SAA-C03 Design High-Performing Architectures Practice Test 20 — 15 Questions

Question 1

A startup runs a read-heavy product catalogue API on Amazon DynamoDB. The table uses on-demand capacity mode, and the team notices that repeated queries for the same popular items return consistently, causing high read request charges. The application can tolerate data that is a few seconds stale. Which change reduces read costs while keeping latency low?

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

Accepted Answer

Enable DynamoDB Accelerator (DAX) for the table and route the read queries through the DAX cluster.. A DAX cluster caches item reads in memory and serves repeated requests for the same popular items without touching the table, which cuts read request charges and keeps latency in the microsecond range. Because the application can accept data that is a few seconds stale, the cache's item TTL behavior is acceptable, making DAX the direct fit for this read-heavy, repeated-key pattern.

Answer

Create a global secondary index on the queried attributes and read from the index instead of the table.

Answer

Switch the table to provisioned capacity mode and purchase reserved capacity.

Answer

Increase the table's read capacity and enable auto scaling to handle the repeated queries.

Question 2

A logistics company runs a REST API on Amazon ECS using the Fargate launch type behind an Application Load Balancer. The API's response times are acceptable, but the operations team wants to reduce the number of database calls per request by caching frequently accessed reference data in memory inside the tasks. The data changes infrequently and slight staleness is acceptable. Which approach best meets these requirements?

Accepted Answer

Implement an in-process cache in the application code that loads reference data at task startup and refreshes it periodically.. Caching reference data in the application's own process memory means each request is served locally with no database or network call, which is the most direct way to reduce per-request database calls. Because the data is small, changes rarely, and tolerates slight staleness, periodic refresh is simple and effective. External caches like Redis or Memcached still require a network call, and EFS is storage rather than memory.

Answer

Store the reference data in an Amazon ElastiCache for Redis cluster and have tasks query it on each request.

Answer

Enable Amazon ElastiCache for Memcached and configure automatic discovery of cache nodes.

Answer

Use the ECS task definition to mount an Amazon EFS file system containing the reference data.

Question 3

A solutions architect is designing a high-performance architecture for a read-heavy web application backed by Amazon RDS for MySQL. The database is currently a single db.r6g.4xlarge instance that is CPU-bound during peak hours, and the application performs many repeated identical read queries. The architect must improve read scalability and reduce load on the primary instance. (Choose two.)

Accepted Answer

Add an Amazon ElastiCache for Redis cluster in front of the database and cache the results of repeated queries.. Read replicas add horizontal read capacity by serving queries from separate instances, while an ElastiCache for Redis cluster absorbs repeated identical reads in memory before they reach the database. Together they reduce the CPU load on the primary from both directions: fewer queries arrive, and the ones that do can be served by replicas instead of the writer.

Answer

Enable RDS Performance Insights and set up a longer retention period for the performance history.

Answer

Convert the instance to a larger db.r6g.16xlarge size to increase memory and CPU.

Answer

Enable Multi-AZ with a standby instance and point read queries at the standby endpoint.

Question 4

A development team is building a new application that stores session state in a relational database. The application experiences unpredictable read traffic, and the team wants a fully managed database that can scale read capacity automatically and provide a reader endpoint that distributes connections across multiple replicas. Which AWS service should a solutions architect recommend?

Accepted Answer

Amazon Aurora with Aurora Replicas and the reader endpoint.. Aurora is a managed relational database whose Aurora Replicas serve read traffic, and the cluster reader endpoint spreads incoming read connections across those replicas automatically. That directly matches the need for automatic read scaling with a reader endpoint. DynamoDB is non-relational, RDS Multi-AZ standbys do not serve reads, and Redshift is an analytics warehouse rather than an OLTP database.

Answer

Amazon Redshift with concurrency scaling enabled.

Answer

Amazon DynamoDB with on-demand capacity mode.

Answer

Amazon RDS for SQL Server with Multi-AZ deployment.

