Practice Free SAA-C03 Exam Online Questions
A company runs several websites on AWS for its different brands Each website generates tens of gigabytes of web traffic logs each day. A solutions architect needs to design a scalable solution to give the company’s developers the ability to analyze traffic patterns across all the company’s websites. This analysis by the developers will occur on demand once a week over the course of several months. The solution must support queries with standard SQL.
Which solution will meet these requirements MOST cost-effectively?
- A . Store the logs in Amazon S3. Use Amazon Athena for analysis.
- B . Store the logs in Amazon RDS. Use a database client for analysis.
- C . Store the logs in Amazon OpenSearch Service. Use OpenSearch Service for analysis.
- D . Store the logs in an Amazon EMR cluster. Use a supported open-source framework for SQL-based analysis.
A
Explanation:
This solution is the most cost-effective and scalable for analyzing large amounts of web traffic logs.
Amazon S3: Storing the logs in Amazon S3 is highly scalable, durable, and cost-effective. S3 is designed to handle large-scale data storage, which is ideal for storing tens of gigabytes of log data generated daily by multiple websites.
Amazon Athena: Athena is a serverless, interactive query service that allows you to analyze data in S3 using standard SQL. It works directly with the data stored in S3, so there’s no need to load the data into a database, which saves on costs and reduces complexity. Athena charges based on the amount of data scanned by queries, making it a cost-effective solution for on-demand analysis that only occurs once a week.
Why Not Other Options?
Option B (Amazon RDS): Storing logs in a relational database like Amazon RDS would be more expensive due to the storage and I/O costs associated with RDS. Additionally, it would require more management overhead.
Option C (Amazon OpenSearch Service): OpenSearch is a good option for full-text search and analytics on log data, but it might be more costly and complex to manage compared to the simplicity and cost-effectiveness of Athena for periodic SQL-based queries.
Option D (Amazon EMR): While EMR can handle large-scale data processing, it involves more operational overhead and might be overkill for the type of ad-hoc, SQL-based analysis required here. Additionally, EMR costs can be higher due to the need to maintain a cluster.
AWS
Reference: Amazon S3- Information on how to store and manage data in Amazon S3.
Amazon Athena- Documentation on using Amazon Athena for querying data stored in S3 using SQL.
A company is designing a solution to capture customer activity on the company’s web applications.
The company wants to analyze the activity data to make predictions.
Customer activity on the web applications is unpredictable and can increase suddenly. The company requires a solution that integrates with other web applications. The solution must include an authorization step.
Which solution will meet these requirements?
- A . Deploy a Gateway Load Balancer (GWLB) in front of an Amazon Elastic Container Service (Amazon ECS) container instance. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Configure the applications to pass an authorization header to the GWLB.
- B . Deploy an Amazon API Gateway endpoint in front of an Amazon Kinesis data stream. Store the data in an Amazon S3 bucket. Use an AWS Lambda function to handle authorization.
- C . Deploy an Amazon API Gateway endpoint in front of an Amazon Data Firehose delivery stream. Store the data in an Amazon S3 bucket. Use an API Gateway Lambda authorizer to handle authorization.
- D . Deploy a Gateway Load Balancer (GWLB) in front of an Amazon Elastic Container Service (Amazon ECS) container instance. Store the data in an Amazon Elastic File System (Amazon EFS) file system. Use an AWS Lambda function to handle authorization.
C
Explanation:
The requirements specify capturing unpredictable and sudden spikes in customer activity, integrating easily with other web applications, and including authorization.
Amazon API Gateway with Lambda authorizer provides a secure, scalable entry point with flexible authorization mechanisms including token validation.
Amazon Kinesis Data Firehose is a fully managed service to reliably load streaming data into destinations such as Amazon S3, which fits well for capturing streaming customer activity data.
API Gateway integrates natively with Firehose for direct ingestion.
This combination supports unpredictable traffic, smooth scaling, and simple authorization.
Option B uses Kinesis Data Streams, which requires more management than Firehose and is less optimized for direct API integration.
Options A and D use Gateway Load Balancer and ECS containers plus EFS, which add complexity and are less suited for unpredictable traffic with integrated authorization.
Reference: Amazon API Gateway
(https: //docs.aws.amazon.com/apigateway/latest/developerguide/welcome.html)
Amazon API Gateway Lambda authorizers
(https: //docs.aws.amazon.com/apigateway/latest/developerguide/apigateway-use-lambda-authorizer.html)
Amazon Kinesis Data Firehose (https: //docs.aws.amazon.com/firehose/latest/dev/what-is-this-service.html)
AWS Well-Architected Framework ― Operational Excellence Pillar (https: //d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf)
An events company runs a web application on Amazon EKS that uses an Amazon DynamoDB table. The table has 1,000 RCUs and 500 WCUs provisioned. The application uses eventually consistent reads.
