of 4 Gbps4\text{ Gbps}. The architecture must be highly available and resilient to single points of failure. Which TWO steps should the solutions architect take to design a high-performing and scalable network architecture that meets these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Provision two AWS Direct Connect connections at separate Direct Connect locations for redundancy and high performance.; Create an AWS Transit Gateway to interconnect the VPCs, and attach it to a Direct Connect gateway using a transit virtual interface (transit VIF).

Answer

Provision two AWS Direct Connect connections at separate Direct Connect locations, and create an AWS Transit Gateway to interconnect the VPCs, attaching it to a Direct Connect gateway using a transit virtual interface (transit VIF).
To support a continuous, high-performance database replication workload requiring 4 Gbps4\text{ Gbps} of low-latency throughput, AWS Direct Connect is the most suitable service. By provisioning two Direct Connect connections at separate physical locations, the architecture ensures high availability and eliminates single points of failure. AWS Transit Gateway simplifies the routing complexity of connecting multiple VPCs to the on-premises network. Attaching the Transit Gateway to a Direct Connect gateway via a transit virtual interface (transit VIF) allows all three VPCs to share the high-throughput Direct Connect connection, optimizing performance and scalability.

Step-by-Step Solution

1
Determine the throughput and latency requirements.
Database replication requires 4 Gbps4\text{ Gbps} of continuous, low-latency throughput, ruling out standard VPN connections which are limited to 1.25 Gbps1.25\text{ Gbps} per tunnel and run over the public internet.
Establishing a baseline for acceptable physical connectivity options.
2
Select the appropriate hybrid connectivity service.
Select AWS Direct Connect with two connections at separate physical locations for high availability.
Direct Connect provides dedicated, low-latency, high-performance bandwidth that guarantees the 4 Gbps4\text{ Gbps} throughput, while multiple locations prevent single points of failure.
3
Choose the routing architecture to scale across multiple VPCs.
Create an AWS Transit Gateway and connect it to the Direct Connect gateway using a transit VIF.
Transit Gateway simplifies the network configuration by acting as a hub for the three target VPCs, allowing them to share the Direct Connect lines without managing complex VPC peering meshes or multiple private virtual interfaces.

Key Concept

AWS Direct Connect combined with AWS Transit Gateway via a transit virtual interface provides a scalable, highly available, and high-performing hybrid network topology.
Estimated Time:2m 0s
Question 793Question →

A company's reporting application queries an Amazon RDS for PostgreSQL database. During business hours, the database experiences high CPU utilization and performance degradation due to a high volume of read-only queries. A solutions architect needs to design a solution to offload the read traffic and reduce database latency. Which solution will meet these requirements?

Show answer & explanation

Answer: Create Amazon RDS Read Replicas and configure the application to route read queries to the read replica endpoints.

Answer

Create Amazon RDS Read Replicas and configure the application to route read queries to the read replica endpoints.
The correct answer is to create Amazon RDS Read Replicas and route read-only queries to them. This directly offloads the read traffic from the primary database instance, reducing CPU utilization and query latency.

Step-by-Step Solution

1
Analyze the database workload requirements.
The application suffers from performance issues caused specifically by read-only queries.
Identifying the bottleneck as read-heavy allows for targeting the read path rather than writing or compute resources.
2
Evaluate high-performing database options for offloading read traffic in relational databases.
Amazon RDS Read Replicas provide read-only copies of the primary database instance that can handle read queries.
Read replicas are designed specifically to scale read capacity horizontally and reduce latency for read-heavy workloads.
3
Select the option that configures the application to point read traffic to these new endpoints.
Configuring the application to route read queries to the read replica endpoints offloads the primary database.
This configuration directly addresses the performance degradation of the primary instance during peak hours.

Key Concept

Scaling read capacity using Amazon RDS Read Replicas
Question 794Question →

A security and analytics company collects telemetry data and daily aggregated summaries from IoT sensors.

The data characteristics are as follows:

* Telemetry Reports: Average file size of 80 KB80\text{ KB}, with 10 million10\text{ million} new files generated daily. The files are frequently analyzed for 10 days10\text{ days}, after which they are rarely accessed. They must be deleted after 90 days90\text{ days}.
* Aggregated Summaries: Average file size of 5 MB5\text{ MB}, with 10,000 new files10,000\text{ new files} generated daily. They are frequently accessed for 45 days45\text{ days}. Between day 4646 and day 9090, they are accessed occasionally (one or two times per month). After day 9090, they are rarely accessed but must be retained indefinitely for long-term audit purposes, with a recovery time of up to 12 hours12\text{ hours} acceptable.

