AWS Certified DevOps Engineer - Professional
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A video-sharing company that stores its videos in Amazon S3 has noticed a significant increase in video access requests. However, they lack insight into which videos are the most popular. To better understand their audience, the company requires a method to identify the general access patterns of their video files. This includes tracking the number of users accessing specific files on any given day, as well as monitoring the frequency of pull requests for these files. What is the most efficient and least labor-intensive approach for the company to achieve this analysis?
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.