All practice questions

1462 questions

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A solutions architect is designing the database tier for an IoT fleet management application. The application receives real-time telemetry from $90,000$ vehicles. The data volume exhibits sudden, highly irregular spikes when vehicles enter high-connectivity zones, ranging from $1,000$ writes per second up to a peak of $50,000$ writes per second. The application requires sub-millisecond read latency for live dashboard queries, while write operations must have single-digit millisecond latency. The primary read query retrieves the latest status events for a specific vehicle. Which database architecture meets these requirements with the highest performance and scalability?

Create an Amazon DynamoDB table using a rounded timestamp as the partition key and vehicle_id as the sort key. Configure the table with On-Demand capacity mode, and deploy a DynamoDB Accelerator (DAX) cluster to cache read queries.

Create an Amazon DynamoDB table using vehicle_id as the partition key and timestamp as the sort key. Configure the table to use Provisioned Capacity Mode set to the peak of

50,000

write capacity units (WCUs) to guarantee write performance during spikes, and cache reads using an Amazon ElastiCache for Memcached cluster.

Create an Amazon DynamoDB table using vehicle_id as the partition key and timestamp as the sort key. Configure the table with On-Demand capacity mode, and deploy a DynamoDB Accelerator (DAX) cluster to handle read queries.

Deploy an Amazon RDS for PostgreSQL database in a Multi-AZ deployment. Set up RDS Read Replicas to handle the read queries, and configure the application to target the Read Replicas as the primary disaster recovery failover mechanism to maintain high performance.

Show answer & explanation

Answer: Create an Amazon DynamoDB table using vehicle_id as the partition key and timestamp as the sort key. Configure the table with On-Demand capacity mode, and deploy a DynamoDB Accelerator (DAX) cluster to handle read queries.

Answer

Create an Amazon DynamoDB table using vehicle_id as the partition key and timestamp as the sort key, configured in On-Demand capacity mode, with a DynamoDB Accelerator (DAX) cluster for caching reads.

The correct option provides the most performant and scalable solution. Partitioning by vehicle_id ensures a uniform distribution of write operations across physical partitions, avoiding the hot key issue. On-Demand capacity mode scales dynamically to handle sudden write spikes up to the peak of

50,000

writes per second without requiring manual provisioning or incurring idle costs. DynamoDB Accelerator (DAX) delivers the required sub-millisecond read latency by caching queries directly in-memory.

Step-by-Step Solution

Evaluate the database scalability and performance requirements.

Identify that the system requires up to

50,000

writes/sec with single-digit millisecond latency, and sub-millisecond read latency. Traditional relational databases like RDS are ruled out because scaling writes to this level under spiky workloads is complex and expensive.

Establishing database type boundaries ensures a high-performing and cost-effective fit.

Determine the optimal partition key design for the DynamoDB table.

Select vehicle_id as the partition key to distribute writes evenly across DynamoDB partitions.

Avoiding monotonically increasing keys (such as timestamps) prevents hot partition bottlenecks.

Select the capacity mode and caching solution.

Choose On-Demand capacity mode to automatically handle unpredictable write spikes, and implement DynamoDB Accelerator (DAX) to achieve sub-millisecond read latencies.

On-Demand mode prevents throttling and over-provisioning costs, while DAX provides microsecond read performance.

Key Concept

High-performing database scaling using partition key design, capacity mode selection, and caching integration on AWS.

Estimated Time:2m 0s

Question 942Question →

An e-commerce company hosts its product catalog image library on an Amazon S3 bucket in the us-east-1 Region. These images must be delivered globally with the lowest possible latency. To ensure reliability, the company requires that if the primary S3 bucket in us-east-1 becomes unavailable or returns server errors, the architecture must automatically serve images from a replicated S3 bucket in the us-west-2 Region. The solution must minimize operational complexity.

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

Select all that apply

Create an Amazon CloudFront distribution and define an origin group containing the primary S3 bucket as the primary origin and the secondary S3 bucket as the secondary origin.

Enable origin failover for the CloudFront origin group and configure it to fail over to the secondary origin on specific HTTP status codes such as 500, 502, 503, and 504.

Configure an Amazon Route 53 latency routing policy with health checks to route user requests directly to the S3 bucket website endpoints.

Deploy AWS WAF to monitor the health of the primary S3 bucket and trigger an AWS Lambda function to update the CloudFront origin if an outage is detected.

Configure the CloudFront cache behavior with a Time to Live (TTL) of zero to force CloudFront to validate content availability with the primary bucket on every request.

Show answer & explanation

Answer: Create an Amazon CloudFront distribution and define an origin group containing the primary S3 bucket as the primary origin and the secondary S3 bucket as the secondary origin.; Enable origin failover for the CloudFront origin group and configure it to fail over to the secondary origin on specific HTTP status codes such as 500, 502, 503, and 504.

Answer

To meet the requirements, the solutions architect should create an Amazon CloudFront distribution with an origin group containing the primary S3 bucket as the primary origin and the secondary S3 bucket as the secondary origin. Additionally, they should enable origin failover in the origin group and configure it to fail over to the secondary origin on status codes such as 500, 502, 503, and 504.

The correct architecture uses Amazon CloudFront for low-latency edge caching of static images, coupled with CloudFront Origin Groups for high availability. By creating an origin group containing the primary S3 bucket and the secondary S3 bucket, and setting up failover on server error status codes (500, 502, 503, 504), CloudFront will automatically fall back to serving content from the secondary bucket in the event of an outage or error with the primary bucket. This is a native feature that minimizes operational complexity.

Step-by-Step Solution

Configure content delivery to minimize latency.

Amazon CloudFront is deployed to cache the product catalog images at edge locations globally.

CloudFront caches static files close to users, fulfilling the requirement for lowest possible global latency.

Establish the secondary S3 bucket as a fallback origin.

An origin group is configured in CloudFront with the primary S3 bucket in us-east-1 as the primary origin and the replicated S3 bucket in us-west-2 as the secondary origin.

This establishes the logical mapping required for automatic origin-level failover.

Configure the failover threshold criteria.

Origin failover is enabled on the origin group, targeting HTTP status codes 500, 502, 503, and 504.

When CloudFront receives these server errors from the primary S3 bucket, it automatically routes the request to the secondary bucket, achieving high availability with zero manual intervention or custom code.

Key Concept

CloudFront Origin Groups and Origin Failover for static S3 assets

Question 943Question →

A healthcare provider stores two types of data in a single Amazon S3 bucket:

* Clinical Imaging Data (DICOM): The average file size is $20\text{ MB}$ . These files are accessed frequently by clinicians for the first $40$ days. After $40$ days, access drops significantly, but images must be retrievable within $4$ hours for follow-up appointments. The images must be retained for $7$ years ( $2555$ days).
* System Access Logs: The average file size is $10\text{ KB}$ . These logs are analyzed daily for security audits during the first $15$ days. After $15$ days, they are rarely accessed, but must be retained for $1$ year ( $365$ days) for regulatory compliance.

Which two lifecycle configurations will meet these requirements in the most cost-effective manner? (Select TWO.)

Select all that apply

Configure a lifecycle rule for the Clinical Imaging Data to transition objects from S3 Standard to S3 Glacier Flexible Retrieval after

40

days, and expire them after

2555

days.

