SAA-C03 Design High-Performing Architectures Practice Test 7 — 15 Questions

Question 1

A service performs many repeated read requests for the same DynamoDB items. The reads are latency-sensitive, but the application can tolerate slightly stale data. Which AWS service is the best fit to reduce read latency?

Accepted Answer

Amazon DAX (DynamoDB Accelerator). Amazon DAX (DynamoDB Accelerator) is an in-memory cache specifically designed for DynamoDB. It reduces read latency from single-digit milliseconds to microseconds by caching frequently accessed items, and it supports eventually consistent reads, which aligns with the application's tolerance for slightly stale data. DAX handles repeated read requests without additional DynamoDB read capacity unit consumption, making it the optimal choice for this latency-sensitive workload.

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Amazon S3 Select

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Amazon SQS FIFO queue

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AWS Lambda provisioned concurrency

Question 2

An application uses an Amazon Aurora cluster. The workload becomes read-heavy, but the team cannot change the database schema. They need higher read throughput while keeping writes on the primary. What should they do?

Accepted Answer

Create Aurora read replicas and use the reader endpoint for read traffic. Aurora read replicas are designed to offload read traffic from the primary instance, and the reader endpoint automatically load-balances connections across all replicas. Since the workload is read-heavy and the schema cannot change, adding read replicas directly increases read throughput without modifying the application's database schema. The reader endpoint ensures that read queries are directed to the replicas while writes continue to hit the primary instance.

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Switch the cluster to a single-AZ Aurora configuration to reduce coordination overhead

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Increase DynamoDB capacity units instead of modifying the database layer

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Enable CloudFront caching for database queries to serve results from edge locations

Question 3

A latency-sensitive API is implemented with AWS Lambda. During traffic ramp-ups, users sometimes experience slow responses due to cold starts. The team wants to ensure fast initialization for a baseline level of concurrent requests. Which AWS feature should they use?

Accepted Answer

Lambda provisioned concurrency. Lambda Provisioned Concurrency keeps a specified number of execution environments initialized and ready to respond immediately, eliminating cold starts for those concurrent requests. This directly addresses the latency-sensitive API requirement during traffic ramp-ups by ensuring fast initialization for a baseline level of concurrency.

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Increase reserved instances for EC2

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Enable S3 event notifications for every request to the API

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Decrease the function timeout to reduce execution variability

Question 4

A distributed system needs extremely low network latency between a set of EC2 instances running the same workload. The team wants the instances to be placed as close together as AWS allows to reduce round-trip time. Which placement strategy should the architect use?

Accepted Answer

Use a Cluster placement group for the instances that must communicate frequently over low latency.. A Cluster placement group is the correct choice because it places instances in a single Availability Zone within the same rack or logical cluster, providing the lowest possible network latency and maximum throughput (up to 10 Gbps for single-flow traffic) between instances. This is ideal for tightly coupled, latency-sensitive workloads like HPC or real-time distributed systems.

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Use a Spread placement group across multiple Availability Zones to maximize fault tolerance.

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Use the default placement strategy without specifying a placement group.

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Use a placement group of type Partition to ensure independent failure of each instance.

Question 5

A web API runs on an Auto Scaling group (ASG) behind an Application Load Balancer (ALB). During traffic spikes, users experience request timeouts even though CPU stays below 40%. After investigation, you find the ASG often has too few healthy targets to handle the current request rate. Which change will best improve responsiveness during spikes?

Accepted Answer

Create a target tracking scaling policy using an ALB metric such as RequestCountPerTarget or TargetResponseTime.. Option B is correct because the issue is that the ASG has too few healthy targets to handle the request rate, even though CPU is low. A target tracking scaling policy based on RequestCountPerTarget or TargetResponseTime directly aligns scaling with the ALB's view of demand, ensuring the ASG adds instances when request rates spike, regardless of CPU utilization. This addresses the root cause—insufficient capacity to serve incoming requests—rather than relying on a metric (CPU) that does not reflect the bottleneck.

Answer

Keep the ASG scaling policy based on CPU utilization, but increase the ASG min capacity by 50%.

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Enable EC2 detailed monitoring for one-minute granularity and keep CPU scaling.

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Switch to scaling based on the ASG network out bytes metric only, ignoring ALB response metrics.

Question 6

A DynamoDB-backed event processing system experiences throttling during a promotion. All events are written and read using the same partition key value (tenantId = "ACME"). The workload is time-ordered per tenant, and the application can tolerate slight reordering across partitions. Which design change will most directly increase throughput and reduce hot-partition throttling?

Accepted Answer

Change the partition key to include an additional sharding attribute derived from a hash of eventId.. Option B is correct because adding a sharding attribute derived from a hash of eventId allows writes and reads to be distributed across multiple partition keys, breaking the single hot partition caused by using tenantId='ACME' for all operations. DynamoDB's throughput is limited per partition, so distributing the load across many partitions directly reduces throttling without changing the application's tolerance for slight reordering.