Traffic is usually low but occasionally spikes. During spikes, DynamoDB throttles requests, causing user-facing errors.
What should a solutions architect do to reduce these errors?
- A . Change the DynamoDB table to on-demand capacity mode.
- B . Create a DynamoDB read replica.
- C . Purchase DynamoDB reserved capacity.
- D . Use strongly consistent reads.
A
Explanation:
The application experiences unpredictable traffic spikes, which exceed the provisioned read and write capacity of the DynamoDB table, resulting in throttling errors. This is a classic scenario where on-demand capacity mode is the most appropriate solution.
Option A is correct because DynamoDB on-demand capacity automatically scales to handle sudden increases in traffic without requiring capacity planning. This eliminates throttling during unpredictable spikes and removes the operational burden of managing RCUs and WCUs. On-demand mode is particularly well-suited for workloads with variable or spiky access patterns.
Option B is incorrect because DynamoDB does not support read replicas in the same way as relational databases.
Option C reduces cost for predictable workloads but does not prevent throttling if capacity limits are exceeded.
Option D increases read cost and does not address capacity constraints.
Therefore, A is the best solution to improve resilience, eliminate throttling errors, and ensure a smooth user experience during traffic surges.
A solutions architect has created an AWS Lambda function that is written in Java. A company will use the Lambda function as a new microservice for its application. The company’s customers must be able to call an HTTPS endpoint to reach the microservice. The microservice must use AWS Identity and Access Management (IAM) to authenticate calls.
Which solution will meet these requirements?
- A . Create an Amazon API Gateway REST API. Configure an API method to use the Lambda function.
Create a second Lambda function that is configured as an authorizer. - B . Create an AWS Lambda function URL for the Lambda function.
Specify AWS_IAM as the authentication type. - C . Create an Amazon CloudFront distribution. Deploy the Lambda function to Lambda@Edge.
Integrate IAM authentication logic into the Lambda@Edge function. - D . Create an Amazon CloudFront distribution. Deploy the Lambda function to CloudFront Functions.
Specify AWS_IAM as the authentication type.
B
Explanation:
Comprehensive and Detailed 250 to 300 words of Explanation (AWS documentation-based, no links):
The requirements are straightforward: expose the Lambda microservice through an HTTPS endpoint and authenticate calls using IAM. Lambda function URLs are a built-in feature that provides a dedicated HTTPS endpoint for a Lambda function without requiring API Gateway, ALB, or CloudFront.
When configured with the authentication type AWS_IAM, the endpoint requires requests to be signed with AWS Signature Version 4 and authorized by IAM policies. This directly satisfies the “must use IAM to authenticate calls” requirement with the least architectural complexity.
Option A can also secure an endpoint with IAM, but it proposes using a Lambda authorizer, which is typically used for custom authorizers (JWT/OAuth/Cognito/external identity). For IAM authentication in API Gateway, you generally use IAM authorization on the method, not an authorizer function. Also, API Gateway REST APIs introduce additional service configuration and per-request costs when a simpler managed option exists that meets the requirements.
Options C and D are not appropriate. Lambda@Edge and CloudFront Functions run at CloudFront edge locations with different programming and deployment models; they are designed for CDN request/response manipulation, not as the primary mechanism to expose a regional Lambda microservice endpoint with IAM authentication. CloudFront Functions in particular is for lightweight JavaScript at the edge and does not provide a native “AWS_IAM authentication type” for invoking an origin Lambda as a microservice endpoint.
Therefore, B is the cleanest and most secure fit: a native HTTPS endpoint backed by Lambda, protected with IAM-based SigV4 authentication.
A company runs database workloads on AWS that are the backend for the company’s customer portals. The company runs a Multi-AZ database cluster on Amazon RDS for PostgreSQL.
The company needs to implement a 30-day backup retention policy. The company currently has both automated RDS backups and manual RDS backups. The company wants to maintain both types of existing RDS backups that are less than 30 days old.
Which solution will meet these requirements MOST cost-effectively?
- A . Configure the RDS backup retention policy to 30 days tor automated backups by using AWS Backup. Manually delete manual backups that are older than 30 days.
- B . Disable RDS automated backups. Delete automated backups and manual backups that are older than 30 days. Configure the RDS backup retention policy to 30 days tor automated backups.
- C . Configure the RDS backup retention policy to 30 days for automated backups. Manually delete manual backups that are older than 30 days
- D . Disable RDS automated backups. Delete automated backups and manual backups that are older than 30 days automatically by using AWS CloudFormation. Configure the RDS backup retention policy to 30 days for automated backups.
A
Explanation:
Setting the RDS backup retention policy to 30 days for automated backups through AWS Backup allows the company to retain backups cost-effectively. Manual backups, however, are not automatically managed by RDS’s retention policy, so they need to be manually deleted if they are older than 30 days to avoid unnecessary storage costs.
