Free AWS SAA-C03 practice questions

10 free AWS SAA-C03 practice questions with the correct answer and a full explanation for each, taken from the CertStash pack of 1,019 questions. Work through them, then open each answer to check your reasoning.

Question 1

A company collects data for temperature, humidity, and atmospheric pressure in cities across multiple continents. The average volume of data that the company collects from each site daily is 500 GB. Each site has a high-speed Internet connection.

The company wants to aggregate the data from all these global sites as quickly as possible in a single Amazon S3 bucket. The solution must minimize operational complexity.

Which solution meets these requirements?

  1. Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.
  2. Upload the data from each site to an S3 bucket in the closest Region. Use S3 Cross-Region Replication to copy objects to the destination S3 bucket. Then remove the data from the origin S3 bucket.
  3. Schedule AWS Snowball Edge Storage Optimized device jobs daily to transfer data from each site to the closest Region. Use S3 CrossRegion Replication to copy objects to the destination S3 bucket.
  4. Upload the data from each site to an Amazon EC2 instance in the closest Region. Store the data in an Amazon Elastic Block Store (Amazon EBS) volume. At regular intervals, take an EBS snapshot and copy it to the Region that contains the destination S3 bucket. Restore the EBS volume in that Region.
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Correct answer: A. Turn on S3 Transfer Acceleration on the destination S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket.

S3 bucket. Use multipart uploads to directly upload site data to the destination S3 bucket. S3 Transfer Acceleration uses CloudFront's globally distributed edge locations to accelerate uploads to S3, making it ideal for geographically dispersed sites with high- speed internet connections uploading large volumes daily. Multipart uploads further optimize the transfer of large files. This solution minimizes operational complexity by requiring no intermediate infrastructure or cross-region replication logic.

Why the other options are wrong

  • B. Requires maintaining multiple S3 buckets and cross-region replication adds latency and complexity compared to direct acceleration.
  • C. Snowball Edge is designed for one-time bulk transfers, not daily 500 GB uploads, and introduces significant operational overhead.
  • D. Using EC2 instances and EBS snapshots is unnecessarily complex and slower than direct S3 acceleration for this use case.

Question 2

A company needs the ability to analyze the log files of its proprietary application. The logs are stored in JSON format in an Amazon S3 bucket.

Queries will be simple and will run on-demand. A solutions architect needs to perform the analysis with minimal changes to the existing architecture.

What should the solutions architect do to meet these requirements with the LEAST amount of operational overhead?

  1. Use Amazon Redshift to load all the content into one place and run the SQL queries as needed.
  2. Use Amazon CloudWatch Logs to store the logs. Run SQL queries as needed from the Amazon CloudWatch console.
  3. Use Amazon Athena directly with Amazon S3 to run the queries as needed.
  4. Use AWS Glue to catalog the logs. Use a transient Apache Spark cluster on Amazon EMR to run the SQL queries as needed.
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Correct answer: C. Use Amazon Athena directly with Amazon S3 to run the queries as needed.

the queries as needed. Amazon Athena is purpose-built for running ad-hoc SQL queries directly against data stored in S3 without requiring any infrastructure setup or data movement. It natively supports JSON format and requires minimal operational overhead, making it the least complex solution for on-demand log analysis.

Why the other options are wrong

  • A. Redshift requires loading data into a cluster and managing that infrastructure, adding unnecessary complexity and cost for simple on-demand queries.
  • B. CloudWatch Logs is not designed for analyzing large volumes of existing S3 data and requires moving data out of S3.
  • D. AWS Glue and EMR require significant setup, cluster management, and ongoing operational overhead compared to Athena.

Question 3

A company uses AWS Organizations to manage multiple AWS accounts for different departments. The management account has an Amazon S3 bucket that contains project reports. The company wants to limit access to this S3 bucket to only users of accounts within the organization in AWS Organizations.

Which solution meets these requirements with the LEAST amount of operational overhead?

