SAA-C03 Design High-Performing Architectures Practice Test 5 — 15 Questions

Question 1

Multiple EC2 instances need a shared filesystem so they can concurrently read and write the same files (for example, user uploads and rendered assets). The instances are in different Availability Zones and must mount the filesystem using NFS. Which AWS storage service best fits?

Choose an answer to begin — your selection is scored in the full session.

Accepted Answer

Amazon EFS. Amazon EFS is a fully managed NFS file system that supports concurrent read/write access from multiple EC2 instances across different Availability Zones. It uses the NFSv4.1 protocol, making it the only AWS storage service that provides a shared POSIX-compliant filesystem mountable via NFS for multi-AZ workloads.

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Amazon S3

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Amazon EBS gp3 volumes

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Instance store on EC2

Question 2

A latency-sensitive trading workload runs on 6 EC2 instances. You must distribute the instances so they do NOT share the same underlying hardware rack, reducing the risk of correlated rack-level faults. Which EC2 placement group strategy best meets this requirement?

Accepted Answer

Spread placement group. A Spread placement group is the correct choice because it ensures each EC2 instance is placed on distinct underlying hardware (different racks), eliminating shared fault domains. This directly meets the requirement to avoid correlated rack-level failures for latency-sensitive trading workloads.

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Cluster placement group

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Partition placement group

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No placement group, rely on the default scheduler

Question 3

A team wants to run containerized services with AWS-managed orchestration and autoscaling. They do NOT require Kubernetes compatibility. Which AWS service choice is most appropriate to meet these goals?

Accepted Answer

Amazon ECS. Amazon ECS is the most appropriate choice because it provides AWS-managed container orchestration and autoscaling without requiring Kubernetes compatibility. ECS integrates natively with AWS services like Application Auto Scaling and CloudWatch to automatically scale container tasks based on metrics such as CPU or memory utilization, meeting the team's requirements directly.

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Amazon EKS

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An EC2 Auto Scaling group only

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Amazon SQS as the compute layer

Question 4

Your web application runs on EC2 instances behind an Application Load Balancer (ALB). During traffic spikes, p95 response time increases, but average CPU utilization remains below 40%. The current Auto Scaling policy scales based on average CPU%. What should you change to improve performance during spikes?

Accepted Answer

Scale on a request-driven metric such as ALB RequestCount per target (or target-group request rate). The p95 response time is increasing during traffic spikes while CPU utilization remains low, indicating that the bottleneck is not compute capacity but rather request handling or connection overhead. By scaling on ALB RequestCountPerTarget, you directly target the metric causing latency—each target's request load—rather than an indirect metric like CPU. This ensures that new instances are launched precisely when individual targets are overwhelmed by requests, reducing queueing delays and improving response times.

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Keep scaling on CPU% to avoid over-scaling

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Disable scaling and manually increase capacity during business hours

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Scale only when network packet drops fall below a threshold

Question 5

An application repeatedly reads the same DynamoDB items with very low latency requirements. The application can tolerate slightly stale data (for example, within a few seconds). You want to improve read latency without changing the existing DynamoDB table schema. Which service is the best choice?

Accepted Answer

Amazon DAX. Amazon DAX (DynamoDB Accelerator) is an in-memory cache that sits between your application and DynamoDB, providing microsecond read latency for frequently accessed items. Since the application can tolerate slightly stale data (within seconds), DAX's default TTL-based caching is ideal because it reduces read pressure on DynamoDB while serving cached results with significantly lower latency than direct DynamoDB reads.

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Amazon S3 Transfer Acceleration

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Amazon EFS

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AWS CloudTrail for data plane reads

Question 6

A web application uses an Amazon Aurora DB cluster. The workload is becoming read-heavy, and the application team wants to increase read throughput without changing the database schema. They can adjust the application to route reads differently. What should they do?

Accepted Answer

Add Aurora read replicas and route read queries to the cluster reader endpoint. Adding Aurora read replicas and routing read queries to the cluster reader endpoint is the correct approach because Aurora replicas share the same underlying storage volume as the primary instance, so they can serve read traffic with minimal replication lag. The reader endpoint automatically load-balances connections across all available replicas, increasing aggregate read throughput without requiring any schema changes.