Question 5

A startup runs a stateless web application on a fleet of Amazon EC2 instances in an Auto Scaling group behind an Application Load Balancer. Traffic is steady during business hours, but the team has configured the scaling policy with a target tracking metric of average CPU utilization at 50 percent. Users report intermittent 5xx errors during sudden traffic surges. Which change will most directly improve the application's ability to absorb rapid traffic increases?

Accepted Answer

Add a step scaling policy that reacts to Amazon CloudWatch alarms on request count per target and adds capacity in larger increments as the breach grows.. Sudden traffic surges require capacity that reacts to demand quickly and in proportion to the breach. Step scaling with CloudWatch alarms adds instances in larger, tiered increments as the metric worsens, shortening the period where the fleet is undersized. Scheduled scaling fits predictable peaks, and raising the CPU target or tuning deregistration affects cost or scale-in, not surge response.

Answer

Enable connection draining on the Application Load Balancer with a long deregistration delay so that in-flight requests complete before instances are removed.

Answer

Switch the Auto Scaling group to a scheduled scaling policy that adds instances at fixed times each morning based on historical traffic patterns.

Answer

Increase the target tracking value for average CPU utilization from 50 percent to 80 percent so that existing instances are used more fully before scaling out.

Question 6

A company runs a stateless containerized web application on Amazon ECS with the Fargate launch type behind an Application Load Balancer. The application must scale out quickly when request latency rises and scale in when traffic drops, and the operations team wants a managed target-tracking approach. Which solution meets these requirements?

Accepted Answer

Configure an Application Auto Scaling target-tracking scaling policy on the ECS service using the ALBRequestCountPerTarget metric.. Application Auto Scaling target tracking on the ECS service is the managed mechanism that adjusts desired task count in response to a metric. ALBRequestCountPerTarget reflects how much work each task is receiving from the load balancer, so it scales out before latency degrades and scales in as traffic falls, and it works natively with the Fargate launch type.

Answer

Use a scheduled scaling action on the ECS service to add tasks at the start of each business day.

Answer

Create an EC2 Auto Scaling group with a target-tracking policy and register the ECS tasks as instances.

Answer

Deploy the application on EC2 instances in an Auto Scaling group and use the ECS-optimized AMI, replacing Fargate.

Question 7

A trading platform ingests market data events at very high volume and must deliver them with the lowest possible latency to multiple independent consumer applications. Each consumer must read the full stream independently, and ordering must be preserved per instrument symbol. Which solution meets these requirements?

Accepted Answer

Create an Amazon Kinesis Data Streams stream with a partition key of the instrument symbol, and have each consumer use the Kinesis Client Library.. Kinesis Data Streams is built for high-throughput, low-latency event ingestion where many applications read the same stream independently. Partitioning by instrument symbol guarantees that all events for a given symbol land on one shard and are read in order, and the Kinesis Client Library gives each consumer application its own checkpointed position without affecting the others.

Answer

Create an Amazon SQS FIFO queue with a message group ID of the instrument symbol, and have each consumer application use a separate queue.

Answer

Use AWS Database Migration Service with a change data capture task to replicate events into an Amazon Aurora cluster that each consumer queries.

Answer

Create an Amazon SNS topic and subscribe each consumer application with an Amazon SQS standard queue endpoint.

Question 8

A company runs a web application on Amazon EC2 instances behind an Application Load Balancer (ALB). The application experiences variable traffic patterns, with sudden spikes during marketing campaigns. The operations team wants to ensure that the application can scale out quickly to handle the spikes and scale in when traffic decreases, while minimizing costs. Which solution should a solutions architect recommend?

Accepted Answer

Configure an Auto Scaling group with a target tracking scaling policy based on average CPU utilization.. A target tracking scaling policy with an Auto Scaling group is the most effective way to handle variable traffic. It automatically adjusts capacity based on CPU utilization, scaling out during spikes and scaling in during low traffic, which optimizes both performance and cost. Other options are either static, do not address scaling, or require major architectural changes.

