- A
Use AWS Glue ETL jobs.
Why wrong: Glue is serverless but may be slower.
- B
Use Amazon EMR with Apache Spark.
Distributed processing is efficient for large data.
- C
Use Amazon Athena to run SQL queries.
Why wrong: Athena is for ad-hoc querying, not complex processing.
- D
Use Amazon SageMaker Processing with a single large instance.
Why wrong: Single instance may be insufficient.
Question 221 of 1,755
MLS-C01 Practice Question: Machine Learning Implementation and Operations
This MLS-C01 practice question tests your understanding of machine learning implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
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?
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.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
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.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
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.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often 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.
Detailed technical explanation
How to think about this question
Under the hood, EMR leverages the YARN resource manager to distribute Spark executors across multiple EC2 instances, enabling data locality with S3 via the EMRFS consistent view and the S3A filesystem protocol. A key subtlety is that EMR can use spot instances for task nodes (up to 90% cost reduction) while keeping core nodes on-demand for reliability, which is critical for cost-effective large-scale processing. In a real-world scenario, a data scientist might use EMR with Spark to perform feature engineering on 100 TB of clickstream data, partitioning by date and applying window functions that would be impractical with Athena or Glue.
KKey Concepts to Remember
- Read the scenario before looking for a memorised answer.
- Find the constraint that changes the correct option.
- Eliminate answers that are true in general but not in this case.
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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 |
What to study next
Got this wrong? Here's your next step.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Machine Learning Implementation and Operations — This question tests Machine Learning Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: 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.
What should I do if I get this MLS-C01 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
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Last reviewed: Jul 4, 2026
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