SAA-C03 Design High-Performing Architectures Practice Test 6 — 15 Questions

Question 1

An ECS service runs on EC2 capacity. During peak traffic, tasks frequently wait for available container instances. The team wants faster scale-out for the underlying EC2 capacity when tasks increase. What is the best first architectural step?

Accepted Answer

Use an ECS capacity provider (or Auto Scaling integration) to scale the EC2 instances based on ECS demand.. Option B is correct because an ECS capacity provider (or Auto Scaling integration) directly links ECS task-level demand to EC2 instance scaling. When tasks are pending due to insufficient container instances, the capacity provider triggers a scale-out event on the Auto Scaling group, adding EC2 instances to accommodate the workload. This is the most direct and efficient architectural step to reduce the wait time for available container instances during peak traffic.

Answer

Tune the container health check settings so tasks stop failing and stay running.

Answer

Pin all tasks to a single Availability Zone to reduce placement overhead.

Answer

Switch the tasks to run only on Fargate so EC2 scaling is no longer relevant.

Question 2

A web application uses an Amazon Aurora DB cluster for a read-heavy workload. The application team needs higher read throughput but cannot change the database schema. They want to avoid blocking writes and are willing to route read traffic separately. What is the most appropriate architecture change?

Accepted Answer

Create Aurora read replicas and route SELECT queries to an Aurora reader endpoint.. Creating Aurora read replicas and routing SELECT queries to the Aurora reader endpoint is the most appropriate architecture change because Aurora's reader endpoint distributes read traffic across up to 15 low-latency read replicas, providing higher aggregate read throughput without blocking writes. This approach requires no schema changes and allows the application to separate read and write traffic, directly addressing the read-heavy workload requirement.

Answer

Scale up the writer instance storage only; read capacity will automatically increase without using a reader endpoint.

Answer

Move the Aurora cluster to Multi-AZ deployment mode only; read scaling is handled automatically without replicas.

Answer

Replace the cluster with a single RDS instance because it offers consistent performance for both reads and writes.

Question 3

A team serves image files from S3 through CloudFront. During a performance review, they notice that CloudFront cache hit ratio is low and the S3 origin receives many repeated requests for the same images. Request URLs include a volatile query parameter called 'sessionId' that changes for each user, but the image content is identical regardless of 'sessionId'. What configuration change will most effectively increase cache hit ratio?

Accepted Answer

Update the CloudFront cache policy so that 'sessionId' is not included in the cache key (and only stable query parameters are used).. The low cache hit ratio is caused by the volatile 'sessionId' query parameter being included in the CloudFront cache key, which creates a unique cache entry for every user request even though the image content is identical. By updating the cache policy to exclude 'sessionId' from the cache key, CloudFront will treat all requests for the same image as the same cached object, dramatically increasing the cache hit ratio and reducing load on the S3 origin.

Answer

Enable origin request policy to forward all query strings to S3 so responses are always correct for every sessionId.

Answer

Set the CloudFront minimum TTL to 0 seconds so cached objects expire quickly and fetch fresh content more often.

Answer

Disable caching by using CloudFront managed caching disabled so that every request validates with the origin.

Question 4

A trading analytics system deploys 10 EC2 instances that exchange very frequent, low-latency messages over the network. The instances must be placed as close together as possible to minimize network hop count and inter-node jitter. Which deployment choice best matches this requirement?

Accepted Answer

Use a cluster placement group so the instances are placed close together to reduce latency and jitter.. A cluster placement group is designed for low-latency, high-throughput scenarios by placing all instances in a single Availability Zone within the same rack or logical cluster, minimizing network hop count and inter-node jitter. This directly meets the requirement for very frequent, low-latency messaging between 10 EC2 instances.

Answer

Use a spread placement group to distribute instances across multiple underlying hardware to improve overall availability.

Answer

Use no placement group and rely on the Auto Scaling group to balance instance placement automatically.

Answer

Use a partition placement group so each instance is assigned to separate failure domains for low variance.

Question 5

Your company currently uses an Application Load Balancer (ALB) in front of a service that receives a large number of TCP and UDP packets (including UDP-based telemetry). During load tests, you need to support both TCP and UDP traffic at high throughput while keeping stable IP endpoints for a downstream firewall allowlist. Which change best meets these requirements?

Accepted Answer

Switch to a Network Load Balancer (NLB) configured for TCP/UDP, and use Elastic IPs to provide stable endpoint IP addresses for allowlisting.. A Network Load Balancer (NLB) operates at Layer 4 and can handle both TCP and UDP traffic natively, unlike an ALB which only supports HTTP/HTTPS and cannot forward UDP packets. By assigning Elastic IPs to the NLB, you provide stable, static IP endpoints that can be added to a downstream firewall allowlist, meeting both the protocol and throughput requirements.

Answer

Keep the ALB and add an AWS WAF Web ACL to improve throughput and add static IP support.

Answer

Replace the ALB with an API Gateway REST API to support UDP because API Gateway can forward UDP packets.