Which two S3 Lifecycle configurations will provide the most cost-effective solution for this data? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: For the Telemetry Reports, create a lifecycle rule to expire the objects after 90 days without transitioning them to any other storage class.; For the Aggregated Summaries, create a lifecycle rule to transition the objects to Amazon S3 Standard-Infrequent Access (S3 Standard-IA) after 45 days, and to Amazon S3 Glacier Deep Archive after 90 days.

Answer

The most cost-effective approach is to retain the Telemetry Reports in S3 Standard for the entire 90 days without transitioning them, and then expire them, while transitioning the Aggregated Summaries to S3 Standard-IA at day 45 and then to S3 Glacier Deep Archive at day 90.
For the Telemetry Reports, keeping the objects in S3 Standard for their entire 90-day lifetime is the most cost-effective option because the 100transitionfeefor10millionobjectssignificantlyexceedsthe100 transition fee for 10 million objects significantly exceeds the 55.20 total storage cost in S3 Standard, and their 80 KB size would trigger a 128 KB billing minimum in S3 Standard-IA. For the Aggregated Summaries, transitioning to S3 Standard-IA after 45 days and then to S3 Glacier Deep Archive after 90 days is ideal because the summaries are large (5 MB) and low-volume (10,000 files daily), making transition costs negligible. It also satisfies the 30-day minimum storage duration for S3 Standard-IA before transitioning to Deep Archive, which allows up to 12-hour retrieval times.

Step-by-Step Solution

1
Analyze the size and count of the Telemetry Reports to evaluate S3 Standard-IA suitability.
Transitioning 10 million objects to S3 Standard-IA costs 100 USD in transition fees. Because the reports are 80 KB, they are under the 128 KB minimum billable size, incurring a storage penalty. The storage of 800 GB in S3 Standard for the full 90 days costs only 55.20 USD. Thus, transitioning is mathematically counterproductive.
To determine whether transitioning small objects is cost-effective by comparing transition costs and storage overhead against potential savings.
2
Analyze S3 Intelligent-Tiering behavior for Telemetry Reports.
Objects under 128 KB are not auto-tiered in S3 Intelligent-Tiering and are billed at S3 Standard rates. Transitioning them incurs lifecycle transition fees without providing any storage tiering benefits.
To rule out S3 Intelligent-Tiering for the small reports due to the 128 KB minimum monitoring limitation.
3
Evaluate S3 Standard-IA and S3 Glacier Deep Archive lifecycle paths for Aggregated Summaries.
The summaries are 5 MB (larger than 128 KB) and have a volume of only 10,000 files daily, resulting in low transition costs (0.10 USD to Standard-IA and 0.50 USD to Glacier Deep Archive). Moving them to S3 Standard-IA after 45 days aligns with the occasional access requirement and satisfies the 30-day S3 Standard-IA minimum storage duration rule before they transition to S3 Glacier Deep Archive at day 90.
To optimize storage for larger files with predictable, decreasing access patterns while satisfying minimum storage duration constraints.
4
Examine the S3 Glacier Flexible Retrieval minimum storage duration constraint.
Glacier Flexible Retrieval has a 90-day minimum storage duration. Transitioning to Glacier Flexible Retrieval at day 45 and then to Glacier Deep Archive at day 90 results in only 45 days in Glacier Flexible Retrieval, triggering early deletion/transition charges.
To avoid early transition fees on Glacier storage tiers.

Key Concept

S3 Lifecycle policies must account for object size thresholds, transition request fees, and minimum storage durations to avoid penalties and negative cost savings.
Question 795Question →

A genomics research organization is deploying a pipeline on AWS to perform real-time DNA sequence alignment. The workload requires a cluster of compute-intensive EC2 instances that need to exchange large datasets continuously with sub-millisecond node-to-node latency. A fleet of worker instances is managed by an Auto Scaling group behind an Application Load Balancer (ALB). The containerized alignment application runs on the instances and is configured to bind to custom TCP port 8443, while the ALB exposes HTTPS port 443 to external clients. The system must maximize node-to-node network performance and ensure the Auto Scaling group scales correctly without falsely terminating healthy instances. Which combination of actions should a solutions architect perform to satisfy these requirements? (Select TWO.)

Select all that apply

Show answer & explanation

Answer: Launch the Auto Scaling group instances in a cluster placement group within a single Availability Zone.; Configure the Application Load Balancer target group health check port to 8443.

