SAA-C03 Questions Bank

Comprehensive and Detailed Step-by-Step Explanation:

To accurately charge customers based on their monthly data download usage, the following solution is recommended:

Structured Logging Configuration:

Action:Ensure that the AWS Transfer for SFTP server is configured to log user activity, including details about file downloads, to Amazon S3 in a structured format.

Implementation:Utilize AWS Transfer Family ' s structured logging feature to capture detailed information about user sessions, including actions performed and data transferred.

docs.aws.amazon.com

Justification:Structured logs provide comprehensive data necessary for analyzing customer-specific download activities.

Data Analysis with Amazon Athena:

Action:Use Amazon Athena to run SQL queries on the structured log data stored in the S3 bucket to calculate the amount of data each customer has downloaded.

Implementation:

a.Define a Schema:Create a table in Athena that maps to the structure of your log files. This involves specifying the format of the logs and the location in S3.

b.Query Data:Write SQL queries to sum the total bytes downloaded by each customer over the billing period. This can be achieved by filtering logs based on user identifiers and summing the data transfer amounts.

Justification:Athena allows for efficient querying

AWS PrivateLink enables private connectivity between VPCs and supported AWS or third-party services without exposing traffic to the public internet. It ensures secure and private communications, making it ideal for connecting internal services to SaaS applications hosted in AWS.

Creating a manual copy of the automated snapshot is the most operationally efficient option because it directly meets the requirement—retain a specific snapshot beyond the automated retention window—with the least added process and tooling. In Amazon RDS, automated backups and their snapshots are retained only for the configured backup retention period (up to the service maximum). When that retention period is exceeded, older automated snapshots are removed automatically. However, manual snapshots are retained until you explicitly delete them, so converting (copying) an automated snapshot to a manual snapshot is the standard operational approach to keep a point-in-time backup for long-term retention.

Option C is incorrect because you cannot extend automated snapshot retention beyond the maximum supported retention; also, setting “45 days” may still be within or beyond the service limits depending on the engine and configuration, and it doesn’t guarantee indefinite retention. Option B (export to S3) is not the most operationally efficient for the stated goal: exporting is a different workflow (often used for analytics, archiving in open formats, or cross-tool usage) and introduces extra steps, format considerations, and ongoing management in S3. Option D is also heavier operationally: native SQL Server backups require managing backup jobs, storage layout, restores, and permissions, and it shifts responsibility to the customer for operational correctness.

Therefore, A is the simplest and most AWS-native way to preserve an RDS snapshot long-term with minimal operational overhead.

Amixed Auto Scaling groupwith both On-Demand and Spot Instances is the best answer because AWS documents that mixed instances groups let you launch and automatically scale a fleet of On-Demand and Spot Instances in a single Auto Scaling group to optimize cost while maintaining scale and performance. Since the workload can tolerate occasional disruptions, Spot is appropriate, but keeping some On-Demand capacity adds stability. This is a more practical architecture than moving the whole workload to Spot alone. Continuing with only On-Demand or buying Reserved Instances would not optimize cost as well for variable usage.

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