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AWS Certified Solutions Architect - Associate SAA-C03 Questions 276–280 (page: 57)

Updated On: 16-Mar-2026

A company has a multi-tier application deployed on several Amazon EC2 instances in an Auto Scaling group. An Amazon RDS for Oracle instance is the application’ s data layer that uses Oracle-specific PL/SQL functions. Traffic to the application has been steadily increasing. This is causing the EC2 instances to become overloaded and the RDS instance to run out of storage. The Auto Scaling group does not have any scaling metrics and defines the minimum healthy instance count only. The company predicts that traffic will continue to increase at a steady but unpredictable rate before leveling off.
What should a solutions architect do to ensure the system can automatically scale for the increased traffic? (Choose two.)

  1. Configure storage Auto Scaling on the RDS for Oracle instance.
  2. Migrate the database to Amazon Aurora to use Auto Scaling storage.
  3. Configure an alarm on the RDS for Oracle instance for low free storage space.
  4. Configure the Auto Scaling group to use the average CPU as the scaling metric.
  5. Configure the Auto Scaling group to use the average free memory as the scaling metric.

Answer(s): A,D

Explanation:

The correct choices enable dynamic scaling of both compute and storage to handle rising load. A) RDS storage Auto Scaling increases storage automatically when needed, preventing the Oracle DB from running out of space as traffic grows. D) Using average CPU as the ASG scaling metric allows adding instances in response to higher compute demand, matching the increasing workload.
B) Migrating to Aurora is not necessary for Auto Scaling storage and adds migration risk; not the asked solution. C) Low free storage alarm is reactive, not automatic scaling. E) Free memory is not a reliable ASG metric for EC2 auto scaling in this context; CPU is the standard scaling signal.



A company provides an online service for posting video content and transcoding it for use by any mobile platform. The application architecture uses Amazon Elastic File System (Amazon EFS) Standard to collect and store the videos so that multiple Amazon EC2 Linux instances can access the video content for processing. As the popularity of the service has grown over time, the storage costs have become too expensive.
Which storage solution is MOST cost-effective?

  1. Use AWS Storage Gateway for files to store and process the video content.
  2. Use AWS Storage Gateway for volumes to store and process the video content.
  3. Use Amazon EFS for storing the video content. Once processing is complete, transfer the files to Amazon Elastic Block Store (Amazon EBS).
  4. Use Amazon S3 for storing the video content. Move the files temporarily over to an Amazon Elastic Block Store (Amazon EBS) volume attached to the server for processing.

Answer(s): D

Explanation:

Amazon S3 is the most cost-effective scalable storage for infrequently accessed or variable video content, with lifecycle policies and tiering (e.g., S3 Standard-IA, Glacier) reducing ongoing costs while still allowing high-throughput access for processing. Processing directly from S3 avoids the cost and complexity of EFS or EBS for large, shared datasets. A) Storage Gateway file interface adds on-premises illusion and latency, not cost savings. B) Gateway for volumes is for block storage on-premises, not ideal for shared EC2 access. C) EFS is already in use and not as cost-efficient for long-term storage. D) Moving data to EBS adds per-volume costs and does not optimize storage economics.



A company wants to create an application to store employee data in a hierarchical structured relationship. The company needs a minimum-latency response to high-traffic queries for the employee data and must protect any sensitive data. The company also needs to receive monthly email messages if any financial information is present in the employee data.
Which combination of steps should a solutions architect take to meet these requirements? (Choose two.)

  1. Use Amazon Redshift to store the employee data in hierarchies. Unload the data to Amazon S3 every month.
  2. Use Amazon DynamoDB to store the employee data in hierarchies. Export the data to Amazon S3 every month.
  3. Configure Amazon Macie for the AWS account. Integrate Macie with Amazon EventBridge to send monthly events to AWS Lambda.
  4. Use Amazon Athena to analyze the employee data in Amazon S3. Integrate Athena with Amazon QuickSight to publish analysis dashboards and share the dashboards with users.
  5. Configure Amazon Macie for the AWS account. Integrate Macie with Amazon EventBridge to send monthly notifications through an Amazon Simple Notification Service (Amazon SNS) subscription.