Key AWS features:
Automated Backups: Can be configured with a retention policy of up to 35 days, ensuring that older automated backups are deleted automatically.
Manual Backups: These are not subject to the automated retention policy and must be manually managed to avoid extra costs.
AWS Documentation: AWS recommends using backup retention policies for automated backups while manually managing manual backups.
A company is developing a platform to process large volumes of data for complex analytics and machine learning (ML) tasks. The platform must handle compute-intensive workloads. The workloads currently require 20 to 30 minutes for each data processing step.
The company wants a solution to accelerate data processing.
Which solution will meet these requirements with the LEAST operational overhead?
- A . Deploy three Amazon EC2 instances. Distribute the EC2 instances across three Availability Zones. Use traditional batch processing techniques for data processing.
- B . Create an Amazon EMR cluster. Use managed scaling. Install Apache Spark to assist with data processing.
- C . Create an AWS Lambda function for each data processing step. Deploy an Amazon Simple Queue Service (Amazon SQS) queue to relay data between Lambda functions.
- D . Create a series of AWS Lambda functions to process the data. Use AWS Step Functions to orchestrate the Lambda functions into data processing steps.
B
Explanation:
Amazon EMR provides a managed big data framework that supports Apache Spark, which is ideal for distributed and compute-intensive data transformations. Managed scaling dynamically adjusts cluster resources, ensuring high performance with minimal management.
From AWS Documentation:
“Amazon EMR provides a managed environment for big data frameworks such as Apache Spark and Hadoop. With managed scaling, EMR automatically resizes clusters to meet workload demands.”
(Source: Amazon EMR Developer Guide)
Why B is correct:
Provides distributed parallel processing for large datasets.
Reduces operational overhead with managed scaling and auto-termination.
Integrates easily with S3, Glue, and ML pipelines.
Optimized for heavy ETL and analytics workloads.
Why others are incorrect:
A: Manual scaling and limited processing capacity.
C & D: Lambda has execution time and memory limits unsuitable for 30-minute compute-intensive tasks.
Reference: Amazon EMR Developer Guide C “Using Managed Scaling”
AWS Well-Architected Framework C Performance Efficiency Pillar
A company runs a custom application on Amazon EC2 On-Demand Instances. The application has frontend nodes that must run 24/7. The backend nodes only need to run for short periods depending on the workload.
Frontend nodes accept jobs and place them in queues. Backend nodes asynchronously process jobs from the queues, and jobs can be restarted. The company wants to scale infrastructure based on workload, using the most cost-effective option.
Which solution meets these requirements MOST cost-effectively?
- A . Use Reserved Instances for the frontend nodes. Use AWS Fargate for the backend nodes.
- B . Use Reserved Instances for the frontend nodes. Use Spot Instances for the backend nodes.
- C . Use Spot Instances for the frontend nodes. Use Reserved Instances for the backend nodes.
- D . Use Spot Instances for the frontend nodes. Use AWS Fargate for the backend nodes.
B
Explanation:
AWS documentation states that workloads running 24/7 should use Reserved Instances or Savings Plans for the lowest cost.
Therefore, the frontend nodes, which always run, should use Reserved Instances.
The backend nodes process asynchronous, restartable jobs, which makes them ideal for EC2 Spot
Instances, the most cost-effective compute option for interruption-tolerant workloads.
Fargate (Options A and D) is significantly more expensive for large compute usage. Spot Instances cannot be used for critical frontend nodes (Option C).
A company is building a serverless application to process clickstream data from its website. The clickstream data is sent to an Amazon Kinesis Data Streams data stream from the application web servers.
The company wants to enrich the clickstream data by joining the clickstream data with customer profile data from an Amazon Aurora Multi-AZ database. The company wants to use Amazon Redshift to analyze the enriched data. The solution must be highly available.
Which solution will meet these requirements?
- A . Use an AWS Lambda function to process and enrich the clickstream data. Use the same Lambda function to write the clickstream data to Amazon S3. Use Amazon Redshift Spectrum to query the enriched data in Amazon S3.
- B . Use an Amazon EC2 Spot Instance to poll the data stream and enrich the clickstream data. Configure the EC2 instance to use the COPY command to send the enriched results to Amazon Redshift.
- C . Use an Amazon Elastic Container Service (Amazon ECS) task with AWS Fargate Spot capacity to poll the data stream and enrich the clickstream data. Configure an Amazon EC2 instance to use the COPY command to send the enriched results to Amazon Redshift.
- D . Use Amazon Kinesis Data Firehose to load the clickstream data from Kinesis Data Streams to Amazon S3. Use AWS Glue crawlers to infer the schema and populate the AWS Glue Data Catalog. Use Amazon Athena to query the raw data in Amazon S3.