Keep the System Access Logs in S3 Standard, and configure a lifecycle rule to expire the objects after

365

days.

Configure a lifecycle rule for the Clinical Imaging Data to transition objects from S3 Standard to S3 Standard-IA after

40

days, transition them to S3 Glacier Flexible Retrieval after

60

days, and expire them after

2555

days.

Configure a lifecycle rule for the System Access Logs to transition objects from S3 Standard to S3 Standard-IA after

15

days, and expire them after

365

days.

Configure a lifecycle rule for the System Access Logs to transition objects from S3 Standard to S3 Glacier Flexible Retrieval after

15

days, and expire them after

365

days.

Show answer & explanation

Answer: Configure a lifecycle rule for the Clinical Imaging Data to transition objects from S3 Standard to S3 Glacier Flexible Retrieval after $40$ days, and expire them after $2555$ days.; Keep the System Access Logs in S3 Standard, and configure a lifecycle rule to expire the objects after $365$ days.

Answer

Configure a lifecycle rule for the Clinical Imaging Data to transition objects from S3 Standard to S3 Glacier Flexible Retrieval after 40 days and expire them after 2555 days, and keep the System Access Logs in S3 Standard, configuring a lifecycle rule to expire the objects after 365 days.

Transitioning the Clinical Imaging Data directly from S3 Standard to S3 Glacier Flexible Retrieval after 40 days is highly cost-effective because the average file size of 20 MB is well above the 128 KB minimum storage threshold, and standard retrieval (which takes 3-5 hours) satisfies the 4-hour retrieval requirement. Keeping the System Access Logs in S3 Standard and expiring them after 365 days is correct because the average file size is 10 KB, which is below the 128 KB minimum size threshold for S3 Standard-IA and S3 Glacier. Transitioning these small files to a lower-cost storage class would incur a storage charge penalty for 128 KB per object, making it significantly more expensive than keeping them in S3 Standard.

Step-by-Step Solution

Analyze the file sizes and access patterns for both datasets.

Clinical Imaging Data files are large (20 MB) and accessed frequently for 40 days, then rarely accessed but must be retrievable within 4 hours. System Access Logs are very small (10 KB) and accessed frequently for 15 days, then rarely accessed.

Understanding file sizes and access requirements is critical for identifying potential minimum size penalties and retrieval time constraints.

Evaluate storage tier options for the Clinical Imaging Data.

Transitioning directly to S3 Glacier Flexible Retrieval after 40 days is optimal because the 20 MB files exceed the 128 KB minimum size threshold, and the 3-5 hour retrieval window meets the 4-hour requirement.

Bypassing S3 Standard-IA prevents paying transition and storage fees for an intermediate class when Glacier already meets the retrieval SLA.

Evaluate storage tier options for the System Access Logs.

Transitioning to S3 Standard-IA or S3 Glacier is cost-ineffective due to the 128 KB minimum storage billing limit on those tiers. The most cost-optimized strategy is to retain them in S3 Standard and expire them after 365 days.

Transitioning 10 KB files to a tier with a 128 KB minimum threshold results in paying for 12.8 times the actual storage consumed, violating cost optimization principles.

Key Concept

Understanding S3 lifecycle transitions, minimum storage durations, and minimum object size billing thresholds to design cost-effective storage strategies.

Question 944Question →

A company hosts a global media application that distributes static images from an Amazon S3 bucket. To minimize latency and protect the application from origin server failures, a solutions architect is implementing an Amazon CloudFront distribution. The architecture must automatically redirect traffic to a backup S3 bucket in a different AWS Region if the primary S3 bucket returns an HTTP 502 or 504 error. Which combination of actions should the solutions architect perform to meet these resiliency requirements? (Select TWO.)

Select all that apply

Create a CloudFront origin group containing both the primary and backup S3 buckets as origins, designating the primary bucket as primary.

Configure the CloudFront distribution's cache behavior to direct requests to the newly created origin group.

Configure an Amazon Route 53 failover routing policy that points to the primary and backup S3 bucket endpoints, and set the CloudFront origin to this Route 53 record.

Deploy an AWS WAF web ACL associated with the CloudFront distribution, and configure a custom rule to intercept 5xx errors and redirect requests to the backup S3 bucket.

Set the Minimum TTL, Default TTL, and Maximum TTL to 0 in the cache behavior settings to force CloudFront to query both S3 buckets simultaneously for each request.

Show answer & explanation

Answer: Create a CloudFront origin group containing both the primary and backup S3 buckets as origins, designating the primary bucket as primary.; Configure the CloudFront distribution's cache behavior to direct requests to the newly created origin group.

Answer

Create a CloudFront origin group with the primary and backup S3 buckets, and configure the cache behavior to point to this origin group.

To set up automatic failover for static content distributed via CloudFront, you must create an origin group that contains a primary origin (the primary S3 bucket) and a secondary origin (the backup S3 bucket). You specify the failover criteria (e.g., HTTP 502 and 504 status codes) that trigger the failover. Finally, you configure the cache behavior to target the origin group so that requests are routed through this failover logic.

Step-by-Step Solution

Define origins in the CloudFront distribution

Two distinct origins are created within the CloudFront configuration: one pointing to the primary S3 bucket and another pointing to the backup S3 bucket.

Before an origin group can be configured, the individual origin endpoints must exist in the distribution.

Create and configure an origin group

An origin group is established with the primary S3 bucket set as the primary origin and the backup S3 bucket set as the failover origin. The failover criteria are configured to trigger on HTTP 502 and 504 status codes.

This establishes the automatic failover relationship and defines which HTTP status codes from the primary origin will trigger a redirection to the backup origin.

Associate the origin group with the cache behavior

The cache behavior for the static images is updated to target the origin group instead of a single origin.

This ensures that incoming user requests are processed by the origin group, enabling the failover mechanism when errors occur.

Key Concept

CloudFront Origin Groups and Origin Failover

Question 945Question →

A company is experiencing unexpected cost increases across several AWS accounts. The finance team wants to implement a cost-governance strategy to receive alerts when monthly spending is projected to exceed their defined budget. Additionally, they want to automatically detect and receive notifications about sudden, unexpected cost spikes as close to real-time as possible. Which of the following actions should the Solutions Architect recommend to meet these requirements? (Select TWO)

Select all that apply

Create a custom budget in AWS Budgets, and configure an alert threshold based on forecasted costs to notify the team when projected spending exceeds the budget.

Set up a daily scheduled report in AWS Cost Explorer to run every morning and trigger an Amazon SNS notification if the report indicates that the monthly budget has been exceeded.

Configure AWS Cost Anomaly Detection with a cost monitor and subscribe to alerts using Amazon SNS to receive notifications when sudden, unexpected cost spikes occur.

Enable AWS Billing Conductor to monitor real-time cost spikes at the account level and trigger automated Service Control Policies (SCPs) to terminate resources when costs exceed a threshold.

Create a CloudWatch billing alarm monitoring the EstimatedCharges metric with a 5-minute resolution to receive near real-time alerts on cost anomalies.

