SAA-C03 Design High-Performing Architectures Practice Test 10 — 15 Questions

Question 1

Based on the exhibit, which storage choice best matches the workload requirements?

Accepted Answer

Use instance store NVMe for the temporary processing workspace.. Instance store NVMe volumes provide temporary, high-performance block storage directly attached to the EC2 host. For a temporary processing workspace where data does not need to persist beyond the instance lifecycle, instance store offers the lowest latency and highest throughput, making it the best match for the workload requirements.

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Use io2 EBS volumes because they provide the highest durable block storage performance.

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Use Amazon EFS for the workspace so the temporary files survive instance replacement.

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Use S3 as the working directory and read and write the intermediate files directly there.

Question 2

A company runs a stateless web API on Amazon EC2 behind an Application Load Balancer. The team notices that during business hours, the ALB starts queueing requests and the average request latency rises. They want to scale out quickly and reliably based on demand, not CPU alone. Which Auto Scaling approach best matches this requirement?

Accepted Answer

Use target tracking scaling based on ALB request count per target.. Target tracking scaling based on ALB request count per target directly aligns with the requirement to scale out based on demand (request queuing and latency) rather than CPU alone. This policy automatically adjusts the Auto Scaling group size to maintain a target value for the average number of requests per instance, which is a more reliable indicator of load for a stateless web API than CPU utilization.

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Use a fixed-size Auto Scaling group and increase capacity manually once per hour.

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Scale based only on EC2 instance memory utilization, regardless of load.

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Use step scaling with a single threshold on average network-in bytes.

Question 3

A company serves mostly static images and JavaScript files from an origin in one AWS Region. They want to reduce origin load and improve global performance. Which change most directly increases cache-hit ratio for static assets while avoiding stale content?

Accepted Answer

Use versioned file names (e.g., app.abc123.js) and configure a long TTL with appropriate revalidation behavior.. Using versioned file names (e.g., app.abc123.js) allows you to set a long Cache-Control max-age TTL (e.g., one year) without risking stale content. When the file changes, the new version gets a new URL, so clients and edge caches immediately fetch the fresh object, maximizing cache hits for unchanged assets while avoiding stale content.

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Set Cache-Control headers on the origin to always be no-cache so clients revalidate frequently.

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Disable query string forwarding so all URLs without query strings share one cached object even when content differs.

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Forward all headers, including cookies, to maximize personalization in edge cached responses.

Question 4

Based on the exhibit, which change best reduces latency during peak traffic without overprovisioning the fleet?

Accepted Answer

Change the Auto Scaling policy to target tracking on ALB RequestCountPerTarget.. Using a target tracking scaling policy on ALB RequestCountPerTarget dynamically adjusts the fleet size based on the actual load per instance, ensuring that capacity scales with demand during peak traffic without manual intervention or overprovisioning. This approach directly addresses latency caused by high request rates per instance by maintaining a target request count, which reduces response time without adding unnecessary instances during off-peak periods.

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Replace the instances with a larger instance family so each server has more headroom.

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Use scheduled scaling to add instances only during the business hours peak window.

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Replace the ALB with a Network Load Balancer to reduce request latency.

Question 5

Based on the exhibit, which change will most improve the CloudFront cache hit ratio for the static assets while still serving the same files to all users?

Accepted Answer

Create a custom cache policy that includes only the v query string and excludes cookies.. The CloudFront cache hit ratio for static assets is reduced when query strings and cookies are forwarded to the origin, because each unique combination creates a separate cache entry. By creating a custom cache policy that includes only the 'v' query string (used for versioning) and excludes cookies, CloudFront can cache a single object for all users regardless of other query parameters or cookie values, maximizing cache hits while still serving the same file.

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Enable Origin Shield and keep the current cache behavior unchanged.

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Move the static assets to individual presigned URLs for each viewer.

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Increase the CloudFront default TTL to 24 hours while continuing to forward all cookies and query strings.