Answer

Launch the Auto Scaling group instances in a cluster placement group, and configure the Application Load Balancer target group health check port to 8443.
To meet the requirements, the solutions architect must configure a cluster placement group and specify the correct custom health check port. A cluster placement group is designed for applications that require low network latency, high network throughput, or both, by placing instances close together within a single Availability Zone. Additionally, because the application listens on custom TCP port 8443, the target group's health checks must be explicitly pointed to port 8443. If the health checks query the default port, the ALB will fail to receive a response, mistakenly marking the healthy instances as unhealthy, which triggers unnecessary instance termination.

Step-by-Step Solution

1
Analyze the node-to-node communication latency requirements.
The workload requires sub-millisecond node-to-node latency, which points to a cluster placement group because it groups instances physically close together on the same underlying hardware within a single Availability Zone.
Selecting the correct placement group type ensures maximum network throughput and low latency for tightly coupled cluster nodes.
2
Evaluate high-availability placement groups vs. cluster placement groups.
Identify that spread and partition placement groups are designed for partition isolation and fault tolerance, which increases latency and fails the low-latency requirement.
Ensures that high-performance compute requirements dictate the design choice over other placement group configurations.
3
Determine the application binding port and the load balancer health check configuration.
The application listens on custom port 8443. The ALB target group health check must be explicitly pointed to port 8443.
Mismatched health check ports (e.g., using default port 80 when the application runs on 8443) lead to false-positive unhealthy status reports and accidental instance termination by Auto Scaling.

Key Concept

High-performance compute clustering requires cluster placement groups for low-latency node-to-node networking, combined with precise health check configurations matching the application's actual port binding.
Estimated Time:3m 0s
Question 796Question →

A solutions architect is deploying a high-performance distributed application on Amazon EC2 instances in a private subnet. The application nodes require low-latency node-to-node network communication. An Application Load Balancer (ALB) routes traffic to the application, which listens on custom port 8080.

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

Select all that apply

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group.; Configure the ALB target group health check to query port 8080.

Answer

Launch the EC2 instances in a cluster placement group, and configure the ALB target group health check to query port 8080.
To satisfy the low-latency network requirement, launching the instances in a cluster placement group is necessary because it places instances physically close together on the same underlying hardware. Additionally, because the application is listening on port 8080, configuring the ALB target group health checks to query port 8080 ensures that the health checks succeed and the load balancer can correctly route traffic.

Step-by-Step Solution

1
Identify network performance requirement.
Low-latency node-to-node network communication requires grouping the instances in a single Availability Zone close to each other, which is achieved using a cluster placement group.
Other placement groups like spread or partition focus on high availability and fault isolation rather than maximum network performance.
2
Identify health check port configuration.
Since the application listens on custom port 8080, configure the health check port to explicitly query 8080.
Leaving the target group health check on default port 80 would fail to establish a connection with the application, falsely marking all instances as unhealthy.

Key Concept

Selecting cluster placement groups for low-latency compute workloads, and matching target group health check ports with application listening ports.
Question 797Question →

A media publishing platform hosts its static assets in an Amazon S3 bucket and distributes them globally via an Amazon CloudFront distribution. During a high-traffic breaking news event, the Amazon S3 bucket experiences a massive surge in read requests, leading to performance degradation and high costs. An investigation reveals that a developer set the CloudFront cache behavior TTL values to 0 to ensure that readers always receive the most up-to-date images and CSS files. Which solution should a solutions architect implement to resolve the S3 performance issue while ensuring users still receive updated assets immediately?

Show answer & explanation

Answer: Configure the CloudFront cache behavior to use a Cache Policy with a long Default TTL (such as 30 days), and update the web application to use a file versioning naming convention (such as appending a unique hash to the asset filenames) whenever an asset is updated.

Answer

Configure the CloudFront cache behavior to use a Cache Policy with a long Default TTL and implement file versioning (cache-busting) in the web application for updated assets.
The correct solution uses a combination of long-term CloudFront caching (long TTL Cache Policy) and web application file versioning (cache-busting). By renaming updated assets (e.g., `image_v2.png`), the application changes the request URI. CloudFront treats the new URI as a cache miss, fetches the new version from S3, caches it, and serves subsequent requests from the edge. This guarantees that users receive updates instantly while keeping the cache hit ratio high and S3 read requests minimal.

Step-by-Step Solution

1
Analyze the current issue.
The S3 bucket is overloaded because CloudFront caching is bypassed (TTL = 0), sending all client requests directly to S3.
Understanding the root cause allows us to target the solution to both enable caching and satisfy the requirement for immediate updates.
2
Select the correct caching strategy.
Enable caching by setting a positive TTL (e.g., 30 days) on CloudFront.
Caching static files on the edge protects the S3 origin from traffic spikes and reduces network latency and data transfer costs.
3
Address the requirement for immediate asset updates.
Implement file versioning or cache-busting (e.g., adding a hash or version to filenames) in the web application's asset references.
When a file is modified, the new path forces CloudFront to retrieve and cache the new version instantly, while unchanged assets continue to be served from the edge cache.