Answer

Use an Auto Scaling group with multiple EC2 instances and no load balancer to avoid any networking bottlenecks.

Question 6

A DynamoDB-backed multi-tenant app experiences throttling. Most write traffic for tenant 'ACME' targets a single logical stream of events (you write items for ACME in near-real time). The table currently uses partition key = tenantId and sort key = eventTimestamp. CloudWatch shows partition-level throttling concentrated in the ACME partition. What design change most directly improves write throughput for the hottest tenant while still enabling efficient queries for recent events for that tenant?

Accepted Answer

Mitigate the hotspot by changing the partition key to include a shard value (for example, tenantId + '#' + shardId) and write using shardId. Query recent events by fanning out across ACME shards and merging results by eventTimestamp.. Option B is correct because it directly addresses the partition-level throttling by introducing a shard key (e.g., tenantId + '#' + shardId) as the partition key, which distributes ACME's write load across multiple physical partitions. To query recent events for ACME, the application must fan out queries across all shards and merge results by eventTimestamp, which is efficient because each shard holds a subset of the data and the sort key remains eventTimestamp for ordering.

Answer

Add a Global Secondary Index (GSI) with the same partition key (tenantId) and eventTimestamp, and rely on the GSI to spread load.

Answer

Increase the table’s write capacity (or on-demand baseline) without changing the partition key, because DynamoDB will automatically balance hotspots.

Answer

Switch the sort key to a random value to prevent writes from landing on the same physical partition.

Question 7

A serverless API built with AWS Lambda serves latency-sensitive requests. The team observes intermittent slow responses during traffic ramp-ups and expects some users to hit the API immediately after a period of inactivity. Which configuration best reduces cold-start latency during these ramp-ups?

Accepted Answer

Enable Lambda provisioned concurrency on a published alias used by the API, and set a minimum provisioned concurrency greater than zero.. Lambda provisioned concurrency keeps a specified number of execution environments initialized and ready to respond immediately, eliminating cold starts for those invocations. By setting a minimum provisioned concurrency greater than zero on the alias used by API Gateway, the function remains warm even after periods of inactivity, ensuring consistent low latency during traffic ramp-ups.

Answer

Increase the Lambda function’s memory setting; cold starts will always be eliminated regardless of traffic patterns.

Answer

Switch the Lambda runtime to a newer language version and remove any VPC configuration so the function never cold starts.

Answer

Set an API Gateway stage variable to "warm" the function at request time, which forces immediate initialization.

Question 8

A media processing pipeline uses EBS-backed storage for an application that performs sustained random I/O with low latency requirements. During peak processing windows, the team sees increased read latency and occasional timeouts at the application layer. They need predictable, high IOPS performance rather than best-effort throughput. Which EBS configuration choice is most appropriate?

Accepted Answer

Use io1 or io2 EBS volumes configured with a high provisioned IOPS value, and attach them to EBS-optimized instances.. Option B is correct because io1 and io2 volumes are provisioned IOPS SSD volumes designed for sustained, predictable high IOPS performance, which directly addresses the application's need for low-latency random I/O during peak loads. Attaching them to EBS-optimized instances ensures dedicated network bandwidth for EBS traffic, eliminating contention and preventing timeouts.

Answer

Use gp2 volumes and rely on burst credits to handle peak random I/O latency requirements.

Answer

Use standard HDD (st1) volumes, because they provide high throughput and will reduce latency automatically.

Answer

Use S3 instead of EBS for random I/O latency reduction without changing the application.

Question 9

Your mobile app writes events to a single DynamoDB table with partition key = customerId and sort key = eventTime. During a promotional campaign, one tenant ("ACME") generates far more traffic than others. CloudWatch shows sustained throttling (ProvisionedThroughputExceeded) and elevated p99 latency only for that tenant. The workload pattern cannot be changed to a completely different schema, but you can change how items are partitioned. Which design change is most likely to reduce the hot-partition throttling while keeping efficient reads for ACME?

Accepted Answer

Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key.. Option B is correct because salting the partition key by appending a shard number (e.g., customerId + random digit) distributes ACME's writes across multiple partitions, eliminating the hot partition. The sort key still preserves eventTime ordering, so queries for a specific customer can be parallelized across shards and merged client-side or via a composite sort key pattern, maintaining efficient reads.

Answer

Use the same partition key (customerId), but increase the table’s provisioned capacity for that tenant.

Answer

Switch to on-demand capacity mode and keep the partition key unchanged.

Answer

Enable Global Tables so that reads are served from a nearby replica for ACME.

Question 10

A marketing team uses CloudFront with an S3 origin to serve a single-page web app. After a release, CloudFront cache hit ratio dropped sharply. The app requests the same static JS and CSS assets, but each request includes a unique tracking query parameter (for example, ?utm_source=campaign123, campaign456, etc.). You want CloudFront to cache those assets efficiently even when the tracking query parameter changes. What should you do?