Show answer & explanation

Answer: Create a custom budget in AWS Budgets, and configure an alert threshold based on forecasted costs to notify the team when projected spending exceeds the budget.; Configure AWS Cost Anomaly Detection with a cost monitor and subscribe to alerts using Amazon SNS to receive notifications when sudden, unexpected cost spikes occur.

Answer

Creating a custom budget in AWS Budgets based on forecasted costs, and configuring AWS Cost Anomaly Detection with a cost monitor and alert subscription.

The correct combination involves configuring AWS Budgets to alert on forecasted cost overruns, which provides a proactive warning before the budget is exceeded, and setting up AWS Cost Anomaly Detection with a monitor and subscription to receive notifications of sudden cost spikes. These tools are built specifically for proactive, automated cost control and anomaly notification.

Step-by-Step Solution

Analyze the requirements for proactive cost monitoring and cost anomaly detection.

Identify that the system needs to alert on future budget overruns (forecasting) and sudden cost spikes (anomalies).

This establishes the functional requirements for selecting the appropriate AWS cost management tools.

Evaluate AWS Budgets for forecast-based alerting.

AWS Budgets can monitor costs against a threshold and send alerts based on forecasted spending, which meets the requirement of preventing budget overruns proactively.

Forecasted alerts allow organizations to take corrective action before a budget limit is actually breached.

Evaluate AWS Cost Anomaly Detection for sudden cost spikes.

AWS Cost Anomaly Detection uses machine learning to identify unexpected cost spikes and sends alerts via SNS, meeting the requirement for detecting anomalies.

Cost Explorer is retrospective and not designed for automated, near real-time anomaly alerting.

Key Concept

AWS Budgets and AWS Cost Anomaly Detection are designed for proactive cost alerting, whereas AWS Cost Explorer is intended for retrospective cost analysis and visualization.

Question 946Question →

A company hosts a web-based educational portal on Amazon EC2 instances behind an Application Load Balancer (ALB) in the ap-northeast-1 Region. Users in South America are experiencing high latency and packet loss when accessing the portal over the public internet during peak hours. The solutions architect must implement a network architecture that minimizes latency, avoids public internet routing fluctuations, and provides a stable connection. Which solution should the solutions architect implement to meet these requirements?

Deploy AWS Global Accelerator and register the Application Load Balancer as the endpoint. Direct the users to access the portal via the accelerator's static IP addresses.

Configure Amazon Route 53 with a latency-based routing policy to route the South American user traffic directly to the Application Load Balancer's DNS name.

Establish an AWS Site-to-Site VPN connection from the users' local networks to the VPC to bypass the public internet and automatically scale throughput.

Configure stateless Network ACLs on the public subnets to allow direct traffic to the EC2 instances, bypassing the Application Load Balancer to reduce network hops.

Show answer & explanation

Answer: Deploy AWS Global Accelerator and register the Application Load Balancer as the endpoint. Direct the users to access the portal via the accelerator's static IP addresses.

Answer

Deploy AWS Global Accelerator and register the Application Load Balancer as the endpoint. Direct the users to access the portal via the accelerator's static IP addresses.

AWS Global Accelerator utilizes the AWS global network to route traffic from the edge location closest to the user directly to the Application Load Balancer. This avoids the public internet congestion, reduces latency, and decreases packet loss.

Step-by-Step Solution

Analyze the user location and the host region to determine the source of latency.

Traffic from South America to the ap-northeast-1 Region (Japan) travels over multiple public internet service providers (ISPs), exposing it to routing fluctuations and packet loss.

Understanding the path of the network packets helps identify why public internet routing is suboptimal.

Evaluate AWS services designed to optimize global client-to-application traffic.

AWS Global Accelerator is selected because it ingresses traffic at the edge location closest to the user and routes it over the private AWS global network.

Using the AWS global network avoids the public internet and minimizes hops, jitter, and latency.

Associate the existing Application Load Balancer with the AWS Global Accelerator endpoint.

Traffic is routed efficiently from the global edge locations to the regional ALB.

This maintains the load balancing architecture while providing a high-performance network path.

Key Concept

AWS Global Accelerator optimizes network path performance for global users by routing traffic over the dedicated AWS global network instead of the public internet.

Question 947Question →

A company is deploying a distributed graph database cluster on Amazon EC2. The database nodes require maximum throughput and the lowest possible latency for node-to-node synchronization. Additionally, the cluster must be fronted by a Network Load Balancer (NLB) listening on custom port 9900 to route client queries to the database nodes. Which combination of steps should the solutions architect take to meet these requirements? (Select TWO.)

Select all that apply

Launch the EC2 instances within a cluster placement group.

Configure the target group of the Network Load Balancer to perform health checks on port 9900.

Launch the EC2 instances within a spread placement group.

Configure the target group of the Network Load Balancer to perform health checks on the default HTTP port 80.

Launch the EC2 instances within a partition placement group.

Show answer & explanation

Answer: Launch the EC2 instances within a cluster placement group.; Configure the target group of the Network Load Balancer to perform health checks on port 9900.

Answer

To meet the requirements, the solutions architect should launch the EC2 instances in a cluster placement group and configure the Network Load Balancer target group to perform health checks on port 9900.

Launching the EC2 instances in a cluster placement group ensures that the instances are placed physically close together in the AWS infrastructure, providing the low-latency, high-throughput network performance required. Configuring the target group health check port to use port 9900 ensures that the Network Load Balancer correctly checks the port where the database application is active, preventing healthy targets from being marked as unhealthy.

Step-by-Step Solution

Analyze the network latency and throughput requirements between the EC2 instances.

The application requires high throughput and very low node-to-node latency, which points to the need for a cluster placement group.

Cluster placement groups place instances physically close to each other within a single Availability Zone, enabling high-performance networking.

Analyze the load balancing and health check requirements.

The database application listens on custom port 9900, meaning health checks must be directed to port 9900.

Querying the default port 80 instead of the custom port 9900 would fail, causing the load balancer to incorrectly identify all instances as unhealthy.

Key Concept

High-Performance and Elastic Compute Solutions require appropriate placement groups for low-latency node-to-node communication, and correct health check configuration to ensure traffic is correctly routed to healthy custom ports.

Question 948Question →

A healthcare organization is migrating its patient portal backend to AWS. The architecture consists of:

* Web APIs hosted on Amazon EC2 instances in a private subnet that run continuously to handle steady-state portal traffic.
* Background workers on Amazon EC2 instances in the same private subnet that process and upload large log files to Amazon S3.
* A database tier running on Amazon RDS for PostgreSQL.

The organization wants to optimize compute, database, and data transfer costs for this workload. Which strategy should a solutions architect recommend to achieve the most cost-effective architecture?

Purchase a single Compute Savings Plan to cover both the EC2 instances and the Amazon RDS database, and configure an Amazon S3 Gateway VPC Endpoint.

Purchase a Compute Savings Plan for the EC2 instances, purchase Amazon RDS Reserved DB Instances for the database, and configure an Amazon S3 Gateway VPC Endpoint.

Purchase a Compute Savings Plan for the EC2 instances, purchase Amazon RDS Reserved DB Instances for the database, and route the log upload traffic to Amazon S3 through a NAT Gateway.

Migrate the continuous log processing background workers to AWS Lambda functions, purchase a Compute Savings Plan for the EC2 instances and Lambda functions, purchase Amazon RDS Reserved DB Instances for the database, and configure an Amazon S3 Gateway VPC Endpoint.

