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

Question 1

A DynamoDB table stores device status items. The partition key is deviceId, and the partition distribution is healthy (no single partition dominates). However, during peak periods the application experiences high read latency because many clients repeatedly request the latest status for the same devices. Which action best improves read latency without changing the DynamoDB partitioning model?

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

Accepted Answer

Add Amazon DAX as a caching layer in front of DynamoDB and route repeated read operations through DAX.. Amazon DAX is a fully managed, in-memory cache for DynamoDB that provides microsecond read latency. By caching the results of repeated GetItem and Query requests for the same device status items, DAX offloads read traffic from the underlying DynamoDB table, reducing the number of read capacity units consumed and eliminating the latency caused by repeated fetches from disk. This directly addresses the high read latency during peak periods without altering the existing partition key or partitioning model.

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Change the partition key to a random value for each request to eliminate hot partitions.

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Increase write capacity only, because writes generally determine read latency in DynamoDB.

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Create an additional Global Secondary Index (GSI) and read exclusively from the index to accelerate reads.

Question 2

A media processing service runs ECS tasks in multiple Availability Zones. Each task must read and write the same shared filesystem with low latency because tasks stream intermediate artifacts to other tasks. The team currently mounts an EBS volume per task, and cross-AZ tasks frequently cannot see each other’s files. Which option best resolves the shared filesystem requirement while supporting high-performing access?

Accepted Answer

Use Amazon EFS with mount targets in each Availability Zone so all tasks mount a common NFS filesystem over the AWS network.. Amazon EFS provides a fully managed, shared NFS filesystem that can be mounted concurrently by ECS tasks across multiple Availability Zones with low latency. It supports POSIX file operations, making it ideal for streaming intermediate artifacts between tasks. EFS mount targets in each AZ ensure local access, meeting the requirement for high-performing shared storage.

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Keep using EBS, but attach the same EBS volume to tasks in multiple Availability Zones using EBS multi-attach so all tasks share the filesystem.

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Use Amazon S3 for the intermediate artifacts and rely on S3 event notifications to emulate POSIX file operations.

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Switch to instance store on each task and use SQS messages between tasks to copy intermediate artifacts.

Question 3

An order lookup API repeatedly reads the same few items from DynamoDB. The application can tolerate slightly stale data for a few seconds, and the team wants the lowest-latency design with minimal application changes. Which two changes should they make? Select two.

Accepted Answer

Put Amazon DynamoDB Accelerator (DAX) in front of the table.. Amazon DynamoDB Accelerator (DAX) is an in-memory cache for DynamoDB that provides microsecond read latency, which is ideal for repeated reads of the same few items. Since the application can tolerate slightly stale data, DAX's default write-through caching with a TTL of 5 minutes ensures low latency without requiring application code changes beyond adding the DAX client.

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Switch all access to strongly consistent reads for faster results.

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Increase the item size so fewer requests are needed.

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Replace the table with Amazon EBS volumes mounted on EC2 instances.

Question 4

A startup runs an HTTP/2 API that also supports WebSocket connections. They need path-based routing to separate microservices (for example, /api/* to Service A and /metrics/* to Service B) and want TLS terminated at the load balancer. Which AWS option best meets these requirements while maintaining high request performance?

Accepted Answer

Use an Amazon ALB with HTTP/2 support, WebSocket upgrades enabled, and listener rules for host/path-based routing.. An Application Load Balancer (ALB) natively supports HTTP/2, WebSocket upgrades, and path-based routing via listener rules. It terminates TLS at the load balancer, offloading encryption from backend services, and maintains high performance for both HTTP/2 and WebSocket traffic. This makes ALB the correct choice for the startup's requirements.

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Use an Amazon NLB and configure target groups with HTTP health checks and listener rules for path-based routing.

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Use Amazon API Gateway with a single backend integration and rely on the client to route requests to different microservices.

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Use Amazon CloudFront without an ALB, and route requests to microservices using only custom origin headers.