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Switch the cluster to Multi-AZ with a longer failover target clock

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Move all reads to the writer endpoint to reduce connection overhead

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Disable automated backups to reduce storage overhead and speed reads

Question 7

A multi-tenant event system writes and reads data in DynamoDB. One tenant generates most of the traffic, causing throttling on a single partition key value, and the dashboards repeatedly read the most recent items for that tenant. Which two changes should the team make to improve performance? Select two.

Accepted Answer

Shard the hot tenant’s writes across multiple partition key values so traffic is spread across partitions.. Sharding the hot tenant's writes across multiple partition key values (e.g., by appending a random suffix or a timestamp-based suffix) distributes the write traffic across multiple physical partitions, avoiding throttling on a single partition. This is a common DynamoDB design pattern for mitigating hot keys, as each partition has its own throughput limits (e.g., up to 3,000 RCU or 1,000 WCU for a partition). By spreading writes, the system can achieve higher aggregate throughput without hitting per-partition limits.

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Switch the table to on-demand capacity mode so DynamoDB automatically removes partition limits.

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Use a larger sort key attribute to increase the maximum write throughput for the tenant.

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Move the table to a single larger provisioned throughput setting and keep the same key design.

Question 8

A distributed analytics platform runs on 12 EC2 instances in one Availability Zone. The nodes exchange a very high volume of east-west messages and the team wants the lowest possible network latency between instances. Which two changes should the architect make first? Select two.

Accepted Answer

Place the instances in a cluster placement group so AWS keeps them physically close together.. A cluster placement group is the correct choice because it ensures EC2 instances are placed in a single Availability Zone and are physically close together, which minimizes network latency and maximizes throughput for high-volume east-west traffic. This is the lowest-latency placement group option available, as it groups instances within a single rack or cluster of racks, reducing the number of network hops.

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Spread the instances across multiple Availability Zones to reduce the chance of correlated failure.

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Use a spread placement group so each instance lands on different underlying hardware.

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Move the workload to burstable T-series instances to absorb short traffic spikes economically.

Question 9

A marketing site serves versioned JavaScript and CSS from an Amazon S3 origin through Amazon CloudFront. After each release, the cache hit ratio drops sharply because clients keep sending request headers and query strings that are not needed for asset retrieval. Which two changes should improve cache efficiency the most? Select two.

Accepted Answer

Create a CloudFront cache policy that excludes unnecessary headers, query strings, and cookies from the cache key.. CloudFront cache policies allow you to explicitly control which headers, query strings, and cookies are included in the cache key. By excluding unnecessary ones (e.g., User-Agent, random query parameters), you prevent cache fragmentation and ensure that identical assets served with different request metadata map to the same cached object, dramatically improving the cache hit ratio.

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Move the S3 origin behind an Application Load Balancer so CloudFront can cache responses more effectively.

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Store the objects in Amazon S3 Standard-IA so repeated requests are cheaper.

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Lower the CloudFront TTL to zero so viewers always receive the newest content immediately.

Question 10

A web application uses an Amazon Aurora DB cluster for a read-heavy workload. The team wants to increase read throughput without changing the database schema or rewriting application data access patterns. Which two changes should they make? Select two.

Accepted Answer

Add Aurora Replicas to scale out read traffic across multiple database instances.. Adding Aurora Replicas (Option A) directly increases read throughput by distributing read-only queries across multiple database instances, which is ideal for a read-heavy workload. Sending read queries to the Aurora reader endpoint (Option B) ensures that these queries are load-balanced across all available replicas, offloading the writer instance and improving overall performance without requiring schema or application changes.

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Point all queries to the writer endpoint so Aurora can balance reads and writes internally.

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Enable Multi-AZ standby for the cluster to increase the number of read-only connections.

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Move the database to a single larger instance class instead of adding replicas.

Question 11

A CPU-bound batch rendering service runs on EC2. The application is Linux-based, compatible with ARM64, and the team wants the best throughput per dollar without changing the workload's architecture. Which two instance-family choices should the team consider first? Select two.

Accepted Answer

A compute-optimized family, because it is designed for workloads that spend most of their time on CPU.. Compute-optimized families (e.g., C5, C6g) are designed for workloads that spend most of their time on CPU, such as batch rendering. Option B is correct because Graviton-based instances (e.g., C6g, M6g) use ARM64 architecture, which is compatible with the workload and often delivers better price-performance for compute-intensive tasks, maximizing throughput per dollar without architectural changes.

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A memory-optimized family, because extra RAM always increases compute throughput.