Show answer & explanation

Answer: Purchase a Compute Savings Plan for the EC2 instances, purchase Amazon RDS Reserved DB Instances for the database, and configure an Amazon S3 Gateway VPC Endpoint.

Answer

Purchase a Compute Savings Plan for the EC2 instances, purchase Amazon RDS Reserved DB Instances for the database, and configure an Amazon S3 Gateway VPC Endpoint.

Purchasing a Compute Savings Plan covers the EC2 instances at a discounted rate, while Amazon RDS Reserved DB Instances provide the necessary discount for the database tier because Compute Savings Plans do not apply to Amazon RDS. Additionally, configuring an Amazon S3 Gateway VPC Endpoint allows the background workers to upload files to Amazon S3 without passing through a NAT Gateway, which avoids costly data processing fees.

Step-by-Step Solution

Analyze the compute requirements for the EC2 instances.

The EC2 instances run continuously in a steady state, making them eligible for compute discounts. A Compute Savings Plan offers flexibility and savings of up to 66% on EC2 instance usage.

Compute Savings Plans are optimal for steady-state EC2 workloads and offer easier management across instance families.

Analyze the database tier requirements.

The Amazon RDS PostgreSQL database runs 24/7. Compute Savings Plans do not cover RDS instances. Therefore, purchasing RDS Reserved DB Instances is the correct purchasing model to obtain database discounts.

Reserved DB Instances must be purchased separately to cover RDS database costs as they are outside the scope of Compute Savings Plans.

Analyze the network data transfer to Amazon S3.

Uploading large log files from a private subnet to S3 via a NAT Gateway would incur high data processing fees ($0.045 per GB). Creating a free Amazon S3 Gateway VPC Endpoint routes this traffic directly and securely to S3 at no cost.

Gateway VPC Endpoints eliminate NAT Gateway data processing costs for S3-bound traffic.

Key Concept

Selecting cost-optimized compute purchasing options (Compute Savings Plans vs. Reserved Instances) and optimizing network data transfer paths to minimize operational costs.

Estimated Time:2m 0s

Question 949Question →

A logistics organization tracks real-time location telemetry for its fleet of vehicles. The fleet operations generate a highly predictable write volume: from Monday through Friday between 08:00 and 18:00, telemetry ingestion averages $15,000$ write requests per second, whereas overnight and during weekends, it drops to a baseline of $100$ write requests per second. Additionally, once a month, an internal audit team executes a heavy read-intensive batch job that scans the entire database to generate compliance reports, which takes approximately 2 hours. Which TWO of the following configurations represent the most cost-effective database capacity design to meet these requirements?

Select all that apply

Configure the Amazon DynamoDB table in Provisioned Capacity Mode and implement Application Auto Scaling with scheduled actions to scale up Write Capacity Units (WCUs) to

15,000

before 08:00 on weekdays and scale down to

100

WCUs during off-peak hours.

Enable Point-in-Time Recovery (PITR) on the DynamoDB table, export the table data directly to Amazon S3 once a month, and perform the compliance audit queries using Amazon Athena.

Configure the Amazon DynamoDB table in On-Demand Capacity Mode to dynamically accommodate the

15,000

writes per second during weekdays and automatically scale down to

100

writes per second overnight.

Configure the database on an Amazon RDS Multi-AZ DB instance deployment, and configure the monthly compliance audit job to read directly from the passive standby DB instance to avoid performance impact on the primary instance.

Export the DynamoDB table data to Amazon S3 Standard-Infrequent Access (S3 Standard-IA), execute the compliance queries using Amazon Athena, and immediately delete the exported data in S3 after the 2-hour audit completes to minimize storage costs.

Show answer & explanation

Answer: Configure the Amazon DynamoDB table in Provisioned Capacity Mode and implement Application Auto Scaling with scheduled actions to scale up Write Capacity Units (WCUs) to $15,000$ before 08:00 on weekdays and scale down to $100$ WCUs during off-peak hours.; Enable Point-in-Time Recovery (PITR) on the DynamoDB table, export the table data directly to Amazon S3 once a month, and perform the compliance audit queries using Amazon Athena.

Answer

Configure the DynamoDB table in Provisioned Capacity Mode with scheduled scaling to transition between

15,000

and

100

WCUs, and export the table data via Point-in-Time Recovery (PITR) to Amazon S3 to query using Amazon Athena.

The correct database design incorporates Provisioned Capacity Mode with scheduled scaling for writes, combined with PITR exports to Amazon S3 and Amazon Athena for read-heavy compliance audits. Scheduled scaling aligns capacity with predictable changes, reducing write costs by over 85% compared to On-Demand capacity mode. Exporting DynamoDB table data using PITR does not consume any live table capacity, eliminating the need to provision high Read Capacity Units (RCUs) for monthly 2-hour compliance scans.

Step-by-Step Solution

Analyze the write workload characteristics and predictability.

The write workload has a predictable, high-volume peak of

15,000

writes/sec during weekdays and drops to

100

writes/sec overnight and on weekends.

Understanding whether traffic is predictable is key to choosing between DynamoDB On-Demand and Provisioned Capacity modes.

Calculate the comparative costs of On-Demand vs. Provisioned Capacity with Scheduled Scaling.

On-Demand write costs:

15,000 \text{ writes/sec} \times 3600 \text{ sec/hour} \times 10 \text{ hours/day} \times 22 \text{ days/month} = 11.88 \text{ billion writes/month}

. At

\$1.25

per million, this costs

\$14,850.00

per month. Provisioned capacity costs:

15,000 \text{ WCUs} \times 220 \text{ hours} \times \$0.00065 \approx \$2,145.00

+ off-peak baseline costs of

\$33.15

, totaling

\$2,178.15

per month.

Determining the most cost-effective capacity option for the database writes.

Evaluate options for offloading the monthly 2-hour database scan.

Scanning the live database directly requires provisioning massive Read Capacity Units (RCUs) which is highly expensive and risks throttling. Exporting the database using PITR to S3 does not consume live read capacity and allows querying via Amazon Athena for a fraction of the cost.

Decoupling analytical queries from transactional workloads to optimize cost and performance.

Key Concept

Using scheduled scaling for predictable capacity adjustments and offloading read-heavy analytical scans using serverless query engines on exported data to minimize total database costs.

Estimated Time:3m 0s

Question 950Question →

An enterprise operates a multi-account environment managed under AWS Organizations. The enterprise has two primary cost management requirements. First, they need to proactively identify and receive alerts for unexpected, sudden spending anomalies across all accounts within 24 hours of occurrence, without manually maintaining static threshold limits for highly variable workloads. Second, they need to present showback-style billing reports to distinct business units where the resource consumption is calculated using custom-defined pricing rules and negotiated rate structures, rather than standard consolidated billing rates.

Which combination of AWS services and configurations should a solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

AWS Cost Anomaly Detection configured with an account monitor and an alert subscription using an Amazon Simple Notification Service (Amazon SNS) topic.

AWS Billing Conductor configured with custom billing groups, pricing plans, and pricing rules to generate pro forma billing data for the business units.