  1. Add the aws PrincipalOrgID global condition key with a reference to the organization ID to the S3 bucket policy.
  2. Create an organizational unit (OU) for each department. Add the aws:PrincipalOrgPaths global condition key to the S3 bucket policy.
  3. Use AWS CloudTrail to monitor the CreateAccount, InviteAccountToOrganization, LeaveOrganization, and RemoveAccountFromOrganization events. Update the S3 bucket policy accordingly.
  4. Tag each user that needs access to the S3 bucket. Add the aws:PrincipalTag global condition key to the S3 bucket policy.
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Correct answer: A. Add the aws PrincipalOrgID global condition key with a reference to the organization ID to the S3 bucket policy.

a reference to the organization ID to the S3 bucket policy. The aws:PrincipalOrgID global condition key restricts access to principals from any account within the specified organization, automatically covering all current and future organization members with a single policy statement. This requires no ongoing maintenance when accounts are added or removed from the organization.

Why the other options are wrong

  • B. PrincipalOrgPaths requires tracking and managing specific OU structures in the bucket policy, adding operational overhead.
  • C. CloudTrail monitoring requires manual policy updates whenever accounts join or leave, creating significant operational burden.
  • D. Manual tagging of users does not scale across an organization and requires continuous updates as users change roles or departments.

Question 4

An application runs on an Amazon EC2 instance in a VPC. The application processes logs that are stored in an Amazon S3 bucket. The EC2 instance needs to access the S3 bucket without connectivity to the internet.

Which solution will provide private network connectivity to Amazon S3?

  1. Create a gateway VPC endpoint to the S3 bucket.
  2. Stream the logs to Amazon CloudWatch Logs. Export the logs to the S3 bucket.
  3. Create an instance profile on Amazon EC2 to allow S3 access.
  4. Create an Amazon API Gateway API with a private link to access the S3 endpoint.
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Correct answer: A. Create a gateway VPC endpoint to the S3 bucket.

A gateway VPC endpoint for S3 provides private network connectivity from EC2 instances to S3 without requiring internet access. It uses route table entries to route S3 traffic through AWS's private backbone, and works with IAM roles for access control.

Why the other options are wrong

  • B. CloudWatch Logs export is an unnecessary intermediary that adds complexity rather than providing direct private access.
  • C. An instance profile grants IAM permissions but does not provide private network connectivity, internet access would still be required.
  • D. API Gateway with private link is overly complex for direct S3 access and not the standard approach for this requirement.

Question 5

A company is hosting a web application on AWS using a single Amazon EC2 instance that stores user-uploaded documents in an Amazon EBS volume. For better scalability and availability, the company duplicated the architecture and created a second EC2 instance and EBS volume in another Availability Zone, placing both behind an Application Load Balancer. After completing this change, users reported that, each time they refreshed the website, they could see one subset of their documents or the other, but never all of the documents at the same time.

What should a solutions architect propose to ensure users see all of their documents at once?

  1. Copy the data so both EBS volumes contain all the documents
  2. Configure the Application Load Balancer to direct a user to the server with the documents
  3. Copy the data from both EBS volumes to Amazon EFS. Modify the application to save new documents to Amazon EFS
  4. Configure the Application Load Balancer to send the request to both servers. Return each document from the correct server
Show answer and explanation

Correct answer: C. Copy the data from both EBS volumes to Amazon EFS. Modify the application to save new documents to Amazon EFS

EFS. Modify the application to save new documents to Amazon EFS Amazon EFS provides a shared, managed NFS file system that both EC2 instances can access simultaneously, ensuring all users see the complete set of documents regardless of which instance handles their request. This is the standard solution for applications requiring shared state across multiple instances.

Why the other options are wrong

  • A. Copying data between EBS volumes requires manual synchronization and does not solve the ongoing consistency problem for new uploads.
  • B. Session persistence does not work well with distributed uploads and does not address the architectural issue of separate storage per instance.
  • D. Request distribution across servers and returning documents from correct servers is complex, error-prone, and does not solve the fundamental shared storage problem.

Question 6

A company uses NFS to store large video files in on-premises network attached storage. Each video file ranges in size from 1 MB to 500 GB. The total storage is 70 TB and is no longer growing. The company decides to migrate the video files to Amazon S3. The company must migrate the video files as soon as possible while using the least possible network bandwidth.

Which solution will meet these requirements?

