The correct answer is to use AWS Glue to create a table that includes the metadata as a column by running an ETL job. This is because S3 object metadata, such as a custom kafka-offset key, is stored as key-value pairs at the object level and is not directly queryable by Amazon Athena, which only reads object content. A Glue ETL job can read the S3 objects, extract the custom metadata from the object’s headers, and write the data—including the offset value as a new column—into a structured format like Parquet in a separate S3 location, which Athena can then query. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how to bridge S3 metadata with Athena for tracking pipeline progress, often appearing as a trap where candidates mistakenly think Glue crawlers or Athena’s `$metadata` column can capture custom tags. A common memory tip: “Crawlers see schema, ETL sees headers”—only an ETL job can programmatically extract and persist custom object metadata for querying.
MLS-C01 Data Engineering Practice Question
This MLS-C01 practice question tests your understanding of data engineering. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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.
Network Topology
A data engineer runs the AWS CLI command above to inspect an object in S3. The engineer wants to query this metadata (kafka-offset) using Amazon Athena to track processing progress. How can the engineer make this metadata available for Athena queries without modifying the existing data pipeline?
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 to create a table that includes the metadata as a column by running an ETL job.
Option B is correct. S3 object metadata is not automatically available in Athena. The engineer can use AWS Glue to crawl the S3 bucket and extract metadata into the Data Catalog; however, custom metadata is not crawled by default. A better approach is to store the metadata in a separate table or use S3 object tagging. But among options, Option B is correct: configure a Glue crawler to extract metadata? Actually, Glue crawlers do not extract custom metadata. Option D is correct: use S3 object tags, which can be queried via Athena using the $metadata column? Not exactly. Let's rethink. The best practice is to store metadata in a separate manifest file. Option B is correct because you can create a Glue table with a custom classifier to extract metadata? Actually, the correct answer is to use S3 Object Lambda to add metadata to the object content? Not listed. Given the options, Option B is correct: Use AWS Glue to create a table that includes the metadata? But Glue crawlers don't capture custom metadata. Option A is wrong because you cannot query metadata directly. Option C is wrong because Lambda cannot add metadata to existing objects without rewriting. Option D is correct: Use S3 object tags, which can be queried via Athena? Actually, Athena does not query tags. The best answer is to store metadata in a separate manifest file in S3 and query that. But the most practical is to use a Glue ETL job to read the objects and extract metadata into a table. Option B is the closest: 'Use AWS Glue to create a table that includes the metadata as a column' - you can use a Glue ETL job to extract metadata and store in Parquet. So Option B is correct.
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 S3 object tags instead of metadata and query the tags using Athena.
Why it's wrong here
Athena does not query S3 object tags.
✗
Use an AWS Lambda function to copy the metadata into the object's content as a new line.
Why it's wrong here
Modifying object content would change the data and is not recommended.
✓
Use AWS Glue to create a table that includes the metadata as a column by running an ETL job.
Why this is correct
A Glue ETL job can read objects, extract metadata, and write to a table that Athena can query.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use Amazon Athena to query the object metadata directly by referencing the metadata field.
Why it's wrong here
Athena cannot query S3 object metadata directly.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
Use explanations to understand the rule behind the answer.
TExam Day Tips
→Underline the problem statement mentally.
→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 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.
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
Data Engineering — This question tests Data Engineering — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use AWS Glue to create a table that includes the metadata as a column by running an ETL job. — Option B is correct. S3 object metadata is not automatically available in Athena. The engineer can use AWS Glue to crawl the S3 bucket and extract metadata into the Data Catalog; however, custom metadata is not crawled by default. A better approach is to store the metadata in a separate table or use S3 object tagging. But among options, Option B is correct: configure a Glue crawler to extract metadata? Actually, Glue crawlers do not extract custom metadata. Option D is correct: use S3 object tags, which can be queried via Athena using the $metadata column? Not exactly. Let's rethink. The best practice is to store metadata in a separate manifest file. Option B is correct because you can create a Glue table with a custom classifier to extract metadata? Actually, the correct answer is to use S3 Object Lambda to add metadata to the object content? Not listed. Given the options, Option B is correct: Use AWS Glue to create a table that includes the metadata? But Glue crawlers don't capture custom metadata. Option A is wrong because you cannot query metadata directly. Option C is wrong because Lambda cannot add metadata to existing objects without rewriting. Option D is correct: Use S3 object tags, which can be queried via Athena? Actually, Athena does not query tags. The best answer is to store metadata in a separate manifest file in S3 and query that. But the most practical is to use a Glue ETL job to read the objects and extract metadata into a table. Option B is the closest: 'Use AWS Glue to create a table that includes the metadata as a column' - you can use a Glue ETL job to extract metadata and store in Parquet. So Option B is correct.
What should I do if I get this MLS-C01 question wrong?
Identify which MLS-C01 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Question Discussion
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