Answer(s): B,E

Explanation:

B) E)
B correctly uses DynamoDB for low-latency, high-traffic hierarchical data access and exports monthly to S3 for durable storage/analysis, aligning with the need for fast responses and data protection via managed AWS services. E uses Macie for data discovery/classification and EventBridge to trigger monthly notifications, delivering the required monthly alerts about sensitive financial information via SNS.
A is incorrect because Redshift is for analytic workloads with higher latency and involves ETL to S3, not optimal for low-latency hierarchical lookups. C is incorrect because Macie alerts are classifications, not a direct monthly financial-notification mechanism via EventBridge + SNS. D is incorrect because Athena/QuickSight provide analytics, not the specified alerting for sensitive data.



A company has an application that is backed by an Amazon DynamoDB table. The company’s compliance requirements specify that database backups must be taken every month, must be available for 6 months, and must be retained for 7 years.
Which solution will meet these requirements?

  1. Create an AWS Backup plan to back up the DynamoDB table on the first day of each month. Specify a lifecycle policy that transitions the backup to cold storage after 6 months. Set the retention period for each backup to 7 years.
  2. Create a DynamoDB on-demand backup of the DynamoDB table on the first day of each month. Transition the backup to Amazon S3 Glacier Flexible Retrieval after 6 months. Create an S3 Lifecycle policy to delete backups that are older than 7 years.
  3. Use the AWS SDK to develop a script that creates an on-demand backup of the DynamoDB table. Set up an Amazon EventBridge rule that runs the script on the first day of each month. Create a second script that will run on the second day of each month to transition DynamoDB backups that are older than 6 months to cold storage and to delete backups that are older than 7 years.
  4. Use the AWS CLI to create an on-demand backup of the DynamoDB table. Set up an Amazon EventBridge rule that runs the command on the first day of each month with a cron expression. Specify in the command to transition the backups to cold storage after 6 months and to delete the backups after 7 years.

Answer(s): A

Explanation:

A) AWS Backup can manage DynamoDB backups with a monthly schedule, apply a lifecycle to move to a cheaper storage class after 6 months, and set a retention period of 7 years, meeting all three requirements in a centralized, compliant manner.
B) DynamoDB on-demand backups do not natively support automated lifecycle transitions to S3 Glacier or a 7-year retention policy; separate steps would be required, making it less automated and not aligned with built-in lifecycle features.
C) Custom scripts with EventBridge introduce operational complexity and potential gaps; lifecycle transitions and long-term retention are not as reliably enforced as with a managed AWS Backup plan.
D) AWS CLI + EventBridge without built-in retention policy does not provide centralized lifecycle management; transitions and deletions would rely on ad-hoc scripting, risking non-compliance.



A company is using Amazon CloudFront with its website. The company has enabled logging on the CloudFront distribution, and logs are saved in one of the company’s Amazon S3 buckets. The company needs to perform advanced analyses on the logs and build visualizations.
What should a solutions architect do to meet these requirements?

  1. Use standard SQL queries in Amazon Athena to analyze the CloudFront logs in the S3 bucket. Visualize the results with AWS Glue.
  2. Use standard SQL queries in Amazon Athena to analyze the CloudFront logs in the S3 bucket. Visualize the results with Amazon QuickSight.
  3. Use standard SQL queries in Amazon DynamoDB to analyze the CloudFront logs in the S3 bucket. Visualize the results with AWS Glue.
  4. Use standard SQL queries in Amazon DynamoDB to analyze the CloudFront logs in the S3 bucket. Visualize the results with Amazon QuickSight.

Answer(s): B

Explanation:

Athena supports SQL queries directly over data stored in S3, including CloudFront access logs, and QuickSight provides visualizations from Athena query results, making B the correct choice.
A incorrect: Visualizing Athena results with AWS Glue is not a visualization service; Glue is for ETL/cataloging, not dashboards.
C incorrect: DynamoDB is a NoSQL database and does not natively query data in S3; CloudFront logs in S3 require Athena, not DynamoDB.
D incorrect: DynamoDB for analysis is unsuitable; CloudFront logs reside in S3 and must be queried with Athena, with QuickSight for visualization.



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