AWS Cost Explorer with scheduled reports configured to run hourly and distribute cost reports to stakeholders.

AWS Budgets configured with static monthly cost targets and an alert notification triggered when actual cost exceeds 110% of the budgeted amount.

AWS Billing and Cost Management console using Cost Categories to dynamically apply custom billing rules and recalculate the consolidated invoice.

Show answer & explanation

Answer: AWS Cost Anomaly Detection configured with an account monitor and an alert subscription using an Amazon Simple Notification Service (Amazon SNS) topic.; AWS Billing Conductor configured with custom billing groups, pricing plans, and pricing rules to generate pro forma billing data for the business units.

Answer

Implement AWS Cost Anomaly Detection with account monitors and SNS alerting, combined with AWS Billing Conductor to establish billing groups, pricing plans, and pricing rules for pro forma showback reporting.

Proactive detection of variable workload anomalies requires AWS Cost Anomaly Detection, which uses machine learning models to dynamically analyze cost and usage patterns and trigger alert notifications via Amazon SNS. For custom showback pricing, AWS Billing Conductor is the correct service because it groups AWS Organizations accounts and applies custom pricing rules to generate pro forma billing files reflecting negotiated rates.

Step-by-Step Solution

Evaluate the requirement for proactive anomaly detection with highly variable workloads.

Identify that static thresholds are unsuitable and choose AWS Cost Anomaly Detection, which uses machine learning to automatically establish baselines and dynamically alert on spikes within 24 hours.

This avoids manual maintenance of static alerts.

Evaluate the requirement to generate showback billing reports with custom negotiated rates instead of consolidated rates.

Select AWS Billing Conductor to organize accounts into billing groups and apply custom pricing rules and plans to generate pro forma billing data.

AWS Billing Conductor is the designated service to reprice and group multi-account billing details for downstream showback.

Verify and exclude alternative tool options.

Discard solutions based on static AWS Budgets, retrospective Cost Explorer schedules, or non-repricing Cost Categories.

These alternatives either introduce manual threshold overhead, lack immediate alerting capacity, or cannot perform dynamic repricing.

Key Concept

Understanding the division of capabilities between proactive dynamic monitoring (AWS Cost Anomaly Detection) and custom showback billing modeling (AWS Billing Conductor).

Question 951Question →

A software development company uses Cost Allocation Tags to track AWS expenditures across multiple departments. The finance team needs to receive immediate notifications if daily spending for resources tagged as Department: R&D exhibits an unusual spike compared to normal historical usage patterns. They do not want to set static monetary thresholds because the department's baseline activities fluctuate dynamically. Which AWS solution should a solutions architect recommend to meet these requirements with the least administrative effort?

Configure AWS Cost Anomaly Detection with a monitor type based on Cost Allocation Tags, and define an alert subscription.

Configure a daily scheduled report in AWS Cost Explorer filtered by the Department: R&D tag, and distribute it to the finance team via email.

Create an AWS Budget with a custom filter for the Department: R&D tag, and configure an email alert triggered when actual costs exceed a fixed target budget.

Purchase a Compute Savings Plan for the resources tagged with Department: R&D to automatically reduce the baseline costs and prevent spike charges.

Show answer & explanation

Answer: Configure AWS Cost Anomaly Detection with a monitor type based on Cost Allocation Tags, and define an alert subscription.

Answer

Configure AWS Cost Anomaly Detection with a monitor type based on Cost Allocation Tags, and define an alert subscription.

The correct option is to configure AWS Cost Anomaly Detection with a monitor type based on Cost Allocation Tags, and define an alert subscription. This service uses machine learning to establish a dynamic baseline of normal spending patterns, allowing it to detect and alert on anomalous spikes immediately without requiring static thresholds.

Step-by-Step Solution

Analyze the requirements for cost monitoring: immediate alerting, tag-based grouping (Department: R&D), and dynamic baselines (no static thresholds).

Identify that standard static thresholds (like AWS Budgets) or retrospective tools (like Cost Explorer) do not fit the dynamic, immediate alert requirement.

The requirement specifies that the department's baseline activities fluctuate dynamically, making static thresholds impractical.

Evaluate AWS Cost Anomaly Detection for tag-based workloads.

Determine that Cost Allocation Tag monitors can be configured to dynamically track specific tag keys and values.

AWS Cost Anomaly Detection uses machine learning to establish a baseline of normal spending and detect anomalies without static inputs.

Configure the notification mechanism for immediate alerts.

Associate the anomaly monitor with an alert subscription to notify the finance team via email or Amazon SNS.

Immediate notification is required to allow the team to react to runaway costs quickly.

Key Concept

AWS Cost Anomaly Detection uses machine learning to identify unexpected cost increases and anomalous spending behavior. It supports monitoring by AWS services, member accounts, cost categories, or cost allocation tags, allowing for dynamic baselines without static thresholds.

Estimated Time:1m 30s

Question 952Question →

A logistics company is migrating its core order processing system to Amazon RDS. The database must be highly available across multiple Availability Zones with a Recovery Time Objective (RTO) of less than 2 minutes and a Recovery Point Objective (RPO) of 0 (no data loss). Additionally, the solutions architect must scale read performance horizontally to handle reporting queries without impacting write latency on the primary database instance.

Which combination of database configuration and application routing strategies will meet these requirements? (Select TWO.)

Select all that apply

Deploy the database as a standard Amazon RDS Multi-AZ DB instance deployment and route read queries to the standby instance.

Deploy the database as an Amazon RDS Multi-AZ DB cluster with one writer and two readable standby DB instances.

Deploy a single-AZ Amazon RDS DB instance and configure a cross-AZ Read Replica, using Amazon Route 53 to automatically promote the replica during an outage.

Configure the application to route write queries to the cluster endpoint and read queries to the reader endpoint of the DB cluster.

Use Amazon Route 53 latency-based routing to distribute database write traffic between the primary database instance and the read replica.

Show answer & explanation

Answer: Deploy the database as an Amazon RDS Multi-AZ DB cluster with one writer and two readable standby DB instances.; Configure the application to route write queries to the cluster endpoint and read queries to the reader endpoint of the DB cluster.

Answer

Deploy the database as an Amazon RDS Multi-AZ DB cluster with one writer and two readable standby DB instances, and configure the application to route write queries to the cluster endpoint and read queries to the reader endpoint of the DB cluster.

Deploying an Amazon RDS Multi-AZ DB cluster provides a primary writer instance and two readable standby instances across three Availability Zones, ensuring automatic failover under 2 minutes and zero data loss (RPO of 0) via semi-synchronous replication. The application can scale reads horizontally by routing queries to the reader endpoint, which load-balances the traffic among the standby instances, while routing writes to the cluster endpoint.

Step-by-Step Solution

Identify the high availability and zero data loss (RPO = 0) requirements during an Availability Zone outage.

Determine that standard single-instance or asynchronous replica configurations are insufficient, and a synchronous or semi-synchronous replication method (such as RDS Multi-AZ or Aurora) is required.

Multi-AZ deployments provide automatic failover and ensure data is replicated to another AZ before writes are committed, satisfying the RPO of 0 and RTO under 2 minutes.

Evaluate how to support horizontal read scaling alongside high availability in RDS.

