AWS Certified DevOps Engineer – Professional (DOP-C02) — Question 46

A video-sharing company stores its videos in Amazon S3. The company has observed a sudden increase in video access requests, but the company does not know which videos are most popular. The company needs to identify the general access pattern for the video files. This pattern includes the number of users who access a certain file on a given day, as well as the number of pull requests for certain files.
How can the company meet these requirements with the LEAST amount of effort?

Answer options

• A. Activate S3 server access logging. Import the access logs into an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.
• B. Activate S3 server access logging. Use Amazon Athena to create an external table with the log files. Use Athena to create a SQL query to analyze the access patterns.
• C. Invoke an AWS Lambda function for every S3 object access event. Configure the Lambda function to write the file access information, such as user. S3 bucket, and file key, to an Amazon Aurora database. Use an Aurora SQL query to analyze the access patterns.
• D. Record an Amazon CloudWatch Logs log message for every S3 object access event. Configure a CloudWatch Logs log stream to write the file access information, such as user, S3 bucket, and file key, to an Amazon Kinesis Data Analytics for SQL application. Perform a sliding window analysis.

Correct answer: B

Explanation

The correct answer is B because activating S3 server access logging and using Amazon Athena allows for easy analysis of access patterns directly from the logs with minimal setup. Option A requires additional steps to import logs into Aurora, while C and D involve more complex configurations with Lambda and CloudWatch, making them less efficient than using Athena.