- A
Use AWS Glue ETL to read both datasets, join them using Spark DataFrames, and write the result to S3.
Correct. AWS Glue ETL runs serverless Spark jobs that can efficiently join large datasets stored in S3 without moving data out of S3. It is cost-effective because you pay only for the resources consumed during the job execution, and it handles large volumes efficiently.
- B
Launch an Amazon EMR cluster with Spark, read data from S3, perform the join, and write results back to S3.
Why wrong: Incorrect. While Amazon EMR with Spark can perform the join, it requires provisioning and managing a cluster, which incurs additional overhead and cost. Glue's serverless model is simpler and more cost-effective for this use case.
- C
Use Amazon Athena to run a SQL query joining the two datasets directly on S3.
Why wrong: Incorrect. Amazon Athena would scan the entire 15 TB of data, leading to high costs (approximately $75 per TB scanned) and potentially slow performance for large joins. It is not optimized for heavy ETL joins and lacks the processing power of Spark.
- D
Load both datasets into Amazon Redshift using COPY commands, then perform the join in Redshift.
Why wrong: Incorrect. Loading both datasets into Redshift first involves significant data movement and extra costs for storage and compute. This approach is not minimal in data movement or cost compared to processing in place with Glue.
MLS-C01 AWS Glue ETL Practice Question
This MLS-C01 practice question tests your understanding of data engineering. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. A key principle to apply: aWS Glue ETL. 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 machine learning team needs to create a training dataset by joining two large datasets (10 TB and 5 TB) stored in S3. The join key is 'user_id'. They want to minimize data movement and cost. Which approach should they use?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"minimum / minimize"Why it matters: Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
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 AWS Glue ETL to read both datasets, join them using Spark DataFrames, and write the result to S3.
AWS Glue ETL provides a serverless Spark environment that can read both datasets from S3, perform the join in a distributed manner, and write the result back to S3. This approach minimizes data movement because Glue reads directly from S3 and writes back to S3 without requiring intermediate storage or cluster management. It is cost-effective due to per-second billing and is optimized for large-scale ETL workloads like joining 10 TB and 5 TB datasets. Amazon Athena would scan 15 TB of data, incurring high costs and potential performance issues, while EMR and Redshift require provisioning and managing clusters, leading to higher operational overhead and costs.
Key principle: AWS Glue ETL
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 to read both datasets, join them using Spark DataFrames, and write the result to S3.
Why this is correct
Correct. AWS Glue ETL runs serverless Spark jobs that can efficiently join large datasets stored in S3 without moving data out of S3. It is cost-effective because you pay only for the resources consumed during the job execution, and it handles large volumes efficiently.
Clue confirmation
The clue word "minimum / minimize" in the question point toward this answer.
Related concept
AWS Glue ETL
- ✗
Launch an Amazon EMR cluster with Spark, read data from S3, perform the join, and write results back to S3.
Why it's wrong here
Incorrect. While Amazon EMR with Spark can perform the join, it requires provisioning and managing a cluster, which incurs additional overhead and cost. Glue's serverless model is simpler and more cost-effective for this use case.
- ✗
Use Amazon Athena to run a SQL query joining the two datasets directly on S3.
Why it's wrong here
Incorrect. Amazon Athena would scan the entire 15 TB of data, leading to high costs (approximately $75 per TB scanned) and potentially slow performance for large joins. It is not optimized for heavy ETL joins and lacks the processing power of Spark.
- ✗
Load both datasets into Amazon Redshift using COPY commands, then perform the join in Redshift.
Why it's wrong here
Incorrect. Loading both datasets into Redshift first involves significant data movement and extra costs for storage and compute. This approach is not minimal in data movement or cost compared to processing in place with Glue.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap is that candidates often assume serverless Athena is the cheapest option for any S3 query, but for large-scale joins, Athena's per-TB scanning cost and limitations on complex joins make AWS Glue ETL (serverless Spark) more cost-effective and efficient for such ETL tasks.
Detailed technical explanation
How to think about this question
Athena uses Presto under the hood to execute SQL queries directly on data in S3, leveraging partitioning, columnar formats (e.g., Parquet), and predicate pushdown to minimize data scanned. For large joins, Athena automatically distributes the query across multiple workers in a managed, serverless fashion, and you only pay for the data scanned (e.g., $5 per TB) — making it highly cost-efficient for ad-hoc joins. A subtle behavior is that Athena's JOIN performance can degrade if the datasets are not properly partitioned or if the join key has high skew, but for a simple user_id join, it typically performs well without any infrastructure setup.
KKey Concepts to Remember
- AWS Glue ETL
- Serverless Spark
- Data Movement Minimization
- Cost-Effective Joins for Large Datasets
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
AWS Glue ETL
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.
Review aWS Glue ETL, then practise related MLS-C01 questions on the same topic to reinforce the concept.
- →
Data Engineering — study guide chapter
Learn the concepts, then practise the questions
- →
Data Engineering practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
Data Engineering — This question tests Data Engineering — AWS Glue ETL.
What is the correct answer to this question?
The correct answer is: Use AWS Glue ETL to read both datasets, join them using Spark DataFrames, and write the result to S3. — AWS Glue ETL provides a serverless Spark environment that can read both datasets from S3, perform the join in a distributed manner, and write the result back to S3. This approach minimizes data movement because Glue reads directly from S3 and writes back to S3 without requiring intermediate storage or cluster management. It is cost-effective due to per-second billing and is optimized for large-scale ETL workloads like joining 10 TB and 5 TB datasets. Amazon Athena would scan 15 TB of data, incurring high costs and potential performance issues, while EMR and Redshift require provisioning and managing clusters, leading to higher operational overhead and costs.
What should I do if I get this MLS-C01 question wrong?
Review aWS Glue ETL, then practise related MLS-C01 questions on the same topic to reinforce the concept.
Are there clue words in this question I should notice?
Yes — watch for: "minimum / minimize". Asks for the least resource use — fewest addresses, smallest subnet, lowest overhead. Eliminate over-provisioned options even if they would technically work.
What is the key concept behind this question?
AWS Glue ETL
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 →
Keep practising
More MLS-C01 practice questions
- A company needs to transfer 10 TB of data from an on-premises data center to Amazon S3. The network bandwidth is limited…
- A company is using Amazon Kinesis Data Streams to ingest real-time clickstream data. The data is consumed by a Lambda fu…
- A team is building a data pipeline to process terabytes of log data daily using Amazon EMR. The data arrives in 5-minute…
- A data science team is building a real-time fraud detection system. Transactions are streamed via Amazon Kinesis Data St…
- A company uses Amazon SageMaker to train and deploy machine learning models. The training data is stored in Amazon S3 (P…
- A data engineering team is designing a data lake on AWS for machine learning workloads. The data includes structured, se…
Last reviewed: Jul 4, 2026
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.