Identify that a standard Multi-AZ DB instance has a passive standby that cannot be read from, whereas an RDS Multi-AZ DB cluster provides two readable standby instances.

Choosing the Multi-AZ DB cluster deployment enables both high availability and horizontal read scaling off the standby instances.

Determine the proper application routing mechanism to separate reads and writes in the cluster.

Route write operations to the cluster endpoint (pointing to the primary writer) and read operations to the reader endpoint (which load-balances reads to the standby instances).

This separation allows the application to scale reads horizontally without overloading the primary writer database instance.

Key Concept

Amazon RDS Multi-AZ DB cluster configurations for high availability and horizontal read scaling.

Question 953Question →

A company has two VPCs (VPC A and VPC B) in the `us-east-1` Region. An application running on Amazon EC2 instances in private subnets in VPC A needs to transfer $40\text{ TB}$ of data monthly to a database cluster in VPC B. Additionally, the EC2 instances in VPC A upload $15\text{ TB}$ of backup data to Amazon S3 daily. Currently, VPC A and VPC B are connected using an AWS Transit Gateway, and all external traffic from the private subnets, including the S3 backup traffic, is routed through a central NAT Gateway. A solutions architect needs to reduce data transfer costs.

Which combination of actions will meet these requirements most cost-effectively? (Select TWO.)

Select all that apply

Create a Gateway VPC Endpoint for Amazon S3 in VPC A, and associate it with the route tables of the private subnets.

Establish a VPC Peering connection between VPC A and VPC B, and route the traffic between the VPCs through the peering connection.

Deploy an Interface VPC Endpoint for Amazon S3 in VPC A to route the backup traffic privately.

Route the Amazon S3 backup traffic through the existing AWS Transit Gateway to a NAT Gateway in VPC B.

Provision a NAT Instance in VPC A to replace the NAT Gateway, and route the Amazon S3 backup traffic through it.

Show answer & explanation

Answer: Create a Gateway VPC Endpoint for Amazon S3 in VPC A, and associate it with the route tables of the private subnets.; Establish a VPC Peering connection between VPC A and VPC B, and route the traffic between the VPCs through the peering connection.

Answer

Create a Gateway VPC Endpoint for Amazon S3 in VPC A and associate it with the route tables of the private subnets, and establish a VPC Peering connection between VPC A and VPC B and route the traffic between the VPCs through the peering connection.

To optimize costs, the solutions architect should address both the S3 backups and the cross-VPC data transfer. For S3, using a Gateway VPC Endpoint is free and bypasses the NAT Gateway data processing fees. For cross-VPC traffic, since there are only two VPCs in the same Region with high data volumes, VPC Peering is more cost-effective than AWS Transit Gateway because VPC Peering does not charge hourly or data processing fees.

Step-by-Step Solution

Analyze S3 backup data transfer costs.

15\text{ TB}

of data daily sent via a NAT Gateway incurs high NAT Gateway data processing charges (approx. $0.045 per GB).

Identifying the primary source of the data transfer cost is the first step to optimization.

Select the most cost-effective S3 routing option.

A Gateway VPC Endpoint for Amazon S3 is free to create and incurs no data processing charges, bypassing the NAT Gateway completely.

Replacing NAT Gateway routing with a Gateway VPC Endpoint for S3 traffic removes NAT Gateway processing fees.

Analyze cross-VPC data transfer costs.

40\text{ TB}

of monthly data transferred through AWS Transit Gateway incurs both hourly attachment fees and per-GB processing charges ($0.02 per GB).

Evaluating the Transit Gateway costs allows comparison with alternative routing options.

Select the most cost-effective cross-VPC routing option.

VPC Peering has no hourly charge and no data processing charges, charging only for standard data transfer rates between Availability Zones.

Replacing Transit Gateway with VPC Peering for a point-to-point connection between two VPCs in the same Region eliminates Transit Gateway data processing fees.

Key Concept

Data transfer cost optimization using Gateway VPC Endpoints for S3 and VPC Peering instead of NAT Gateway and Transit Gateway for high-volume point-to-point transfers.

Question 954Question →

An enterprise has multiple business units operating under a consolidated billing family in AWS Organizations. Each team relies on specific cost allocation tags for their resources. The Solutions Architect must implement a cost management solution to meet two main requirements:

1. Receive alerts within 24 hours of any sudden, unexpected spending spikes that deviate from historical usage patterns.
2. Track monthly spend against a budget for each resource tag and receive email warnings when the forecasted cost is projected to exceed the allocated budget.

Which combination of AWS services or features will meet these requirements with the least administrative overhead? (Select TWO.)

Select all that apply

AWS Cost Anomaly Detection to monitor cost monitors and send alerts when abnormal spending patterns are detected.

AWS Budgets with cost filters based on cost allocation tags to trigger notifications when forecasted amounts exceed thresholds.

AWS Cost Explorer with daily scheduled reports configured to email administrators when cost changes occur.

AWS Billing Conductor to define pro forma billing groups and set up billing alerts for each tag configuration.

AWS Budgets Reports configured to analyze historical anomalies and trigger warning notifications to email distribution lists.

Show answer & explanation

Answer: AWS Cost Anomaly Detection to monitor cost monitors and send alerts when abnormal spending patterns are detected.; AWS Budgets with cost filters based on cost allocation tags to trigger notifications when forecasted amounts exceed thresholds.

Answer

AWS Cost Anomaly Detection should be used to detect sudden, unexpected spending spikes using machine learning, and AWS Budgets with cost filters should be used to track monthly tag-based spending against forecasted thresholds.

The correct options are the use of AWS Cost Anomaly Detection and AWS Budgets. AWS Cost Anomaly Detection satisfies the requirement to identify unexpected cost spikes that deviate from historical patterns within 24 hours using machine learning. AWS Budgets satisfies the requirement to monitor specific tag-based costs and alert proactively based on forecasted monthly spending before the budget threshold is actually crossed.

Step-by-Step Solution

Analyze the requirement for detecting unexpected spending spikes that deviate from historical patterns.

AWS Cost Anomaly Detection is selected because it uses machine learning to establish a baseline of historical usage and dynamically flag anomalies within 24 hours.

Traditional static budgets cannot easily detect irregular spikes that remain under a threshold, whereas AWS Cost Anomaly Detection is designed for this purpose.

Analyze the requirement for tracking monthly spend against tag-based budgets and receiving alerts based on forecasted amounts.

AWS Budgets is selected and configured with tag-based cost filters and forecasted alert thresholds.

AWS Budgets supports defining alerts on both actual and forecasted metrics, allowing proactive action before the budget is actually breached.

Key Concept

Selecting the appropriate AWS cost management tool based on the required monitoring latency (proactive forecasting vs. dynamic ML anomaly detection vs. retrospective reporting).

Question 955Question →

A gaming company is deploying a backend matchmaking and state synchronization engine on Amazon EC2 instances. The engine requires sub-millisecond latency and high network throughput for peer-to-peer node communication to keep game states synchronized. The game client traffic is distributed using a Network Load Balancer (NLB) configured to route UDP and TCP traffic to port $8200$ on the instances. Which two actions should the solutions architect take to meet these performance and reliability requirements? (Choose two.)

Select all that apply

Launch the Amazon EC2 instances in a cluster placement group.

