MLS-C01 Practice Question: Machine Learning Implementation and Operations
A data scientist needs to process a large dataset (100 TB) for training a machine learning model. The data is stored in Amazon S3. Which approach is most cost-effective and efficient for data processing?
⚠ Common exam trap
A common mix-up: candidates choose AWS Glue (Option A) because it is marketed as a serverless ETL service, but they overlook that for 100 TB, Glue's per-DPU pricing and lack of distributed processing optimizations make it less cost-effective and slower than EMR with Spark, which is purpose-built for big data workloads.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use Amazon EMR with Apache Spark.
Amazon EMR with Apache Spark is the most cost-effective and efficient approach for processing 100 TB of data stored in S3 because it provides a managed, scalable cluster that can process large datasets in parallel using in-memory computation. EMR integrates natively with S3 via the EMRFS connector, allowing data to be read directly from S3 without the need for intermediate storage, and it supports auto-scaling and spot instances to reduce costs. For petabyte-scale data, Spark's distributed processing engine outperforms single-node solutions and is more flexible than SQL-only or ETL-only services.
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 AWS Glue ETL jobs.
Why it's wrong here
Glue is serverless but may be slower.
- ✓
Use Amazon EMR with Apache Spark.
Why this is correct
Distributed processing is efficient for large data.
- ✗
Use Amazon Athena to run SQL queries.
Why it's wrong here
Athena is for ad-hoc querying, not complex processing.
- ✗
Use Amazon SageMaker Processing with a single large instance.
Why it's wrong here
Single instance may be insufficient.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
Go deeper
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This MLS-C01 practice question is part of Courseiva's free Amazon Web Services certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the MLS-C01 exam.