- A
The CSV files have different schemas (e.g., different columns) across partitions.
Schema evolution causes missing columns to appear as NULL when queried.
- B
Athena is configured to skip corrupted records, causing NULLs.
Why wrong: Athena does not silently skip records; it would throw an error.
- C
The Glue crawler incorrectly inferred the data type of the columns.
Why wrong: Incorrect data type would cause conversion errors, not NULLs.
- D
The CSV files use a custom delimiter that the Glue crawler does not recognize.
Why wrong: The crawler can be configured to handle custom delimiters, but the issue is NULLs, not parsing.
Question 820 of 1,755
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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.
A company is storing customer transaction data in Amazon S3 as CSV files. A data scientist uses AWS Glue to crawl the data and create a table in the AWS Glue Data Catalog. When querying the table with Amazon Athena, the data scientist notices that some columns have NULL values where data should exist. The data scientist examines the raw CSV files and confirms the data is present. What is the most likely cause of the NULL values?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
The CSV files have different schemas (e.g., different columns) across partitions.
The most likely cause is that the CSV files have different schemas across partitions. When AWS Glue crawler infers the schema, it samples a subset of files. If partitions have different columns or column order, the inferred schema may not include columns present only in later partitions. When Athena queries the table, it uses the schema from the Data Catalog; columns missing from the schema appear as NULL. Option A is correct. Option B is incorrect because Athena does not skip corrupted records by default; it would fail on parse errors. Option C is incorrect because data type inference errors would cause different issues, such as type mismatches, not NULLs for existing data. Option D is incorrect because the Glue crawler can handle custom delimiters if configured; the issue here is schema mismatch, not delimiter recognition.
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.
- ✓
The CSV files have different schemas (e.g., different columns) across partitions.
Why this is correct
Schema evolution causes missing columns to appear as NULL when queried.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Athena is configured to skip corrupted records, causing NULLs.
Why it's wrong here
Athena does not silently skip records; it would throw an error.
- ✗
The Glue crawler incorrectly inferred the data type of the columns.
Why it's wrong here
Incorrect data type would cause conversion errors, not NULLs.
- ✗
The CSV files use a custom delimiter that the Glue crawler does not recognize.
Why it's wrong here
The crawler can be configured to handle custom delimiters, but the issue is NULLs, not parsing.
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.
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 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.
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FAQ
Questions learners often ask
What does this MLS-C01 question test?
Exploratory Data Analysis — This question tests Exploratory Data Analysis — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: The CSV files have different schemas (e.g., different columns) across partitions. — The most likely cause is that the CSV files have different schemas across partitions. When AWS Glue crawler infers the schema, it samples a subset of files. If partitions have different columns or column order, the inferred schema may not include columns present only in later partitions. When Athena queries the table, it uses the schema from the Data Catalog; columns missing from the schema appear as NULL. Option A is correct. Option B is incorrect because Athena does not skip corrupted records by default; it would fail on parse errors. Option C is incorrect because data type inference errors would cause different issues, such as type mismatches, not NULLs for existing data. Option D is incorrect because the Glue crawler can handle custom delimiters if configured; the issue here is schema mismatch, not delimiter recognition.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 20, 2026
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