Answer

Increase the table's provisioned capacity (read/write units) to handle the promotion peak.

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Enable DAX caching for all reads but keep the same partition key and item layout.

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Switch the table to eventually consistent reads for queries to lower read throttling.

Question 7

A team serves static assets from an S3 origin through CloudFront. Cache hit ratio is low. Analytics show that requests include an Authorization header (even though the assets are public) and the cache key currently varies on that header, causing CloudFront to treat the same asset as different cache entries. What is the best change to improve cache hit ratio without breaking access controls?

Accepted Answer

Modify the CloudFront cache policy so the cache key does not include the Authorization header.. The low cache hit ratio is caused by the Authorization header being included in the CloudFront cache key, which creates separate cache entries for the same object even though the assets are public. By modifying the cache policy to exclude the Authorization header, CloudFront will treat all requests for the same asset as identical, dramatically improving the cache hit ratio without affecting access controls because the assets are already public.

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Keep Authorization in the CloudFront cache key, but increase the origin response minimum TTL to 1 day.

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Switch the S3 origin from the current bucket to a website endpoint to enable automatic caching headers.

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Enable CloudFront to forward all headers to S3 so origin can decide caching behavior per request.

Question 8

A containerized service fleet running on EC2 instances needs to share user-uploaded files and access them with low latency. The workload is bursty: sometimes dozens of instances concurrently read the same directory for short periods, and then traffic drops. Which Amazon EFS configuration best matches these performance needs?

Accepted Answer

Use Amazon EFS General Purpose performance mode and Throughput mode set to Bursting.. Option A is correct because the workload is bursty with concurrent reads of the same directory, which favors the General Purpose performance mode for its strong consistency and lower latency per operation. The Bursting Throughput mode is ideal for bursty traffic as it allows the file system to accumulate burst credits during idle periods and consume them during high-demand spikes, matching the described pattern without incurring additional costs.

Answer

Use Amazon EFS Max I/O performance mode with Throughput mode set to Provisioned.

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Use Amazon EFS General Purpose performance mode with Throughput mode set to Provisioned.

Answer

Use Amazon EFS Max I/O performance mode with Throughput mode set to Bursting.

Question 9

A media platform runs a CPU-heavy thumbnail generation workload on an EC2 Auto Scaling group using t3.large instances. During peak traffic, p95 processing time increases significantly even though average CPU remains around 40–50%. CloudWatch also shows CPU credit depletion behavior. Which change will most directly improve performance predictability for this workload?

Accepted Answer

Change the Auto Scaling group instance type to a compute-optimized family (for example, c7i) to provide steady CPU performance.. The t3.large instances rely on CPU credits for burst performance, and when credits are exhausted, CPU performance is throttled to the baseline (e.g., 30% for t3.large). This causes unpredictable processing times during peak traffic, even if average CPU is moderate. Switching to a compute-optimized family like c7i provides dedicated, consistent CPU performance without credit-based throttling, directly improving predictability for CPU-heavy thumbnail generation.

Answer

Increase the t3.large maximum CPU credits and keep the Auto Scaling group using the same burstable instance type.

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Add a placement group to the existing t3.large instances so they are packed close together for lower latency between nodes.

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Switch the workload to run on Lambda with the same logic so invocations automatically scale without instance selection changes.

Question 10

Your company needs a high-throughput, low-latency TCP service using a custom binary protocol. Requirements: preserve the original client source IP for rate limiting, keep latency minimal, and use TCP health checks. The current setup uses an Application Load Balancer and performance is inconsistent. Which load balancer choice best meets these requirements?

Accepted Answer

Use a Network Load Balancer (NLB) with TCP listeners so traffic stays at Layer 4 and the original source IP is preserved.. A Network Load Balancer (NLB) operates at Layer 4 and preserves the original client source IP by default, which is essential for accurate rate limiting. Its TCP listeners provide low-latency, high-throughput handling of custom binary protocols, and it supports TCP health checks natively. This directly addresses the performance inconsistency seen with the Application Load Balancer, which operates at Layer 7 and introduces additional processing overhead.

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Keep the Application Load Balancer (ALB), because ALBs also preserve client source IP for TCP protocols.

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Use Amazon API Gateway because it preserves client source IP and provides TCP health checks for all protocols.

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Use Amazon CloudFront with an S3 origin, because CloudFront reduces latency for TCP-based protocols.

Question 11

A site serves static assets (JS/CSS) through CloudFront from an S3 origin. After a recent frontend change, CloudFront shows a cache hit ratio below 20%. In CloudFront access logs, requests to the same asset URL path differ by a query parameter named rnd (a random value appended by the app on every request). The origin content is identical regardless of rnd. What is the best CloudFront configuration change to restore effective caching?

