Amazon Web Services Updated SAA-C03 Exam Questions and Answers by mitchell

Big data workloads that need to process terabytes of data in memory requirememory-optimized instancesfor the core and task nodes to ensure sufficient memory for processing data efficiently.

Primary Node:Ageneral purpose instanceis suitable because it manages cluster operations, including coordination and monitoring, and does not process data directly.

Core and Task Nodes:These nodes handle data storage and processing.Memory-optimized instancesare ideal because they provide high memory-to-CPU ratios, which is critical for in-memory big data workloads.

Why Other Options Are Incorrect:

Option A:Storage optimized and compute optimized instances are not suitable for workloads that rely heavily on in-memory processing.

Option B:A memory-optimized primary node is unnecessary because the primary node does not process data.

Option D:General purpose instances for all nodes will not provide sufficient memory for processing terabytes of data in memory.

AWS Documentation References:

Amazon EMR Instance Types

Memory-Optimized Instances

The Requester Pays feature in Amazon S3 allows the bucket owner to configure the bucket so that the requester, rather than the bucket owner, pays for data transfer and request costs. This is ideal for sharing large datasets with the public or with other AWS accounts when you want to minimize your own data transfer expenses.

Reference Extract from AWS Documentation / Study Guide:

"Requester Pays buckets allow you to configure the bucket so that the requester instead of the bucket owner pays the cost of the request and the data download from the bucket."

Source: AWS Certified Solutions Architect – Official Study Guide, S3 Cost Management section.

Lambda@Edge allows you to run functions at CloudFront edge locations, enabling modifications to content before it's delivered to users, including compression — which directly reduces data transfer cost.

“Lambda@Edge can be used to compress or modify your content before delivering it to end users, which reduces the amount of data transferred.”

— Lambda@Edge Use Cases

Since code modifications aren’t allowed, using Lambda@Edge is a non-invasive way to:

Compress responses.

Reduce transfer size = lower CloudFront cost.

Incorrect Options:

B: S3 Transfer Acceleration improves speed, not cost.

C: Caching doesn’t help if content is always unique (single-use).

D: Multipart uploads help with large file uploads, not transfers.

Amazon ECS with AWS Fargate provides serverless container compute that can scale tasks dynamically in response to demand. This matches the unpredictable scaling requirements of a gaming workload. Aurora Serverless v2 provides on-demand, autoscaling relational database capacity while supporting complex SQL queries. EC2 with Auto Scaling (A) works but requires significant management overhead. Lambda with DynamoDB (B) is not suitable because the workload requires a relational database and long-running processes. ParallelCluster with HPC (D) is designed for batch scientific workloads, not dynamic, interactive gaming. Option C provides the correct balance of elasticity, performance, and managed services for both compute and relational database needs.