Configure the target group health checks to explicitly use port

8200

Launch the Amazon EC2 instances in a spread placement group.

Launch the Amazon EC2 instances in a partition placement group.

Configure the target group health checks to use the default health check port (port

80

Show answer & explanation

Answer: Launch the Amazon EC2 instances in a cluster placement group.; Configure the target group health checks to explicitly use port $8200$ .

Answer

The correct options are to launch the EC2 instances in a cluster placement group and to configure the target group health checks to explicitly use port

8200

Launching the Amazon EC2 instances in a cluster placement group ensures that the compute nodes are physically close together within a single Availability Zone, enabling low-latency, high-throughput network performance. In addition, configuring the target group health checks to explicitly use port

8200

ensures that the Network Load Balancer correctly monitors the status of the custom application port rather than failing on the default port.

Step-by-Step Solution

Analyze the low-latency networking requirements of the peer-to-peer matchmaking engine.

The application requires sub-millisecond node-to-node latency, which points to using an EC2 cluster placement group.

A cluster placement group places instances physically close together within a single Availability Zone, enabling high-speed, low-latency communication.

Analyze the port mapping for the application and the health check configuration.

The application runs on port

8200

. Configuring the target group health checks to use this custom port ensures accurate health monitoring.

Using the default port like port

80

for health checks when the application is running on port

8200

would lead to false-negative health check results, causing the NLB to mark healthy targets as unhealthy.

Key Concept

Optimizing compute node-to-node network latency and health monitoring configurations for high-performance applications on AWS.

Question 956Question →

A startup is deploying a new application that experiences highly unpredictable traffic. The application performs database read and write operations that spike suddenly for a few minutes and then drop to zero for hours. The startup wants to minimize costs and avoid paying for unused resources. Which Amazon DynamoDB capacity mode is the most cost-effective for this workload?

On-demand capacity mode

Provisioned capacity mode with Auto Scaling

Provisioned capacity mode with high fixed capacity

Provisioned capacity mode with an Amazon SQS Standard queue to buffer writes

Show answer & explanation

Answer: On-demand capacity mode

Answer

On-demand capacity mode

On-demand capacity mode is the most cost-effective choice for unpredictable, spiky workloads with long idle periods. It automatically scales to meet the sudden spikes instantly and incurs zero costs during idle hours since billing is based strictly on actual read and write request units consumed.

Step-by-Step Solution

Analyze the workload characteristics from the scenario.

The workload has highly unpredictable traffic, sudden spikes lasting a few minutes, and long periods of zero activity.

To determine the most cost-optimized and auto-scaled option, we must match workload patterns to AWS billing models.

Evaluate DynamoDB capacity modes against the workload patterns.

Provisioned capacity mode requires paying for a minimum threshold continuously, leading to idle costs. On-demand capacity mode scales instantly and charges only per request.

This identifies the configuration that eliminates costs during idle times and handles sudden spikes without throttling.

Key Concept

Amazon DynamoDB capacity modes for cost optimization

Estimated Time:1m 0s

Question 957Question →

A company is developing a new event-driven registration system that experiences highly unpredictable, short spikes in traffic during marketing campaigns, with long periods of complete inactivity. The system needs to execute lightweight backend logic to process registration requests, maintain strict processing sequence for order transactions, and store the registration details. The company wants to minimize costs during idle periods. Which two AWS service configurations should a solutions architect recommend to meet these requirements? (Select TWO.)

Select all that apply

AWS Lambda to execute the short-lived backend logic.

Amazon DynamoDB in on-demand capacity mode to store registration details.

An AWS Lambda function configured with a continuous loop to poll for incoming orders 24/7.

Amazon DynamoDB with provisioned capacity mode set to the maximum expected peak load.

A standard Amazon SQS queue to buffer requests that require strict sequential processing.

Show answer & explanation

Answer: AWS Lambda to execute the short-lived backend logic.; Amazon DynamoDB in on-demand capacity mode to store registration details.

Answer

The correct options are the use of AWS Lambda for short-lived backend logic, and Amazon DynamoDB in on-demand capacity mode for storing registration details.

AWS Lambda and Amazon DynamoDB in on-demand capacity mode together form a fully serverless backend. Both services automatically scale to zero when there is no traffic, ensuring no compute or write/read database capacity charges are incurred during inactive periods, and scale instantly to handle spikes.

Step-by-Step Solution

Analyze the workload requirements and identify that the traffic is highly unpredictable with long periods of complete inactivity.

This requires compute and database services that scale down to zero when idle to minimize costs.

Designing for cost optimization requires avoiding provisioned resources that incur charges during idle times.

Evaluate the compute options for executing lightweight backend logic under these conditions.

AWS Lambda is selected because it scales automatically and charges only when the code runs, with no idle costs.

Traditional server-based options or continuous execution models would waste money during inactive periods.

Evaluate the database options to store registration details cost-effectively.

Amazon DynamoDB with on-demand capacity mode is selected because it scales instantly with traffic spikes and charges only for read/write requests, with no hourly provisioning costs.

Provisioned database capacity incurs ongoing charges regardless of actual utilization, violating the cost-optimization goal.

Key Concept

Serverless architectures dynamically scale compute and database resources to match demand, minimizing idle costs for spiky, unpredictable workloads.

Question 958Question →

A logistics company processes high-volume shipment updates arriving at highly unpredictable intervals. The processing application requires strict ordering of updates per shipment ID to prevent data corruption in the database. Each processing job is CPU-heavy and takes approximately $18$ minutes to complete. The database uses Amazon DynamoDB to record updates. The company wants to implement a serverless, cost-optimized architecture that automatically scales to zero during idle periods and scales up to handle workload spikes. Which architecture should a solutions architect recommend?

Publish shipment updates to an Amazon SQS FIFO queue. Run the processing application on AWS Fargate as an Amazon ECS service. Configure ECS Service Auto Scaling to scale tasks between

0

and

10

based on the SQS queue backlog, and configure Amazon DynamoDB with On-Demand capacity mode.

Publish shipment updates to an Amazon SQS Standard queue. Run the processing application on AWS Fargate as an Amazon ECS service. Configure ECS Service Auto Scaling based on average CPU utilization, and configure Amazon DynamoDB with Provisioned capacity mode.

Configure an AWS Lambda function to process updates directly from an Amazon SQS FIFO queue event source, and configure Amazon DynamoDB with On-Demand capacity mode.

Publish shipment updates to an Amazon SQS FIFO queue. Run the processing application on AWS Fargate as an Amazon ECS service. Configure ECS Service Auto Scaling to scale tasks based on the SQS queue backlog, and configure Amazon DynamoDB with Provisioned capacity mode to handle unpredictable spiky traffic.

Show answer & explanation

Answer: Publish shipment updates to an Amazon SQS FIFO queue. Run the processing application on AWS Fargate as an Amazon ECS service. Configure ECS Service Auto Scaling to scale tasks between $0$ and $10$ based on the SQS queue backlog, and configure Amazon DynamoDB with On-Demand capacity mode.

Answer

Publish shipment updates to an Amazon SQS FIFO queue. Run the processing application on AWS Fargate as an Amazon ECS service. Configure ECS Service Auto Scaling to scale tasks between

0

and

10

based on the SQS queue backlog, and configure Amazon DynamoDB with On-Demand capacity mode.