Question 6

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 pre-warms a specified number of execution environments so that when a user upload triggers the Lambda function, there is no cold-start latency. This is the most direct way to eliminate initialization time for a predictable workload, as it keeps instances 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 7

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.. A cache-aside (lazy loading) strategy with TTL and explicit invalidation ensures that after a write to DynamoDB, the stale Redis entry is removed, forcing the next read to fetch the fresh profile from DynamoDB and repopulate the cache. This combination minimizes the window of stale reads while maintaining high read performance, which is critical for user profile caches where consistency matters.

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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 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 relational data model. Aurora automatically scales read replicas and uses a shared storage volume, making this highly efficient for read-heavy workloads.

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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

A web application uses an Amazon Aurora DB cluster for a read-heavy workload. The application team needs higher read throughput but cannot change the database schema. They want to avoid blocking writes and are willing to route read traffic separately. What is the most appropriate architecture change?

Accepted Answer

Create Aurora read replicas and route SELECT queries to an Aurora reader endpoint.. Creating Aurora read replicas and routing SELECT queries to the Aurora reader endpoint is the most appropriate architecture change because Aurora's reader endpoint distributes read traffic across up to 15 low-latency read replicas, providing higher aggregate read throughput without blocking writes. This approach requires no schema changes and allows the application to separate read and write traffic, directly addressing the read-heavy workload requirement.

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Scale up the writer instance storage only; read capacity will automatically increase without using a reader endpoint.

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Move the Aurora cluster to Multi-AZ deployment mode only; read scaling is handled automatically without replicas.

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Replace the cluster with a single RDS instance because it offers consistent performance for both reads and writes.

Question 10

A team serves image files from S3 through CloudFront. During a performance review, they notice that CloudFront cache hit ratio is low and the S3 origin receives many repeated requests for the same images. Request URLs include a volatile query parameter called 'sessionId' that changes for each user, but the image content is identical regardless of 'sessionId'. What configuration change will most effectively increase cache hit ratio?

Accepted Answer

Update the CloudFront cache policy so that 'sessionId' is not included in the cache key (and only stable query parameters are used).. The low cache hit ratio is caused by the volatile 'sessionId' query parameter being included in the CloudFront cache key, which creates a unique cache entry for every user request even though the image content is identical. By updating the cache policy to exclude 'sessionId' from the cache key, CloudFront will treat all requests for the same image as the same cached object, dramatically increasing the cache hit ratio and reducing load on the S3 origin.

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Enable origin request policy to forward all query strings to S3 so responses are always correct for every sessionId.

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Set the CloudFront minimum TTL to 0 seconds so cached objects expire quickly and fetch fresh content more often.

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Disable caching by using CloudFront managed caching disabled so that every request validates with the origin.

Question 11

A trading analytics system deploys 10 EC2 instances that exchange very frequent, low-latency messages over the network. The instances must be placed as close together as possible to minimize network hop count and inter-node jitter. Which deployment choice best matches this requirement?

Accepted Answer

Use a cluster placement group so the instances are placed close together to reduce latency and jitter.. A cluster placement group is designed for low-latency, high-throughput scenarios by placing all instances in a single Availability Zone within the same rack or logical cluster, minimizing network hop count and inter-node jitter. This directly meets the requirement for very frequent, low-latency messaging between 10 EC2 instances.

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Use a spread placement group to distribute instances across multiple underlying hardware to improve overall availability.

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Use no placement group and rely on the Auto Scaling group to balance instance placement automatically.

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Use a partition placement group so each instance is assigned to separate failure domains for low variance.

Question 12

Your company currently uses an Application Load Balancer (ALB) in front of a service that receives a large number of TCP and UDP packets (including UDP-based telemetry). During load tests, you need to support both TCP and UDP traffic at high throughput while keeping stable IP endpoints for a downstream firewall allowlist. Which change best meets these requirements?