  1. Create an S3 bucket. Create an IAM role that has permissions to write to the S3 bucket. Use the AWS CLI to copy all files locally to the S3 bucket.
  2. Create an AWS Snowball Edge job. Receive a Snowball Edge device on premises. Use the Snowball Edge client to transfer data to the device. Return the device so that AWS can import the data into Amazon S3.
  3. Deploy an S3 File Gateway on premises. Create a public service endpoint to connect to the S3 File Gateway. Create an S3 bucket. Create a new NFS file share on the S3 File Gateway. Point the new file share to the S3 bucket. Transfer the data from the existing NFS file share to the S3 File Gateway.
  4. Set up an AWS Direct Connect connection between the on-premises network and AWS. Deploy an S3 File Gateway on premises. Create a public virtual interface (VIF) to connect to the S3 File Gateway. Create an S3 bucket. Create a new NFS file share on the S3 File Gateway. Point the new file share to the S3 bucket. Transfer the data from the existing NFS file share to the S3 File Gateway.
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Correct answer: B. Create an AWS Snowball Edge job. Receive a Snowball Edge device on premises. Use the Snowball Edge client to transfer data to the device. Return the device so that AWS can import the data into Amazon S3.

Snowball Edge device on premises. Use the Snowball Edge client to transfer data to the device. Return the device so that AWS can import the data into Amazon S3. AWS Snowball Edge is purpose-built for migrating large datasets (70 TB fits within device capacity) in a single operation, using the least network bandwidth by physically transporting data. This is significantly faster and more bandwidth-efficient than any network-based approach for one-time bulk transfers of this scale.

Why the other options are wrong

  • A. AWS CLI copy over the network would consume enormous bandwidth and take far longer than physical transfer.
  • C. S3 File Gateway still transfers data over the network, consuming the same bandwidth as a direct approach without the speed benefits of Snowball.
  • D. Direct Connect with S3 File Gateway still uses network bandwidth and adds the complexity and cost of Direct Connect setup without the efficiency of physical data transport.

Question 7

A company has an application that ingests incoming messages. Dozens of other applications and microservices then quickly consume these messages. The number of messages varies drastically and sometimes increases suddenly to 100,000 each second. The company wants to decouple the solution and increase scalability.

Which solution meets these requirements?

  1. Persist the messages to Amazon Kinesis Data Analytics. Configure the consumer applications to read and process the messages.
  2. Deploy the ingestion application on Amazon EC2 instances in an Auto Scaling group to scale the number of EC2 instances based on CPU metrics.
  3. Write the messages to Amazon Kinesis Data Streams with a single shard. Use an AWS Lambda function to preprocess messages and store them in Amazon DynamoDB. Configure the consumer applications to read from DynamoDB to process the messages.
  4. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues.
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Correct answer: D. Publish the messages to an Amazon Simple Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues.

Notification Service (Amazon SNS) topic with multiple Amazon Simple Queue Service (Amazon SOS) subscriptions. Configure the consumer applications to process the messages from the queues. Amazon SQS provides durable message queuing that decouples producers from consumers, and SNS with multiple SQS subscriptions enables multiple independent consumer applications to process the same messages. This architecture naturally scales to handle sudden spikes to 100,000 messages per second and allows each consumer application to scale independently.

Why the other options are wrong

  • A. Kinesis Data Analytics is for stream processing and analysis, not for decoupling and fan-out to multiple independent consumer applications.
  • B. Auto Scaling EC2 based on CPU does not decouple the ingestion from consumption and doesn't address the fan-out requirement for multiple consumers.
  • C. Single Kinesis shard is a bottleneck for 100,000 messages per second, and DynamoDB adds unnecessary storage complexity when a queue is the appropriate pattern.

Question 8

A company is migrating a distributed application to AWS. The application serves variable workloads. The legacy platform consists of a primary server that coordinates jobs across

multiple compute nodes. The company wants to modernize the application with a solution that maximizes resiliency and scalability.

How should a solutions architect design the architecture to meet these requirements?

  1. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling to use scheduled scaling.
  2. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling based on the size of the queue.
  3. Implement the primary server and the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure AWS CloudTrail as a destination for the jobs. Configure EC2 Auto Scaling based on the load on the primary server.
  4. Implement the primary server and the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure Amazon EventBridge (Amazon CloudWatch Events) as a destination for the jobs. Configure EC2 Auto Scaling based on the load on the compute nodes.
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Correct answer: B. Configure an Amazon Simple Queue Service (Amazon SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling based on the size of the queue.

