SAA-C03 Design Resilient Architectures Practice Question

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

• ✗

  Use an Auto Scaling group with a single instance to process the S3 files sequentially.

  Why it's wrong here

  A single instance is a single point of failure. If that instance crashes during a long batch process, the entire job might be lost or stuck. This design lacks the necessary resilience to handle failures during long-running tasks, potentially resulting in significant data processing delays or total failures.

• ✓

  Use an SQS queue to store tasks and an Auto Scaling group to process them.

  Why this is correct

  This architecture decouples the workload from the compute. If an instance fails, the message remains in the queue to be retried. The Auto Scaling group ensures that sufficient compute power is always available to clear the queue, providing a highly resilient and scalable solution for long-running batch processing.

• ✗

  Directly trigger Lambda functions from S3 and increase the timeout.

  Why it's wrong here

  Lambda has a hard 15-minute execution limit. For batch processes that take hours, this is fundamentally incompatible. Even with retries, the architecture is not well-suited for long-running batch operations and would likely result in repeated function failures, leading to inefficient resource usage and a lack of overall process resilience.

• ✗

  Use a single large EC2 instance to process the data in memory.

  Why it's wrong here

  Relying on a single large instance is a high-risk strategy. If the instance fails or the memory is exhausted, the entire process is lost. This monolithic approach lacks the fault tolerance and horizontal scalability needed for resilient, reliable batch processing in a production environment where job completion is critical.