Accepted Answer

Update the CloudFront cache policy so the cache key ignores the tracking query parameter, while still using the path and other essential headers.. Option B is correct because CloudFront's cache key determines whether a request is served from the cache or forwarded to the origin. By configuring a cache policy that ignores the tracking query parameter (e.g., utm_source), CloudFront treats all requests for the same asset path as identical, regardless of the unique tracking parameter. This allows the same JS and CSS files to be cached once and served for all campaign variations, restoring the cache hit ratio.

Answer

Create a cache policy that forwards the query string to the origin and varies the cache key by all query parameters.

Answer

Enable S3 origin access control and keep the existing default cache policy, because origin access changes caching behavior automatically.

Answer

Set the CloudFront Time-to-Live (TTL) to 0 seconds to ensure the origin always serves the latest asset content.

Question 11

You run a web application on an EC2 Auto Scaling group behind an Application Load Balancer (ALB). During scheduled traffic spikes, new instances launch but customers occasionally see 5xx errors for the first few minutes after scale-out. Operational logs show instances need ~4 minutes to warm up (load caches and initialize dependencies). ALB target health becomes healthy only after this warm-up. Which change most directly improves performance during spikes by reducing the time to serve traffic after scaling?

Accepted Answer

Use an Auto Scaling warm pool so instances are pre-initialized and ready to register quickly when the ASG scales out.. B is correct because a warm pool pre-initializes instances (e.g., loading caches and dependencies) before they are added to the Auto Scaling group. When the ASG scales out, these pre-warmed instances can be quickly moved into service, bypassing the ~4-minute warm-up delay and reducing the window for 5xx errors.

Answer

Configure a larger ALB deregistration delay so that old targets remain longer before termination.

Answer

Increase the number of desired instances immediately without using scaling policies, and then rely on manual reconfiguration.

Answer

Switch from ALB to NLB so instances become reachable sooner without waiting for health checks.

Question 12

A data processing application runs on a single EC2 instance and needs persistent block storage with sustained low-latency random read/write performance (high IOPS). Which storage choice is most appropriate?

Accepted Answer

EBS io2 provisioned IOPS SSD. Amazon EBS io2 Provisioned IOPS SSD volumes are designed for I/O-intensive workloads that require sustained, low-latency random read/write performance with high IOPS. They provide consistent performance by allowing you to provision a specific IOPS level (up to 256,000 IOPS per volume) and offer a 99.999% durability guarantee, making them ideal for a single EC2 instance needing persistent block storage.

Answer

Amazon S3 Standard

Answer

Amazon EFS for POSIX file sharing between multiple instances

Answer

EBS Throughput Optimized HDD (st1) storage

Question 13

A trading analytics system deploys multiple EC2 instances that exchange very frequent, low-latency, east-west messages. The application team wants the instances to be placed to minimize network latency and variability. Which AWS feature should they use?

Accepted Answer

EC2 Placement Groups with the "cluster" strategy. The cluster placement group is the correct choice because it places instances into a low-latency, high-bandwidth group within a single Availability Zone, which minimizes network latency and variability for east-west traffic. This strategy is specifically designed for applications that require very frequent, low-latency communication between instances, such as trading analytics systems.

Answer

EC2 Placement Groups with the "spread" strategy

Answer

Auto Scaling cooldown adjustments only

Answer

Switching the instances to a larger instance size without any placement group

Question 14

Your team serves static JavaScript and CSS files from an S3 origin through CloudFront. After a release, the CloudFront cache hit ratio dropped because clients keep re-downloading the same assets. What is the best next change to improve caching performance?

Accepted Answer

Update origin responses to include long-lived Cache-Control headers (for example, max-age) so CloudFront can cache objects. Option A is correct because setting long-lived Cache-Control headers (e.g., max-age=31536000) on static assets tells CloudFront to cache them at edge locations for an extended period. This reduces the number of requests forwarded to the S3 origin, improving the cache hit ratio and preventing clients from re-downloading unchanged assets on every visit.

Answer

Switch the S3 bucket to S3 Glacier so objects are not frequently accessed

Answer

Disable CloudFront compression to reduce CPU usage at the edge

Answer

Set CloudFront to forward all query strings to the origin to ensure the latest assets are returned

Question 15

A DynamoDB-backed multi-tenant app experiences throttling during a promotion. Most writes and reads target tenant "ACME" and use the same partition key value, causing a hot partition. Which design change most directly improves performance?

Accepted Answer

Add a "shard" component to the partition key (for example, tenantId + hashed bucket) to spread traffic across partitions. Option A is correct because adding a shard component to the partition key (e.g., appending a random or hash-based suffix to the tenant ID) distributes writes and reads for the same tenant across multiple physical partitions. This directly alleviates the hot partition caused by all ACME traffic hitting a single partition key value, allowing DynamoDB to utilize its full provisioned throughput across partitions.

Answer

Increase the table’s read capacity without changing the partition key

Answer

Switch all reads to strongly consistent reads to guarantee faster results

Answer

Store ACME data in S3 and query it directly to avoid DynamoDB throttling