SAA-C03 Q365 · Analytics

Question

A company is developing a platform to process large volumes of data for complex analytics and machine learning (ML) tasks. The platform must handle compute-intensive workloads. The workloads currently require 20 to 30 minutes for each data processing step. The company wants a solution to accelerate data processing. Which solution will meet these requirements with the LEAST operational overhead?

Accepted Answer

**Answer B: Amazon EMR with managed scaling and Apache Spark** meets every requirement:

1. **Compute-intensive workloads**: Amazon EMR is purpose-built for large-scale data processing. It provisions clusters of EC2 instances (including GPU/memory-optimized types) that can handle heavy computation without the 15-minute timeout or 10GB memory limit of Lambda.

2. **Complex analytics and ML tasks**: Apache Spark is the industry-standard distributed processing framework for analytics and ML. Spark MLlib provides native ML capabilities. EMR comes with pre-configured Spark integration, removing setup complexity.

3. **Accelerate data processing**: Spark's in-memory distributed computing can dramatically reduce processing times. Work that takes 20-30 minutes sequentially can be parallelized acro

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🏗️ Architecture

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