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A storage-optimized family, because local storage bandwidth is the main factor for rendering performance.

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A burstable family, because CPU credits make sustained rendering faster during long runs.

Question 12

A single EC2 instance hosts a low-latency database cache that writes a large random working set to block storage. The application needs sustained high IOPS and low latency, and the storage must remain attached to the instance while it runs. Which two design choices best meet the requirement? Select two.

Accepted Answer

Use an io2 Block Express EBS volume for the highest sustained IOPS and low-latency performance.. Io2 Block Express EBS volumes are designed for mission-critical workloads requiring sustained high IOPS and low latency. They offer up to 256,000 IOPS per volume with sub-millisecond latency, making them ideal for a low-latency database cache that writes a large random working set to block storage. The storage remains attached to the EC2 instance while it runs, meeting the requirement for persistent block-level storage.

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Use an S3 bucket as the backing store because object storage scales automatically.

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Choose a cold HDD-based volume so the cache has durable low-cost storage.

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Use the root volume from a T-series instance because burst credits can absorb the write spikes.

Question 13

Based on the exhibit, a media company serves versioned JavaScript and CSS files from an Amazon S3 origin through CloudFront. After a frontend release, the cache hit ratio dropped sharply even though the file names are versioned. The application team says the browser requests include the same Authorization header on every asset request because the frontend and API share one domain. What should the solutions architect do to improve CloudFront cache hit ratio without changing the application authentication model for the API?

Accepted Answer

Create a CloudFront cache policy that excludes Authorization, cookies, and unnecessary query strings from the cache key.. The sharp drop in cache hit ratio is caused by the Authorization header being included in the cache key, which makes each request unique even though the file names are versioned. By creating a CloudFront cache policy that excludes the Authorization header (and unnecessary cookies/query strings) from the cache key, CloudFront can serve cached responses to requests with different Authorization headers, restoring the cache hit ratio without altering the application's authentication model for the API.

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Enable S3 Transfer Acceleration on the bucket so CloudFront fetches objects faster from the origin.

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Switch the origin from S3 to an Application Load Balancer so CloudFront can cache dynamic responses more effectively.

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Configure CloudFront to forward every viewer header to the origin so the origin can decide whether the content is cacheable.

Question 14

Based on the exhibit, a retail analytics service repeatedly reads the same DynamoDB items during an active campaign. The business can tolerate data that is a few seconds stale, but the application must minimize latency and reduce pressure on DynamoDB. A load test shows that 80% of reads target only 200 item keys. What should the solutions architect implement?

Accepted Answer

Add a DynamoDB Accelerator (DAX) cluster in front of the table and point the application to the DAX endpoint.. DynamoDB Accelerator (DAX) is a fully managed, highly available, in-memory cache designed specifically for DynamoDB. It reduces read latency from single-digit milliseconds to microseconds and offloads repeated reads from the table, which directly addresses the requirement to minimize latency and reduce pressure on DynamoDB. Since the business can tolerate stale data (DAX default TTL is 5 minutes, but can be configured lower), DAX is ideal for the 80% of reads hitting only 200 hot keys.

Answer

Switch the table to provisioned capacity with auto scaling so DynamoDB can handle the repeated reads more efficiently.

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Create a global table in a second Region and read from the replica Region to lower latency.

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Move the hot items into Amazon ElastiCache for Redis and keep the remaining data in DynamoDB.

Question 15

Based on the exhibit, a serverless checkout API is implemented in AWS Lambda and deployed in one Region. The function has a cold-start time of 700-900 ms on the first request after idle periods. Marketing launches a predictable traffic spike every weekday at 09:00 UTC, and the p95 latency target is under 150 ms during the first five minutes of the spike. What should the solutions architect do to meet the latency target while controlling cost?

Accepted Answer

Configure provisioned concurrency and scale it up before the predictable spike begins.. Provisioned concurrency pre-warms a specified number of execution environments so that the Lambda function has zero cold-start latency when invoked. By scheduling the provisioned concurrency to scale up before the 09:00 UTC spike, the function can serve the first requests within the 150 ms p95 latency target, while the scheduled scaling down after the spike controls cost by releasing unused capacity.

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Increase the Lambda memory size and leave concurrency at the default value.

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Put the Lambda function behind an Application Load Balancer so the load balancer absorbs the initialization delay.

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Set reserved concurrency to the expected peak so Lambda will pre-create execution environments.