SAA-C03 Exam Dumps : AWS Certified Solutions Architect - Associate (SAA-C03)

EMR managed scaling dynamically resizes the cluster by adding or removing nodes based on the workload. This feature helps minimize idle time and reduces costs by scaling the cluster to meet processing demands efficiently.

Option A:Increasing the number of core nodes might increase idle time further, as it does not address the root cause of underutilization.

Option C:Migrating the job to Lambda is infeasible for large analytics jobs due to resource and runtime constraints.

Option D:Changing to memory-optimized instances may not necessarily reduce idle time or optimize costs.

AWS Documentation References:

EMR Managed Scaling

The requirement to “have access to the operating system” means the compute layer must be Amazon EC2 (or containers on EC2). Managed runtimes such as Lambda and Fargate do not provide OS-level access.

The requirement for a “low-latency NoSQL database” maps directly to Amazon DynamoDB, which is a fully managed NoSQL key-value and document database that provides single-digit millisecond latency at any scale.

Because the application runs in a private subnet, AWS best practice is to access DynamoDB privately via a VPC endpoint (gateway endpoint for DynamoDB). This avoids traversing the public internet and simplifies security.

An instance profile (EC2 role) is the recommended method to grant EC2 instances permission to access DynamoDB without hardcoding credentials.

Why the other options are not correct:

B: Lambda does not provide OS access, which violates the security requirement.

C: Fargate does not provide OS access, and Aurora PostgreSQL is a relational database, not NoSQL.

D: S3 + Athena is an analytics pattern, not a low-latency NoSQL database solution; query latency is much higher and not suitable for OLTP-style app storage.

To decouple distributed workloads and improve scalability and resiliency, Amazon SQS should be used as a reliable job queue. The compute nodes (workers) can poll the SQS queue for tasks, and EC2 Auto Scaling can dynamically scale instances based on the ApproximateNumberOfMessages metric.

From AWS Documentation:

“Amazon SQS enables you to decouple application components, allowing each part to scale independently. You can scale EC2 instances automatically based on the number of messages in the queue.”

(Source: Amazon SQS Developer Guide – Scaling Consumers with Auto Scaling)

Why B is correct:

Eliminates the single point of failure (the primary coordinator).

Enables event-driven scaling based on queue depth.

Provides durability and resiliency since messages are stored redundantly.

Fully managed and integrates seamlessly with Auto Scaling policies.

Why other options are incorrect:

A: Scheduled scaling does not respond to variable workloads.

C & D: Misuse of CloudTrail/EventBridge; not intended for workload coordination.