SAA-C03 Design High-Performing Architectures Practice Test 11 — 15 Questions

Question 1

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?

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

Accepted Answer

Change the partition key to a salted key such as customerId + shard number, and include the eventTime ordering using the sort key.. 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.

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

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

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

Question 2

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.. 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.

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

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

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

Question 3

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.

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

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

Question 4

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

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Amazon EFS for POSIX file sharing between multiple instances

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EBS Throughput Optimized HDD (st1) storage

Question 5

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.

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EC2 Placement Groups with the "spread" strategy

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Auto Scaling cooldown adjustments only

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

Question 6

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. 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.

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

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Disable CloudFront compression to reduce CPU usage at the edge

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

Question 7

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. 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

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Switch all reads to strongly consistent reads to guarantee faster results

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

Question 8

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 9

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.

Answer

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

Answer

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

Question 10

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.

Answer

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 11

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.

Answer

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 12

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.. 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 13

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.. 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 14

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.

Answer

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 15

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.. 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.

Answer

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.