A
Explanation:
Option Ais the best solution as it leveragesAWS Lambdafor serverless, scalable, and highly available processing and enrichment of clickstream data. Lambda can process the data in real-time, join it with the Aurora database data, and write the enriched results to Amazon S3. FromS3, Amazon Redshift Spectrumcan directly query the enriched data without needing to load the data into Redshift, enabling cost efficiency and high availability.
Why Other Options Are Incorrect:
Option B: EC2 Spot Instances are not guaranteed to be highly available, as Spot Instances can be interrupted at any time. This does not align with the requirement for high availability.
Option C: While ECS with AWS Fargate provides scalability, using EC2 for the COPY command introduces operational overhead and compromises high availability.
Option D: Kinesis Data Firehose and Athena are suitable for querying raw data, but they do not directly support enriching the data by joining with Aurora. This solution fails to meet the requirement for data enrichment.
Key AWS Features Used:
AWS Lambda: Real-time serverless processing with integration capabilities for Aurora and S3.
Amazon S3: Cost-effective storage for enriched data.
Amazon Redshift Spectrum: Direct querying of data stored in S3 without loading it into Redshift.
AWS Documentation
Reference: AWS Lambda Function Overview
Amazon Redshift Spectrum
Processing Streaming Data with Kinesis Data Streams
A company wants to restrict access to the content of its web application. The company needs to protect the content by using authorization techniques that are available on AWS. The company also wants to implement a serverless architecture for authorization and authentication that has low login latency.
The solution must integrate with the web application and serve web content globally. The application currently has a small user base, but the company expects the application’s user base to increase
Which solution will meet these requirements?
- A . Configure Amazon Cognito for authentication. Implement Lambda@Edge for authorization. Configure Amazon CloudFront to serve the web application globally
- B . Configure AWS Directory Service for Microsoft Active Directory for authentication. Implement AWS Lambda for authorization. Use an Application Load Balancer to serve the web application globally.
- C . Configure Amazon Cognito for authentication. Implement AWS Lambda for authorization Use Amazon S3 Transfer Acceleration to serve the web application globally.
- D . Configure AWS Directory Service for Microsoft Active Directory for authentication. Implement Lambda@Edge for authorization. Use AWS Elastic Beanstalk to serve the web application globally.
A
Explanation:
Amazon Cognitoprovides scalable, serverless authentication, andLambda@Edgeis used for authorization, providing low-latency access control at the edge. Amazon CloudFrontserves the web application globally with reduced latency and ensures secure access for users around the world. This solution minimizes operational overhead while providing scalability and security.
Option B (Directory Service): Directory Service is more suitable for enterprise use cases involving Active Directory, not for web-based applications.
Option C (S3 Transfer Acceleration): S3 Transfer Acceleration helps with file transfers but does not provide authorization features.
Option D (Elastic Beanstalk): Elastic Beanstalk adds unnecessary overhead when CloudFront can handle global delivery efficiently.
AWS
Reference: Amazon Cognito
Lambda@Edge
A company is building a gaming application that needs to send unique events to multiple leaderboards, player matchmaking systems, and authentication services concurrently. The company requires an AWS-based event-driven system that delivers events in order and supports a publish-subscribe model. The gaming application must be the publisher, and the leaderboards, matchmaking systems, and authentication services must be the subscribers.
Which solution will meet these requirements?
- A . Amazon EventBridge event buses
- B . Amazon Simple Notification Service (Amazon SNS) FIFO topics
- C . Amazon Simple Notification Service (Amazon SNS) standard topics
- D . Amazon Simple Queue Service (Amazon SQS) FIFO queues
B
Explanation:
The requirement is an event-driven pub/sub system that guarantees ordered delivery of events.
Amazon SNS FIFO topics provide the publish-subscribe model along with FIFO (First-In-First-Out) delivery and exactly-once message processing, ensuring ordered delivery to multiple subscribers.
Option A, EventBridge, provides event buses but does not guarantee event ordering across multiple subscribers.
Option C (SNS standard topics) provides pub/sub but without ordering guarantees.
Option D (SQS FIFO queues) guarantees order but are point-to-point queues, not pub/sub.
Thus, Amazon SNS FIFO topics meet the requirements for ordered pub/sub messaging.
Reference: Amazon SNS FIFO Topics (https: //docs.aws.amazon.com/sns/latest/dg/fifo-topics.html)
Amazon EventBridge (https: //docs.aws.amazon.com/eventbridge/latest/userguide/what-is-amazon-eventbridge.html)
AWS Well-Architected Framework ― Performance Efficiency Pillar (https: //d1.awsstatic.com/whitepapers/architecture/AWS_Well-Architected_Framework.pdf)