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.
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?
Network Topology
A
Use S3 object tags instead of metadata and query the tags using Athena.
Why wrong: Athena does not query S3 object tags.
B
Use an AWS Lambda function to copy the metadata into the object's content as a new line.
Why wrong: Modifying object content would change the data and is not recommended.
C
Use AWS Glue to create a table that includes the metadata as a column by running an ETL job.
A Glue ETL job can read objects, extract metadata, and write to a table that Athena can query.
D
Use Amazon Athena to query the object metadata directly by referencing the metadata field.
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 C is correct because AWS Glue ETL jobs can read the S3 object's user-defined metadata (e.g., 'kafka-offset') and write it as a column in a new or transformed dataset, which Athena can then query. This approach does not modify the existing data pipeline, as the original objects remain unchanged; the metadata is extracted and stored in a queryable format (e.g., Parquet or CSV) in a separate location. Glue's ability to access S3 object metadata via the `getObjectMetadata` API during ETL processing makes this a clean, pipeline-agnostic solution.
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
The trap here is that candidates assume Athena can natively query S3 object metadata (like HTTP headers) because Athena can query data in S3, but Athena has no access to object-level metadata—it only reads the content of files, not the object's key-value metadata fields.
Detailed technical explanation
How to think about this question
Under the hood, S3 user-defined metadata is stored as HTTP headers (x-amz-meta-*) and is not part of the object's byte content; Athena's Presto/Trino engine reads only the data within the object, not its headers. AWS Glue ETL jobs can use the `boto3` `head_object` or `get_object` calls to retrieve metadata and then write it as a column, enabling schema-on-read queries. In real-world streaming pipelines, this pattern is used to track offsets without reprocessing raw data, as the metadata can be stored in a separate 'metadata catalog' table in the Glue Data Catalog.
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 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.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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 C is correct because AWS Glue ETL jobs can read the S3 object's user-defined metadata (e.g., 'kafka-offset') and write it as a column in a new or transformed dataset, which Athena can then query. This approach does not modify the existing data pipeline, as the original objects remain unchanged; the metadata is extracted and stored in a queryable format (e.g., Parquet or CSV) in a separate location. Glue's ability to access S3 object metadata via the `getObjectMetadata` API during ETL processing makes this a clean, pipeline-agnostic solution.
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.
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Question Discussion
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