The answer is AWS Glue ETL jobs reading from and writing to S3. This is correct because AWS Glue provides a fully serverless Spark environment that handles serverless ETL preprocessing with AWS Glue, allowing you to run complex data cleaning, feature engineering, and normalization directly on data stored in Amazon S3 without provisioning or managing any underlying infrastructure. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between serverless preprocessing options: Glue is purpose-built for heavy ETL transformations on large datasets, while SageMaker Processing jobs are better suited for post-training tasks like batch inference, and EMR requires cluster management. A common trap is choosing SageMaker Processing because it is also serverless, but remember that Glue is the native service for ETL preprocessing on S3. Memory tip: “Glue for the gooey data prep, Processing for the polished model steps.”
MLS-C01 Data Engineering 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. 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 machine learning team needs to preprocess large volumes of clickstream data stored in Amazon S3 before training a model. The preprocessing includes data cleaning, feature engineering, and normalization. The team wants to use a serverless solution that minimizes operational overhead. Which combination of services should the team use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
AWS Glue ETL jobs reading from and writing to S3.
AWS Glue provides a serverless Spark environment for running ETL jobs on data in S3. Amazon SageMaker Processing jobs are also serverless but are more suited for post-training tasks. Option B is wrong because EMR requires cluster management. Option C is wrong because SageMaker Notebooks are interactive, not automated. Option D is wrong because Athena is for ad-hoc queries, not complex transformations.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Amazon SageMaker Notebooks with custom Python scripts.
Why it's wrong here
Notebooks are interactive, not automated for scheduled preprocessing.
AWS Glue is serverless and designed for ETL on data lakes.
Related concept
Static NAT maps one inside address to one outside address.
✗
Amazon Athena with SQL queries.
Why it's wrong here
Athena is for querying, not for complex transformations like normalization.
Common exam traps
Common exam trap: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
Data Engineering — This question tests Data Engineering — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: AWS Glue ETL jobs reading from and writing to S3. — AWS Glue provides a serverless Spark environment for running ETL jobs on data in S3. Amazon SageMaker Processing jobs are also serverless but are more suited for post-training tasks. Option B is wrong because EMR requires cluster management. Option C is wrong because SageMaker Notebooks are interactive, not automated. Option D is wrong because Athena is for ad-hoc queries, not complex transformations.
What should I do if I get this MLS-C01 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related MLS-C01 NAT questions on configuration and troubleshooting.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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