Key Concept

CloudFront Caching and File Versioning (Cache-Busting)
Estimated Time:2m 0s
Question 798Question →

A company is designing a 33-year cost-optimization strategy for a web application deployed on AWS. The application consists of the following components:
- A containerized API hosted on Amazon ECS with AWS Fargate that requires a steady-state baseline of 4 vCPUs4\text{ vCPUs} and 8 GB8\text{ GB} of RAM. During peak hours, the API dynamically scales up to 16 vCPUs16\text{ vCPUs} and 32 GB32\text{ GB} of RAM.
- A batch processing job that runs once daily for 3 hours3\text{ hours}. The job is fault-tolerant, can be interrupted without loss of progress, and requires a minimum of 8 vCPUs8\text{ vCPUs}.
- An Amazon DynamoDB table that stores user sessions. The table experiences highly unpredictable, spiky traffic patterns with long periods of inactive idle time.

Which combination of compute purchasing models and database capacity settings will provide the most cost-effective architecture?

Show answer & explanation

Answer: Purchase a 33-year Compute Savings Plan to cover the baseline Fargate capacity. Use Fargate Spot for the scaling tasks. Run the daily batch processing job on Amazon EC2 Spot Instances, and configure the DynamoDB table to use On-Demand capacity mode.

Answer

The correct answer is the option that purchases a 33-year Compute Savings Plan for the baseline Fargate capacity, uses Fargate Spot for scaling Fargate tasks, runs the daily batch processing job on Amazon EC2 Spot Instances, and configures the DynamoDB table to use On-Demand capacity mode.
The correct answer is correct because a 33-year Compute Savings Plan provides the highest discount for the steady-state baseline Fargate tasks, which run continuously. Using Fargate Spot for the scaling API capacity allows the application to scale cost-effectively. Running the 33-hour fault-tolerant batch job on Amazon EC2 Spot Instances leverages the lowest-cost compute option for interruptible workloads, while avoiding the 15-minute15\text{-minute} execution limit of AWS Lambda. Finally, configuring Amazon DynamoDB in On-Demand capacity mode is optimal for spiky, unpredictable traffic with long idle periods, as it avoids paying for unused provisioned capacity.

Step-by-Step Solution

1
Analyze the baseline compute requirement for the user-facing API.
The baseline ECS Fargate capacity runs continuously (24/724/7) and is best covered by a 33-year Compute Savings Plan, which offers significant discounts for Fargate workloads.
Steady-state workloads running continuously benefit most from long-term commitments like Savings Plans.
2
Analyze the scaling compute requirement for the user-facing API.
The scaling Fargate capacity is transient and dynamic, making Fargate Spot the most cost-effective option to handle scale-out events.
Using Spot capacity for non-critical, scalable compute tiers minimizes On-Demand costs.
3
Analyze the batch processing job requirement.
Since the job is fault-tolerant, runs for 3 hours3\text{ hours}, and can tolerate interruptions, Amazon EC2 Spot Instances should be selected.
AWS Lambda cannot be used because its maximum execution timeout is 15 minutes15\text{ minutes}, and Spot Instances provide up to a 90%90\% discount compared to On-Demand compute.
4
Analyze the database capacity requirement.
The DynamoDB table experiences highly unpredictable, spiky traffic with long idle periods, which aligns with On-Demand capacity mode.
On-Demand capacity mode charges per request and scales to zero during idle periods, avoiding the cost of over-provisioned capacity.

Key Concept

Cost-optimizing AWS compute requires matching workloads to appropriate purchasing models: Compute Savings Plans for steady-state Fargate/EC2 baseline compute, Spot capacity (Fargate Spot or EC2 Spot) for interruptible and scaling components, and On-Demand database capacity modes for spiky, unpredictable access patterns.
Estimated Time:2m 30s
Question 799Question →

A financial technology company runs a real-time ledger application that processes high-frequency ledger entries. The transaction volume peaks at 25,00025,000 write operations per second. The application uses an Amazon DynamoDB table with a partition key of `AccountID` (UUID) and a sort key of `TransactionTimestamp`. During peak trading hours, transactions for a few high-volume institutional accounts are throttled with `ProvisionedThroughputExceededException` errors, even though the table's total read and write capacities are scaled using on-demand mode. The application requires consistent sub-second write response times and must retrieve transactions for any account sorted chronologically. Which database optimization strategy should a solutions architect recommend to resolve the throttling issue while meeting the performance requirements?