Accepted Answer

Switch to a Network Load Balancer (NLB) configured for TCP/UDP, and use Elastic IPs to provide stable endpoint IP addresses for allowlisting.. A Network Load Balancer (NLB) operates at Layer 4 and can handle both TCP and UDP traffic natively, unlike an ALB which only supports HTTP/HTTPS and cannot forward UDP packets. By assigning Elastic IPs to the NLB, you provide stable, static IP endpoints that can be added to a downstream firewall allowlist, meeting both the protocol and throughput requirements.

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Keep the ALB and add an AWS WAF Web ACL to improve throughput and add static IP support.

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Replace the ALB with an API Gateway REST API to support UDP because API Gateway can forward UDP packets.

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Use an Auto Scaling group with multiple EC2 instances and no load balancer to avoid any networking bottlenecks.

Question 13

A DynamoDB-backed multi-tenant app experiences throttling. Most write traffic for tenant 'ACME' targets a single logical stream of events (you write items for ACME in near-real time). The table currently uses partition key = tenantId and sort key = eventTimestamp. CloudWatch shows partition-level throttling concentrated in the ACME partition. What design change most directly improves write throughput for the hottest tenant while still enabling efficient queries for recent events for that tenant?

Accepted Answer

Mitigate the hotspot by changing the partition key to include a shard value (for example, tenantId + '#' + shardId) and write using shardId. Query recent events by fanning out across ACME shards and merging results by eventTimestamp.. It directly addresses the partition-level throttling by introducing a shard key (e.g., tenantId + '#' + shardId) as the partition key, which distributes ACME's write load across multiple physical partitions. To query recent events for ACME, the application must fan out queries across all shards and merge results by eventTimestamp, which is efficient because each shard holds a subset of the data and the sort key remains eventTimestamp for ordering.

Answer

Add a Global Secondary Index (GSI) with the same partition key (tenantId) and eventTimestamp, and rely on the GSI to spread load.

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Increase the table’s write capacity (or on-demand baseline) without changing the partition key, because DynamoDB will automatically balance hotspots.

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Switch the sort key to a random value to prevent writes from landing on the same physical partition.

Question 14

A serverless API built with AWS Lambda serves latency-sensitive requests. The team observes intermittent slow responses during traffic ramp-ups and expects some users to hit the API immediately after a period of inactivity. Which configuration best reduces cold-start latency during these ramp-ups?

Accepted Answer

Enable Lambda provisioned concurrency on a published alias used by the API, and set a minimum provisioned concurrency greater than zero.. Lambda provisioned concurrency keeps a specified number of execution environments initialized and ready to respond immediately, eliminating cold starts for those invocations. By setting a minimum provisioned concurrency greater than zero on the alias used by API Gateway, the function remains warm even after periods of inactivity, ensuring consistent low latency during traffic ramp-ups.

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Increase the Lambda function’s memory setting; cold starts will always be eliminated regardless of traffic patterns.

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Switch the Lambda runtime to a newer language version and remove any VPC configuration so the function never cold starts.

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Set an API Gateway stage variable to "warm" the function at request time, which forces immediate initialization.

Question 15

A media processing pipeline uses EBS-backed storage for an application that performs sustained random I/O with low latency requirements. During peak processing windows, the team sees increased read latency and occasional timeouts at the application layer. They need predictable, high IOPS performance rather than best-effort throughput. Which EBS configuration choice is most appropriate?

Accepted Answer

Use io1 or io2 EBS volumes configured with a high provisioned IOPS value, and attach them to EBS-optimized instances.. Io1 and io2 volumes are provisioned IOPS SSD volumes designed for sustained, predictable high IOPS performance, which directly addresses the application's need for low-latency random I/O during peak loads. Attaching them to EBS-optimized instances ensures dedicated network bandwidth for EBS traffic, eliminating contention and preventing timeouts.

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Use gp2 volumes and rely on burst credits to handle peak random I/O latency requirements.

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Use standard HDD (st1) volumes, because they provide high throughput and will reduce latency automatically.

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Use S3 instead of EBS for random I/O latency reduction without changing the application.