The correct solution satisfies all architectural and cost constraints. An Amazon SQS FIFO queue maintains strict ordering per shipment ID using message groups. Since the CPU-intensive tasks run for

18

minutes, AWS Fargate is used instead of AWS Lambda, which would time out. ECS Service Auto Scaling dynamically scales the Fargate tasks down to

0

when the queue is empty to eliminate idle compute costs. Using Amazon DynamoDB in On-Demand capacity mode is optimal because the workload intervals are unpredictable, meaning the company only pays for active database reads and writes with zero base cost when idle.

Step-by-Step Solution

Analyze execution duration constraints against compute options.

Since each processing job takes approximately

18

minutes, AWS Lambda is ruled out due to its strict

15

-minute maximum execution timeout. AWS Fargate (ECS) is the appropriate serverless compute choice as it does not have this timeout constraint.

Ensures that processing completes successfully without timing out.

Determine ordering and scalability requirements for ingestion.

Strict ordering per shipment ID requires an Amazon SQS FIFO queue, where the shipment ID is used as the message group ID.

Guarantees sequential processing per shipment to avoid database corruption while allowing scaling across multiple shipment IDs.

Select database capacity mode for unpredictable workload intervals.

Configure Amazon DynamoDB with On-Demand capacity mode rather than Provisioned capacity mode.

Minimizes cost by charging only for actual read/write requests, scaling to zero when there are no updates, and automatically scaling to handle sudden spikes.

Design cost-efficient auto scaling for compute resources.

Configure ECS Service Auto Scaling based on SQS queue backlog (using queue size metrics) to scale tasks down to

0

when idle.

Eliminates idle compute costs during periods of no incoming updates.

Key Concept

Selecting serverless compute and database resources that scale dynamically while handling long-running task constraints and strict ordering requirements in a cost-optimized manner.

Estimated Time:2m 30s

Question 959Question →

A company operates a globally distributed mobile gaming application. The gaming client communicates with a backend matchmaking and state service using a custom TCP-based protocol. The backend is currently hosted on Amazon ECS tasks behind an Application Load Balancer (ALB) in a single AWS Region. Users in distant regions are experiencing high latency and connection timeouts due to public internet routing inefficiencies. Additionally, client devices require a fixed set of IP addresses for security allowlisting in corporate firewalls. Which solution should a solutions architect recommend to resolve the latency issues while meeting the security requirements?

Configure AWS Global Accelerator in front of the ALB. Register the ALB as the endpoint group target. Update the client configuration to connect via the provided Anycast static IP addresses.

Configure an Amazon CloudFront distribution with the ALB as the origin. Route traffic through CloudFront using a custom Domain Name System (DNS) record, and enable Origin Shield.

Configure Amazon Route 53 with latency-based routing to resolve the application's domain name directly to the ALB. Apply AWS WAF to the ALB to accelerate TCP connections.

Deploy AWS WAF and enable AWS Shield Advanced directly on the ALB. Use the AWS Shield Anycast IPs to route global client traffic through the nearest AWS edge location.

Show answer & explanation

Answer: Configure AWS Global Accelerator in front of the ALB. Register the ALB as the endpoint group target. Update the client configuration to connect via the provided Anycast static IP addresses.

Answer

Configure AWS Global Accelerator in front of the ALB. Register the ALB as the endpoint group target. Update the client configuration to connect via the provided Anycast static IP addresses.

AWS Global Accelerator is designed to optimize routing and improve availability for TCP and UDP traffic by onboarding traffic at the nearest AWS Edge Location and routing it over the congestion-free AWS global network. It also provides two static Anycast IP addresses, which satisfies the client firewall allowlisting requirement.

Step-by-Step Solution

Analyze the protocol and client requirements.

The application uses a custom TCP-based protocol (non-HTTP/S) and requires static IP addresses on the client-side for firewall allowlisting.

This rules out Amazon CloudFront, which is restricted to HTTP/HTTPS traffic, and requires a service that can provide static Anycast IPs.

Evaluate network acceleration services.

AWS Global Accelerator supports non-HTTP TCP/UDP protocols, routes traffic over the AWS global network to reduce latency, and provides two static Anycast IP addresses.

This directly matches all requirements of the scenario, including latency reduction and fixed IP support.

Select the optimal architecture configuration.

Provision AWS Global Accelerator with the Application Load Balancer (ALB) as the endpoint target.

This setup allows clients to connect to the static Anycast IPs, routing their TCP packets over the optimized AWS global backbone to the ALB.

Key Concept

AWS Global Accelerator vs. Amazon CloudFront for non-HTTP/S TCP traffic

Estimated Time:1m 30s

Question 960Question →

A financial services company is deploying a distributed in-memory data grid (IMDG) on Amazon EC2 instances to cache real-time trading data. The grid nodes require sub-millisecond node-to-node network latency and high throughput for clustering and state replication. Additionally, the grid must expose an administration API port (port 8443) to a Network Load Balancer (NLB) for health monitoring and external control, while the main grid communication runs on port 5701. How should a solutions architect configure the EC2 instances and the NLB target group to meet these requirements?

Launch the EC2 instances in a cluster placement group. Register the instances in the NLB target group on port 5701, and configure the target group health checks to explicitly use port 8443.

Launch the EC2 instances in a partition placement group. Register the instances in the NLB target group on port 5701, and allow the target group health checks to default to the traffic port.

Launch the EC2 instances in a spread placement group. Register the instances in the NLB target group on port 5701, and configure the target group health checks to explicitly use port 8443.

Launch the EC2 instances in a cluster placement group. Register the instances in the NLB target group on port 5701, and allow the target group health checks to default to the traffic port.

Show answer & explanation

Answer: Launch the EC2 instances in a cluster placement group. Register the instances in the NLB target group on port 5701, and configure the target group health checks to explicitly use port 8443.

Answer

Launch the EC2 instances in a cluster placement group, register the instances in the NLB target group on port 5701, and configure the target group health checks to explicitly use port 8443.

A cluster placement group is designed for low-latency, high-throughput node-to-node communication within a single Availability Zone. Since the main grid traffic is on port 5701, the targets must be registered on that port. However, because the health status is monitored via an administration API on port 8443, the health check port must be explicitly overridden to port 8443, otherwise the load balancer will query the traffic port (5701) and fail to receive valid health responses.

Step-by-Step Solution

Analyze placement group requirements for the EC2 instances.

Determine that a cluster placement group is required because the distributed in-memory data grid requires sub-millisecond node-to-node latency and high throughput.

Cluster placement groups pack instances close together within an Availability Zone to enable low-latency, high-throughput network performance.

Configure the target group for the Network Load Balancer.

Traffic routing must point to the primary port where nodes handle incoming client connections and data replication.

Configure the health check port override in the target group settings.

Configure the target group health check port explicitly to port 8443.

By default, the load balancer performs health checks on the registered traffic port (port 5701). Since the administration and health API runs on port 8443, the health check port must be overridden, otherwise the load balancer will query the wrong port and mark the targets as unhealthy.

Key Concept

EC2 cluster placement groups for low-latency networking combined with custom port overrides for target group health checks.

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