Question 5

A DynamoDB table uses this schema: partition key = customerId, sort key = timestamp. During a marketing campaign, one customer generates extremely high read traffic and the application sees ProvisionedThroughputExceeded errors even though the table’s total capacity is sufficient. What change most directly improves read distribution across partitions?

Accepted Answer

Add a salt component to the partition key by changing it to customerId#salt, where salt is derived from a hash of requestId so a single customer’s requests are spread across many partitions; keep the sort key as timestamp.. Adding a salt to the partition key (e.g., customerId#hash(requestId)) distributes the read-heavy customer's data across multiple physical partitions. This prevents a single hot partition from throttling requests, even when the table's total provisioned capacity is sufficient. DynamoDB's partition key determines the internal hash used for data placement, so increasing partition key cardinality directly improves read distribution.

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Increase the table’s provisioned read capacity units while keeping partition key = customerId.

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Remove the sort key and use timestamp as the partition key to increase cardinality.

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Switch to on-demand capacity and rely on DynamoDB to automatically distribute reads across partitions.

Question 6

Based on the exhibit, what change should the team make to achieve the lowest possible network latency for the distributed workload?

Accepted Answer

Move the workload into a cluster placement group in one Availability Zone.. A cluster placement group provides the lowest possible network latency and highest throughput by placing all instances in a single Availability Zone with low-latency, non-blocking 10 Gbps or 25 Gbps network connectivity between them. This is ideal for tightly coupled, distributed workloads that require frequent inter-node communication, such as HPC or data analytics jobs.

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Place the instances in a spread placement group across multiple Availability Zones.

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Add an Application Load Balancer in front of the workers to reduce inter-node latency.

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Increase the EC2 instance size while keeping the current multi-AZ layout.

Question 7

An Aurora PostgreSQL cluster is experiencing high read latency because 85% of traffic consists of read-only queries. The write workload must stay on the writer instance, and the team wants to offload reads without changing the application’s core query patterns. What is the best architectural option?

Accepted Answer

Add Aurora reader instances (read replicas) and route read queries to the reader endpoint while keeping writes on the writer endpoint.. Adding Aurora reader instances (read replicas) and routing read queries to the reader endpoint offloads read traffic from the writer instance without altering application query patterns. Aurora reader endpoints automatically distribute read-only connections across all replicas, reducing latency on the writer while keeping writes on the writer instance. This directly addresses the 85% read-heavy workload without requiring application changes.

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Increase the writer instance size so it can handle more reads and writes simultaneously.

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Enable Multi-AZ failover only and rely on the standby to serve reads in normal operation.

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Move the read workload to ElastiCache Redis while keeping DynamoDB as the SQL data source.

Question 8

A customer-facing application has a relational data model and needs frequent complex queries (joins and aggregations), but it also experiences a significant read-heavy workload. Which design choice best improves read performance while keeping relational features?

Accepted Answer

Add read replicas to an RDS or Aurora cluster and keep the primary for writes.. Adding read replicas to an RDS or Aurora cluster offloads read traffic from the primary instance, improving read performance for complex queries (joins and aggregations) while preserving the full relational data model and SQL capabilities. Aurora’s distributed storage layer also allows replicas to serve reads with minimal replication lag, making this the optimal choice for read-heavy workloads that require relational features.

Answer

Use DynamoDB with a single partition key and avoid indexes to keep writes simple.

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Store the data in S3 and query it directly from the application without a database.

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Switch the database to DynamoDB but keep using the same relational SQL queries and joins.

Question 9

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.. 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 efficient architectural step to reduce placement delays during peak traffic.

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

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

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

Question 10

Based on the exhibit, which Amazon EFS performance mode is the best fit for this workload?

Accepted Answer

Use General Purpose performance mode for low-latency access.. The General Purpose performance mode is the best fit for this workload because it provides the lowest latency for file operations, which is critical for latency-sensitive applications such as web serving, content management, and development environments. EFS General Purpose mode is optimized for workloads where consistent low-latency access is required, making it the default and recommended choice for most use cases.

