SAA-C03 Design High-Performing Architectures practice questions

Design High-Performing Architectures is 24% of the AWS Certified Solutions Architect – Associate (SAA-C03) exam, the third-heaviest of its 4 domains: roughly 12 of the 50 scored questions, and every 10% of accuracy here moves your scaled score about 24 points. Below are 8 free questions from this domain only, each with the answer explained and why every wrong option misses. Last updated 2026-07-03.

Every question is verified against official AWS documentation, linked below each answer.

  1. Question 1Design High-Performing Architectures

    A company serves a high-traffic public API using Amazon API Gateway and Lambda. The API responses change only once every hour but are identical for all users. API Gateway metrics show 80% of invocations are redundant re-computations. What is the simplest way to reduce Lambda invocations and improve response latency?

    Reveal answer & explanation

    Answer: A. Enable API Gateway stage-level caching with an appropriate TTL

    API Gateway has a built-in stage-level response cache. With a TTL matching how often the data actually changes, repeated identical requests are served straight from the cache without invoking Lambda at all, cutting both latency and compute cost.

    Go deeper: Why does caching at the API Gateway stage level save more than caching inside the Lambda function? What determines the right TTL to set for the cache?

    Source: official AWS documentation · verified 2026-07-03

  2. Question 2Design High-Performing Architectures

    A company runs a MySQL workload on Amazon RDS. The database is read-heavy with 95% SELECT queries. The engineering team wants to offload read traffic with the least amount of infrastructure management. Which approach is most appropriate?

    Reveal answer & explanation

    Answer: A. Create an RDS Read Replica and point read traffic to its endpoint

    An RDS Read Replica asynchronously replicates from the primary and exposes its own read endpoint, so SELECT traffic can be routed there directly, offloading the primary — with minimal infrastructure to manage.

    Go deeper: Why can't the Multi-AZ standby be used to serve read traffic? What tradeoff comes with a read replica's asynchronous replication?

    Source: official AWS documentation · verified 2026-07-03

  3. Question 3Design High-Performing Architectures

    A company runs a containerized application on Amazon EKS. The application processes large scientific datasets by reading and writing to shared storage simultaneously across dozens of pods. The workload requires consistent sub-millisecond storage latency and throughput of several GB/s. Which storage solution is most appropriate?

    Reveal answer & explanation

    Answer: B. Amazon FSx for Lustre integrated with S3

    FSx for Lustre is a high-performance parallel file system built for HPC and ML workloads. It delivers sub-millisecond latency and throughput in the hundreds of GB/s, can be mounted concurrently by many pods at once, and natively integrates with S3 for loading and offloading data.

    Go deeper: Why does a shared, multi-pod workload rule out EBS as a storage option? What makes a parallel file system different from a general-purpose network file system like EFS?

    Source: official AWS documentation · verified 2026-07-03

  4. Question 4Design High-Performing Architectures

    An online travel booking application queries Amazon Aurora PostgreSQL for available flights. Each search query joins five tables and returns results in 2–4 seconds. The marketing team runs the same searches repeatedly for popular routes. The engineering team wants to serve popular route searches in under 100 ms without rewriting the complex SQL queries. Which solution achieves this?

    Reveal answer & explanation

    Answer: A. Add an Amazon ElastiCache for Redis cluster and cache query result sets keyed by route parameters

    Caching the result sets for popular route searches in Redis, keyed by the route parameters, lets repeated identical searches be served from memory in under a millisecond — entirely bypassing the expensive five-table join in Aurora — without touching the SQL.

    Go deeper: Why does caching the whole result set beat caching individual rows for a five-table join? What determines a good cache key for this kind of query?

    Source: official AWS documentation · verified 2026-07-03

  5. Question 5Design High-Performing Architectures

    A company runs a batch analytics workload that reads 50 TB of compressed CSV files from Amazon S3 using Amazon Athena. Queries scan entire datasets every run and take over 30 minutes, primarily because of format overhead. The data schema is fixed and rarely changes. Which single change most dramatically reduces query run time and data scanned?

    Reveal answer & explanation

    Answer: A. Convert the CSV files to Apache Parquet format partitioned by date and store them back in S3

    Converting the CSV files to columnar Parquet lets Athena read only the needed columns and apply predicate pushdown and compression, while partitioning by date lets it skip irrelevant S3 prefixes entirely. Together these typically cut both query time and data scanned by 80-90%.

    Go deeper: Why does row format (CSV) force Athena to scan more data than a columnar format? How does partitioning let a query engine skip data before scanning it?

    Source: official AWS documentation · verified 2026-07-03

  6. Question 6Design High-Performing Architectures

    A company processes clickstream events using an Amazon Kinesis Data Streams consumer running on EC2. The consumer calls GetRecords every second and processes records in batches. As throughput scales, the team observes that record processing lag grows and CPU utilization is low. Investigation reveals the consumer is spending most of its time waiting on GetRecords API responses. Which change best improves consumer throughput?

    Reveal answer & explanation

    Answer: A. Switch from the classic GetRecords polling model to the Kinesis Enhanced Fan-Out (EFO) feature with SubscribeToShard

    Enhanced Fan-Out uses HTTP/2 server push via SubscribeToShard to deliver records to each registered consumer at up to 2 MB/s per shard as soon as they arrive, eliminating the polling overhead and the shared 2 MB/s read limit that classic GetRecords consumers contend for on a shard.

    Go deeper: Why do classic GetRecords consumers on the same shard have to share a 2 MB/s read limit? What does 'server push' change about how quickly a consumer sees new records?

    Source: official AWS documentation · verified 2026-07-03

  7. Question 7Design High-Performing Architectures

    A financial services firm runs a latency-sensitive order matching engine on Amazon EC2. The application requires predictable, consistent network performance and the lowest possible packet loss between instances in the same region. The team notices occasional latency spikes they attribute to network jitter from co-hosted workloads. Which EC2 feature most directly addresses this?

    Reveal answer & explanation

    Answer: B. Use a placement group with Cluster strategy

    A Cluster placement group packs instances physically close together within a single Availability Zone on the same high-bisection-bandwidth rack fabric, which is the primary mechanism for minimizing round-trip time and jitter between co-located instances.

    Go deeper: Why does physical proximity between instances reduce network jitter? What's the difference between reducing CPU overhead on one instance versus reducing distance between two instances?

    Source: official AWS documentation · verified 2026-07-03

  8. Question 8Design High-Performing Architectures

    A company wants to accelerate DNS resolution for an application that makes thousands of DNS queries per minute to external domains. The application runs on EC2 instances in a VPC. Which AWS feature provides the fastest DNS resolution for queries within the VPC?

    Reveal answer & explanation

    Answer: B. Use the Amazon Route 53 Resolver (VPC+2 resolver) already available at the VPC base address

    The Route 53 Resolver is built into every VPC at the reserved IP address (VPC CIDR base + 2). It forwards external queries to authoritative servers with typically single-digit millisecond latency and requires no deployment or management.

    Go deeper: Why is a resolver built into the VPC faster than one reached over the internet? What's the tradeoff of running your own DNS server on EC2 instead of using the built-in resolver?

    Source: official AWS documentation · verified 2026-07-03

More SAA-C03 practice