SQS) queue as a destination for the jobs. Implement the compute nodes with Amazon EC2 instances that are managed in an Auto Scaling group. Configure EC2 Auto Scaling based on the size of the queue. SQS serves as a distributed job queue replacing the legacy primary server coordination role, and EC2 Auto Scaling based on queue depth (ApproximateNumberOfMessages) naturally scales compute capacity to match workload demand. This maximizes resiliency by removing the single point of failure of a primary server and provides true elasticity for variable workloads.

Why the other options are wrong

  • A. Scheduled scaling does not respond to actual demand and cannot handle unexpected workload spikes characteristic of variable workloads.
  • C. Keeping the primary server eliminates the resiliency benefit and contradicts modernization; CloudTrail is an audit tool, not a job destination.
  • D. EventBridge is not designed as a job queue, and scaling based on compute node load is reactive rather than proactive like queue-based scaling.

Question 9

A company is running an SMB file server in its data center. The file server stores large files that are accessed frequently for the first few days after the files are created. After 7 days the files are rarely accessed.

The total data size is increasing and is close to the company's total storage capacity. A solutions architect must increase the company's available storage space without losing low-latency access to the most recently accessed files. The solutions architect must also provide file lifecycle management to avoid future storage issues.

Which solution will meet these requirements?

  1. Use AWS DataSync to copy data that is older than 7 days from the SMB file server to AWS.
  2. Create an Amazon S3 File Gateway to extend the company's storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days.
  3. Create an Amazon FSx for Windows File Server file system to extend the company's storage space.
  4. Install a utility on each user's computer to access Amazon S3. Create an S3 Lifecycle policy to transition the data to S3 Glacier Flexible Retrieval after 7 days. ✅Correct Answer: B, Create an Amazon S3 File Gateway to extend the company's storage space. Create an S3 Lifecycle policy to transition the data to S3 Glacier Deep Archive after 7 days. Amazon S3 File Gateway presents an SMB share backed by Amazon S3 and caches recently accessed files locally, so storage capacity grows while recent files keep low-latency access. An S3 Lifecycle policy then moves objects to S3 Glacier Deep Archive after 7 days, giving the required automatic file lifecycle management and preventing future capacity problems. This meets both requirements with one managed solution.
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Answer and explanation for question 9

Question 10

A company is building an ecommerce web application on AWS. The application sends information about new orders to an Amazon API Gateway REST API to process. The company wants to ensure that orders are processed in the order that they are received.

Which solution will meet these requirements?

  1. Use an API Gateway integration to publish a message to an Amazon Simple Notification Service (Amazon SNS) topic when the application receives an order. Subscribe an AWS Lambda function to the topic to perform processing.
  2. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) FIFO queue when the application receives an order. Configure the SQS FIFO queue to invoke an AWS Lambda function for processing.
  3. Use an API Gateway authorizer to block any requests while the application processes an order.
  4. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) standard queue when the application receives an order. Configure the SQS standard queue to invoke an AWS Lambda function for processing.
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Correct answer: B. Use an API Gateway integration to send a message to an Amazon Simple Queue Service (Amazon SQS) FIFO queue when the application receives an order. Configure the SQS FIFO queue to invoke an AWS Lambda function for processing.

an Amazon Simple Queue Service (Amazon SQS) FIFO queue when the application receives an order. Configure the SQS FIFO queue to invoke an AWS Lambda function for processing. SQS FIFO (First-In-First-Out) queues guarantee that messages are processed in the exact order they are received, which is critical for order processing. When API Gateway sends messages to an SQS FIFO queue and invokes a Lambda function for processing, the ordering guarantee is maintained throughout. FIFO queues are specifically designed for scenarios where message order matters.

Why the other options are wrong

  • A. SNS topics do not guarantee message ordering; they distribute messages to subscribers without maintaining order.
  • C. API Gateway authorizers cannot block requests during processing; they authenticate/authorize requests but don't manage order processing.
  • D. SQS standard queues do not guarantee ordering; messages can be processed out of order, violating the requirement.

That was 10 of 1,019.

The full AWS SAA-C03 pack has all 1,019 questions, each with the answer, the explanation and why the other options are wrong, plus a questions-only copy for timed runs. US$39, paid once, with free monthly updates and a pass-or-your-money-back guarantee.

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