Amazon Web Services Updated DVA-C02 Exam Questions and Answers by mahnoor

Requirement Summary:

Lambda function accesses RDS for MySQL

Credentials are currently hardcoded in code (insecure)

Must enable automated credential rotation every 30 days

Option A: Use AWS Secrets Manager + automatic rotation

Best and secure option

Secrets Manager allows:

Secure storage of secrets

Integration with RDS for automatic rotation

Scheduled rotation every X days (e.g., 30 days)

Lambda can fetch credentials via SDK (GetSecretValue)

Option B: Use SSM Parameter Store + rotation

SSM does not support automatic rotation of secrets.

You'd need to build custom rotation logic = higher operational overhead.

Option C: Encrypted S3 + Object Lambda rotation

Not intended for credential storage.

S3 is not a secrets management system, and Object Lambda does not perform rotation.

Option D: SSM + EventBridge rotation

No native integration between Parameter Store and EventBridge for secret rotation.

You’d need to build custom Lambda functions = higher maintenance.

Secrets Manager rotation for RDS: https://docs.aws.amazon.com/secretsmanager/latest/userguide/rotating-secrets.html

Secure retrieval in Lambda: https://docs.aws.amazon.com/secretsmanager/latest/userguide/retrieving-secrets_lambda.html

Integration with RDS MySQL: https://docs.aws.amazon.com/secretsmanager/latest/userguide/integrating_rds_config.html

The workload is explicitly very I/O intensive and requires reading the same file multiple times. Reading repeatedly from S3 across the network will introduce latency and repeated network I/O. For best performance, the function should download the file once to fast local storage and perform all repeated reads locally.

AWS Lambda provides ephemeral storage mounted at /tmp. AWS allows increasing this ephemeral storage beyond the default size. Because the files are at most 2 GB, the developer can configure the Lambda function’s ephemeral storage to 2 GB and copy each S3 object into /tmp before processing. This converts repeated network reads into repeated local filesystem reads, which is significantly faster and more consistent for I/O-heavy workloads.

Option D (read directly from S3) is the least optimized because each pass over the file requires additional network I/O and S3 GET requests.

Option A is not feasible: Lambda cannot directly attach and manage an EBS volume like an EC2 instance can.

Option B is not applicable: ENA is an EC2 networking feature; Lambda networking is managed by the service and does not allow attaching ENAs for performance tuning in that way.

Because the files are disposable after processing, using ephemeral /tmp storage is also cost-effective and operationally simple, and it is aligned with the “process once per file” pattern.

Therefore, increase Lambda ephemeral storage and copy the video into /tmp for repeated reads.

This solution will meet the requirements by using DynamoDB Accelerator (DAX), which is a fully managed, highly available, in-memory cache for DynamoDB that delivers up to a 10 times performance improvement - from milliseconds to microseconds - even at millions of requests per second. The developer can use DAX to cache the trading data that is used to process each trading request, which will reduce the data retrieval time with the least possible effort. Option A is not optimal because it will add local secondary indexes (LSIs) for the trading data, which may not improve the performance or reduce the latency of data retrieval, as LSIs are stored on the same partition as the base table and share the same provisioned throughput. Option B is not optimal because it will store the trading data in Amazon S3 and use S3 Transfer Acceleration, which is a feature that enables fast, easy, and secure transfers of files over long distances between S3 buckets and clients, not between DynamoDB and clients. Option C is not optimal because it will add retries with exponential backoff for DynamoDB queries, which is a strategy to handle transient errors by retrying failed requests with increasing delays, not by reducing data retrieval time.