Answer

Deploy the application on AWS Lambda behind an API Gateway and remove the EC2 instances.

Answer

Use a scheduled scaling policy that adds instances at the start of each marketing campaign.

Answer

Enable sticky sessions on the ALB and increase the instance size.

Question 9

A media company serves video thumbnails from an Amazon S3 bucket in us-east-1 to viewers across Europe and Asia. The thumbnails are immutable after upload and are requested repeatedly by the same users. The company wants to reduce latency for the global audience and reduce data transfer costs, and it does not want to modify application code. Which solution meets these requirements with the LEAST operational effort?

Accepted Answer

Create an Amazon CloudFront distribution with the S3 bucket as origin, enable caching, and configure an origin access control.. CloudFront is the AWS content delivery network that caches objects at edge locations worldwide, so repeated thumbnail requests from Europe and Asia are served close to viewers instead of from us-east-1. Because the objects are immutable, cache hit ratios stay high, cutting origin fetches and S3 egress charges. Origin access control keeps the bucket private, and the application continues to use the distribution domain without code changes.

Answer

Replicate the bucket to eu-west-1 and ap-southeast-1 using S3 Cross-Region Replication, and have the application select the nearest bucket.

Answer

Attach an S3 bucket policy that allows public read and place an Application Load Balancer in front of the bucket.

Answer

Enable S3 Transfer Acceleration on the bucket and update the application to use the accelerated endpoint.

Question 10

A logistics company runs an Amazon RDS for MySQL database that supports a parcel-tracking API. During the morning peak, read queries for tracking history saturate the primary instance's CPU, slowing writes. The reads can tolerate a few seconds of staleness, and the team wants to offload them without changing the database engine. Which action should the team take?

Accepted Answer

Create one or more read replicas and update the application to send tracking-history queries to the replica endpoint.. A read replica is a dedicated copy of the primary that serves read-only queries, so routing the tracking-history requests to it removes that CPU pressure from the primary instance while writes continue unimpeded. Replication is asynchronous, producing lag on the order of seconds, which matches the workload's tolerance for slightly stale reads. This keeps the MySQL engine and requires only an endpoint change in the application.

Answer

Enable Multi-AZ on the DB instance and point the read queries at the standby instance.

Answer

Increase the size of the RDS DB instance and enable storage autoscaling on the primary.

Answer

Migrate the database to Amazon DynamoDB with a global secondary index on the tracking ID.

Question 11

A genomics research team stores about 400 TB of compressed sequence files in Amazon S3 and runs a distributed analysis on Amazon EC2 instances in the same Region. The analysis reads each file sequentially and writes intermediate results to local instance storage. The team reports that the S3 GET requests are a bottleneck and wants to improve read throughput while keeping data durable. (Choose two.)

Accepted Answer

Prefix the object keys with a hash or random value to spread requests across multiple S3 partitions.. S3 request performance is bounded by how well requests spread across the index partitions and by per-object concurrency. Randomizing key prefixes distributes GETs across many partitions so no single prefix throttles, and byte-range fetches let many threads or instances read one large object in parallel. Together they raise aggregate read throughput for the 400 TB analysis while the objects stay durable in S3.

Answer

Mount the bucket with an S3 File Gateway and read the files over NFS from the EC2 fleet.

Answer

Enable S3 Versioning on the bucket so concurrent readers can access older object versions.

Answer

Configure the bucket for S3 Standard-Infrequent Access to lower the cost of frequent GET requests.

Question 12

A startup runs a stateless web application on Amazon EC2 instances behind an Application Load Balancer. Traffic is steady during the day but drops to almost zero overnight, and the team wants to reduce compute cost without manual intervention or a service interruption. Which action should the team take?