Accepted Answer

Create a custom CloudFront Cache Policy that does not include the rnd query parameter in the cache key (whitelist only required parameters, or forward no query strings).. The rnd query parameter makes each request appear unique to CloudFront, causing a cache miss for every request even though the underlying content is identical. By creating a custom cache policy that either forwards no query strings or whitelists only required parameters, CloudFront will ignore the rnd parameter when computing the cache key, allowing it to serve cached responses and dramatically improve the cache hit ratio.

Answer

Increase the origin response Cache-Control max-age header on S3 so CloudFront caches longer even with different rnd values.

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Disable compression on CloudFront so the response body is identical byte-for-byte and cache hits improve.

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Switch the origin from S3 to an ALB so CloudFront can cache based on ALB target health checks instead of the query string.

Question 12

An event ingestion service writes to a DynamoDB table where the partition key is tenantId and the sort key is eventTime. During a campaign, one tenant generates a disproportionate share of traffic, causing write throttling and increased latency for that tenant’s writes. You can change the data model and application queries, but you must still efficiently retrieve events for a tenant for the last 10 minutes. Which change best improves write throughput by reducing hot partitions?

Accepted Answer

Add a shard attribute to the partition key (partition key = tenantId#shard, where shard is randomly selected from a fixed range). Query all shards for the tenant for eventTime values in the last 10 minutes, then merge results in the application.. Option B is correct because it distributes writes for a hot tenant across multiple partitions by appending a random shard suffix to the tenantId partition key. This eliminates a single hot partition, allowing DynamoDB to scale write capacity horizontally. The application can then query all shards for the last 10 minutes and merge results, satisfying the retrieval requirement.

Answer

Keep tenantId as the partition key and rely on DynamoDB adaptive capacity to automatically remove all throttling.

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Change the sort key to eventTimeBucket (for example, eventTime rounded to 1-minute buckets) while keeping the partition key as tenantId.

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Enable DAX and use it for write operations so throttled writes are served from cache instead of reaching DynamoDB.

Question 13

A media company serves versioned JavaScript and CSS from an S3 origin through CloudFront. After a release, the cache hit ratio drops because the SPA sends an Authorization header and several tracking query strings on every request, even though the assets are public and identical for all users. Which changes would most improve cache efficiency without changing the content returned? Select three.

Accepted Answer

Create a CloudFront cache policy that excludes the Authorization header from the cache key when the assets do not require per-user authorization.. Option A is correct because CloudFront's default behavior includes the Authorization header in the cache key, causing unique cache entries for each user even when the content is public. By creating a cache policy that excludes the Authorization header, CloudFront treats all requests for the same object as identical, dramatically improving the cache hit ratio without affecting the content served.

Answer

Place the S3 origin behind an Application Load Balancer so CloudFront can reuse more cached responses.

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Enable S3 Transfer Acceleration to increase the cache hit ratio for repeated browser requests.

Question 14

An event-ingestion application writes telemetry to DynamoDB with partition key tenantId and sort key eventTime. During a promotion, one tenant generates 10 times the normal traffic. Dashboards repeatedly query the most recent items for that tenant, and they can tolerate slightly stale data. Which changes would most effectively reduce throttling and improve responsiveness? Select three.

Accepted Answer

Introduce a sharded partition key for the hot tenant and query the small shard set when reading recent data.. Option A is correct because introducing a sharded partition key (e.g., tenantId#shardId) for the hot tenant spreads its write traffic across multiple physical partitions, reducing throttling. When querying recent data, the dashboard can read from a small, fixed set of shards (e.g., 10 shards) and merge results, which is efficient and tolerates the slight staleness. This pattern directly addresses the hot partition issue without requiring application-level aggregation.

Answer

Increase only the sort-key cardinality while leaving the partition key unchanged.

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Move the table to the Standard-IA table class because throttling is usually caused by storage class selection.

Question 15

A rendering service runs on a single EC2 instance and writes a large working set of metadata to disk using sustained random reads and writes. The data must persist across stops and restarts, and the team sees queue depth spikes when the job reaches peak throughput. Which changes should the team make? Select three.

Accepted Answer

Use an Amazon EBS io2 volume with provisioned IOPS for the metadata store.. Option A is correct because an Amazon EBS io2 volume with provisioned IOPS is designed for I/O-intensive workloads with sustained random reads and writes, such as the rendering service's metadata store. The io2 volume type offers high durability (99.999%) and consistent low-latency performance, which directly addresses the queue depth spikes caused by peak throughput demands. Provisioned IOPS ensures the volume can handle the required random I/O without throttling, meeting the persistence requirement across stops and restarts.

Answer

Move the working set to Amazon EFS because it automatically stripes across Availability Zones.

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Store the metadata in Amazon S3 because object storage is cheaper and supports random writes.