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Use Max I/O performance mode to optimize for the highest possible latency tolerance.

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Use One Zone storage class to increase metadata speed.

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Use Provisioned Throughput mode because it is the only performance mode available.

Question 11

Your application uses ElastiCache Redis as a cache for user profiles stored in DynamoDB. You must ensure that when a profile is updated, subsequent reads see the latest value quickly. Which cache strategy is generally the best fit for this requirement?

Accepted Answer

Use a cache-aside approach with TTL plus explicit invalidation after writes.. The cache-aside (lazy loading) pattern with TTL plus explicit invalidation ensures that after a write to DynamoDB, the stale Redis entry is removed, forcing the next read to fetch the updated profile from DynamoDB and repopulate the cache. This minimizes the window of inconsistency while keeping cache management simple and efficient for user profile workloads.

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Write to DynamoDB only, and never update or invalidate the Redis cache.

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Cache only for reads, and do not fetch from DynamoDB when a key is missing.

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Rely on eventual consistency of Redis replication to propagate updates to all nodes.

Question 12

Based on the exhibit, what change best reduces Lambda cold-start impact for a predictable user-upload workflow?

Accepted Answer

Enable provisioned concurrency for the function.. Provisioned concurrency initializes a specified number of execution environments in advance, so when a user upload triggers the Lambda function, there is no cold-start delay. This directly addresses the predictable, user-upload workflow by ensuring warm containers are ready to handle requests immediately.

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Set a reserved concurrency limit for the function to protect it from throttling.

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Increase the function timeout to give more time for initialization.

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Move the function to a larger memory setting only to eliminate all initialization time.

Question 13

A team runs a latency-sensitive service on EC2 and needs consistent, low-latency block storage for a database. The application requires predictable performance and should be fast for random reads/writes. Which EBS volume type is the best choice?

Accepted Answer

EBS gp3 (general purpose SSD). B is correct because gp3 is a general-purpose SSD that provides consistent, low-latency performance for random read/write operations, making it ideal for latency-sensitive databases. It offers a baseline of 3,000 IOPS and 125 MB/s throughput, with the ability to independently scale IOPS up to 16,000 and throughput up to 1,000 MB/s, ensuring predictable performance without the burst-bucket limitations of gp2.

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EBS st1 (throughput optimized HDD)

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EBS sc1 (cold HDD)

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EBS magnetic (legacy magnetic)

Question 14

A team runs a stateless web app on Amazon EC2 behind an Application Load Balancer. During traffic spikes, new EC2 instances take several minutes to finish bootstrapping before they can receive traffic. Which Auto Scaling configuration most directly reduces the time until additional capacity is available?

Accepted Answer

Use an Auto Scaling warm pool so pre-initialized instances are ready to enter service.. An Auto Scaling warm pool allows you to maintain a pool of pre-initialized instances that are ready to quickly enter the target group and start serving traffic. Instead of waiting for new instances to boot and configure during a scale-out event, the warm pool provides instances that have already completed bootstrapping, drastically reducing the time to additional capacity.

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Increase the ALB target group deregistration delay.

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Reduce the Auto Scaling group minimum size to one instance.

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Replace the Application Load Balancer with a Network Load Balancer.

Question 15

A company runs a stateless application tier behind an Application Load Balancer. Match each observed scaling pattern on the left to the best Auto Scaling strategy or metric on the right.

Accepted Answer

Steady increase: Step scaling. Steady increase is best handled by step scaling for gradual adjustments; sudden spikes use simple scaling for immediate action; cyclical patterns benefit from scheduled scaling; consistent low traffic may not need scaling; unpredictable bursts are managed by target tracking to maintain a metric; gradual decrease uses simple scaling to reduce capacity.

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Steady increase: Simple scaling

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Unpredictable bursts: Scheduled scaling