Accepted Answer

Create a scheduled scaling action on an Auto Scaling group that reduces the desired capacity at night and restores it each morning.. The workload has a predictable daily pattern, which is exactly what scheduled scaling on an Auto Scaling group is designed for. Reducing desired capacity overnight and restoring it in the morning lowers compute spend while the group and load balancer keep the application available. Connection draining on the ALB ensures in-flight requests finish before instances terminate, so users see no interruption.

Answer

Replace the Auto Scaling group with a single large EC2 instance and use Elastic Load Balancing health checks.

Answer

Attach an Amazon EBS volume with higher IOPS to each instance to reduce the number of instances needed.

Answer

Switch to Spot Instances for the entire fleet and rely on capacity-optimized allocation.

Question 13

A financial services company runs a high-traffic REST API on Amazon EC2 instances behind an Application Load Balancer. The API retrieves user session data from an Amazon DynamoDB table for every request. During peak hours, DynamoDB read capacity is exhausted, causing throttling and increased latency. The workload is read-heavy and the session data is accessed frequently but changes infrequently. The solutions architect needs to reduce DynamoDB read load and improve API response times with minimal application changes. Which solution meets these requirements?

Accepted Answer

Enable DynamoDB Accelerator (DAX) for the table and modify the application to use the DAX client for session reads.. DynamoDB Accelerator (DAX) is a fully managed, highly available in-memory cache for DynamoDB that delivers up to a 10x performance improvement, from milliseconds to microseconds. It is API-compatible with DynamoDB, so the application only needs to use the DAX client. For read-heavy workloads with infrequent updates, DAX reduces read capacity consumption and improves response times without requiring a major rewrite or data migration.

Answer

Migrate the session data to Amazon ElastiCache for Redis and modify the application to read from Redis instead of DynamoDB.

Answer

Create a read replica of the DynamoDB table in another region and direct all reads to the replica.

Answer

Increase the DynamoDB read capacity to a higher provisioned level and enable auto scaling.

Question 14

A genomics research company runs a large-scale sequence alignment workload on AWS. The workload requires a shared file system that can be accessed concurrently by thousands of EC2 instances, provides high throughput and low latency, and supports POSIX permissions. The data set is about 500 TB and grows by 10 TB per month. The solutions architect needs to choose a storage solution that meets these performance and scalability requirements. Which solution should the architect use?

Accepted Answer

Amazon FSx for Lustre with a linked Amazon S3 data repository.. Amazon FSx for Lustre is a fully managed, high-performance file system optimized for compute-intensive workloads such as genomics, machine learning, and HPC. It provides POSIX-compliant access, scales to hundreds of gigabytes per second throughput and millions of IOPS, and can be linked to an S3 bucket for durable storage. It supports thousands of concurrent EC2 instances, making it the best fit for large-scale sequence alignment.

Answer

Amazon FSx for Windows File Server with Multi-AZ deployment.

Answer

Amazon EFS with Provisioned Throughput mode and a mount target in each Availability Zone.

Answer

Amazon S3 with S3 Transfer Acceleration and an S3 Gateway endpoint.

Question 15

A solutions architect is designing a high-performance computing (HPC) workload that requires a shared file system with high throughput and low latency for thousands of compute instances. The workload also requires a caching layer to accelerate repeated reads of the same data. Which two AWS services should be combined to meet these requirements? (Choose two.)

Accepted Answer

Amazon FSx for Lustre. Amazon FSx for Lustre provides the high-performance shared file system needed for HPC, with low latency and high throughput. Amazon ElastiCache for Redis adds an in-memory caching layer to accelerate repeated reads, reducing load on the file system. Together, they meet the requirements for a scalable, high-performance HPC storage and caching solution. Other options either have high latency (Glacier), are for hybrid scenarios (File Gateway), or lack the necessary scalability (EBS Multi-Attach).

Answer

Amazon S3 Glacier

Answer

Amazon EBS Multi-Attach enabled io1 volumes

Answer

AWS Storage Gateway File Gateway