Show answer & explanation

Answer: Implement write sharding by appending a random integer suffix from 00 to 99 to the `AccountID` during writes, and query all sharded partitions in parallel using the application layer to merge the results.

Answer

Implement write sharding by appending a random integer suffix to the partition key, and query the sharded partitions in parallel to merge the results.
The correct strategy is to distribute the write load across multiple physical partitions using write sharding. DynamoDB limits any single partition key to a maximum of 1,0001,000 write capacity units (WCUs) per second. When a few accounts generate massive transaction volumes, their writes are throttled despite the table having on-demand capacity enabled. By appending a random integer suffix (such as 00 to 99) to the `AccountID`, the write operations for a single account are spread across 1010 physical partitions, elevating the limit to 10,00010,000 WCUs. The application can query all 1010 sharded partitions in parallel and merge the sorted streams, fulfilling the chronological query requirements with sub-second response times.

Step-by-Step Solution

1
Diagnose the partition bottleneck.
Identify that a single DynamoDB partition has a hard limit of 1,0001,000 write capacity units (WCUs) per second, which cannot be bypassed by table-level on-demand scaling if traffic is concentrated on a single key.
Understanding the physical constraints of DynamoDB partitions is necessary to address localized throttling.
2
Evaluate DynamoDB Accelerator (DAX) behavior for write operations.
Confirm that DAX is a write-through cache, meaning writes are committed to the base table synchronously and still count against the 1,0001,000 WCU partition limit.
Rule out caching as a write-throughput scaling solution.
3
Evaluate Global Secondary Index (GSI) capabilities.
Confirm that DynamoDB does not support direct writes to GSIs and that partitioning by a monotonically increasing timestamp creates a severe hot partition.
Rule out secondary index writes and verify database access patterns.
4
Evaluate Global Tables capabilities.
Determine that cross-region replication replicates writes asynchronously but does not increase the physical write limit of a single partition key inside any region.
Rule out regional replication as a mechanism for scaling single-key writes.
5
Design and verify a write sharding strategy.
By appending a random suffix (00 to 99), writes for a single account are distributed across up to 1010 partitions, increasing the single-account write limit to 10,00010,000 WCUs. Querying these partitions in parallel allows the application to merge results chronologically.
Select the correct pattern that distributes write load while preserving query capability.

Key Concept

Mitigating hot partition keys in DynamoDB using write sharding to scale beyond the single-partition write limit.
Question 800Question →

An advertising technology platform generates temporary campaign performance report files that average 45 MB45\text{ MB} in size. These reports are queried frequently by analytics tools for exactly 10 days10\text{ days} after creation, after which they are no longer needed and must be deleted. A solutions architect needs to design a cost-optimized storage strategy for these files. Which strategy will meet these requirements at the lowest cost?

Show answer & explanation

Answer: Store the files in Amazon S3 Standard and use an S3 Lifecycle policy to delete the objects after 10 days10\text{ days}.

Answer

Store the files in Amazon S3 Standard and use an S3 Lifecycle policy to delete the objects after 10 days10\text{ days}.
The correct option is to store the files in Amazon S3 Standard and delete them after 10 days10\text{ days} using an S3 Lifecycle policy. Since the files are deleted after 10 days10\text{ days}, they do not meet the minimum storage duration requirements of S3 Standard-IA (30 days30\text{ days}) or S3 Glacier Flexible Retrieval (90 days90\text{ days}). Deleting them early from these tiers results in paying for the remaining storage duration as a penalty, making them more expensive than S3 Standard. Amazon S3 Intelligent-Tiering is also not cost-effective because it incurs a monitoring fee per object and the files are deleted before they can transition to lower-cost tiers.

Step-by-Step Solution

1
Analyze the retention requirement of the files.
The files are only kept for 10 days10\text{ days} before being deleted.
Determines the total duration that storage charges will apply.
2
Evaluate the minimum storage duration rules of Amazon S3 storage classes.
S3 Standard-IA has a minimum of 30 days30\text{ days}, and S3 Glacier Flexible Retrieval has a minimum of 90 days90\text{ days}.
Helps identify if any storage classes will charge a penalty for early deletion.
3
Determine the most cost-effective storage class with no early deletion penalties.
S3 Standard has no minimum storage duration or deletion penalties, making it the most cost-effective option for files stored for only 10 days10\text{ days}.
Ensures the selected option achieves the lowest overall cost.

Key Concept

Identifying minimum storage duration requirements for S3 storage classes to avoid early deletion penalties.
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