Quick Answer
AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can transform CSV files from S3 into Parquet format using its built-in Spark engine, with a cost-effective pay-per-execution model that eliminates the need for provisioning servers. This service is ideal for the scenario because it automatically handles schema inference, data partitioning, and compression, making Parquet conversion efficient for large datasets while integrating natively with data warehouses like Amazon Redshift. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your understanding of serverless data transformation services within the ML data pipeline, often appearing alongside traps like Amazon EMR (which requires cluster management) or AWS Data Pipeline (which is not serverless). A key memory tip is to associate Glue with “glue” that binds S3 and Redshift together—think “Glue for the ETL, not the cluster.”
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. 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 engineer needs to transform large CSV files stored in S3 into Parquet format and load them into a data warehouse for analysis. The transformation must be cost-effective and serverless. Which AWS service should be used?
Amazon Athena
Why wrong: Athena is for querying data, not transforming and loading.
Amazon EMR with Spark
Why wrong: EMR requires provisioning clusters; not serverless.
AWS Glue
AWS Glue is a serverless ETL service that can perform the transformation efficiently.
AWS Data Pipeline
Why wrong: Data Pipeline is a managed orchestration service but not serverless; it relies on EC2 instances.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
AWS Glue
AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can automatically convert CSV files from S3 into Parquet format using its built-in Spark engine. It is cost-effective as you only pay for the resources consumed during the job execution, and it integrates directly with data warehouses like Amazon Redshift for loading transformed data.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
Amazon Athena
Why it's wrong here
Athena is for querying data, not transforming and loading.
Amazon EMR with Spark
Why it's wrong here
EMR requires provisioning clusters; not serverless.
AWS Glue
Why this is correct
AWS Glue is a serverless ETL service that can perform the transformation efficiently.
Related concept
Read the scenario before looking for a memorised answer.
AWS Data Pipeline
Why it's wrong here
Data Pipeline is a managed orchestration service but not serverless; it relies on EC2 instances.
Common exam traps
The trap here is that candidates confuse Amazon Athena's ability to query Parquet files with the ability to transform CSV into Parquet, overlooking that Athena is a query engine, not an ETL service, while Glue is purpose-built for serverless data transformation.
Detailed technical explanation
AWS Glue uses Apache Spark under the hood, automatically inferring schemas from CSV files via its Data Catalog crawler and then applying optimized Parquet columnar storage, which reduces query costs in services like Athena by up to 90% due to predicate pushdown and compression. A subtle behavior is that Glue jobs can handle schema evolution (e.g., new columns in CSV) by using the 'mergeSchema' option, but this may cause performance overhead if not configured properly. In real-world scenarios, Glue's serverless nature means it scales from zero to thousands of executors based on workload, but cold starts can add latency for small, frequent jobs.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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.
What to study next
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FAQ
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
The correct answer is: AWS Glue — AWS Glue is the correct choice because it provides a fully managed, serverless ETL service that can automatically convert CSV files from S3 into Parquet format using its built-in Spark engine. It is cost-effective as you only pay for the resources consumed during the job execution, and it integrates directly with data warehouses like Amazon Redshift for loading transformed data.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 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.
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