AWS Certified DevOps Engineer – Professional — Question 178

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 using Amazon Athena allows for quick analysis of the S3 server access logs without needing to set up a database. Option A involves more effort by requiring database management, while C introduces unnecessary complexity with Lambda functions, and D adds additional services that complicate the solution.