The answer is a schema mismatch across CSV partitions, where the Glue crawler infers the table schema from only the first few files and ignores columns present in later files. This occurs because the crawler samples data to define the schema in the Glue Data Catalog, so when different partitions contain varying columns—such as an extra field in newer files—Athena cannot map those unmapped columns and returns NULL values for the missing schema entries. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your understanding of how Glue crawlers handle semi-structured data and the common pitfall of assuming uniform schemas across partitioned datasets. A frequent trap is blaming data type inference or SerDe issues, but the root cause is almost always inconsistent column structures between partitions. Remember: if your CSV data has NULLs where data exists, think “schema drift across partitions”—the crawler’s first-file snapshot is the culprit.
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 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.
Option D is correct because the Glue crawler infers schema from the first few files; if later files have different schemas (e.g., more columns), the extra data is not captured. Option A is wrong because the crawler handles CSV without SerDe issues. Option B is wrong because Athena does not modify data. Option C is wrong because the issue is schema mismatch, not data type inference.
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.
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.
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. — Option D is correct because the Glue crawler infers schema from the first few files; if later files have different schemas (e.g., more columns), the extra data is not captured. Option A is wrong because the crawler handles CSV without SerDe issues. Option B is wrong because Athena does not modify data. Option C is wrong because the issue is schema mismatch, not data type inference.
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.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A data engineer runs a SQL query on Amazon Athena to explore a dataset stored in S3 as CSV. The query returns zero rows for a column that should have numeric values. Which step should the engineer take to diagnose the issue?
medium
A.Verify that the S3 bucket has encryption enabled.
B.Run an AWS Glue crawler to update the table schema.
C.Add a partition to the table for the date column.
✓ D.Check the table schema in AWS Glue Data Catalog to ensure the column data type is correct.
Why D: Option B is correct because checking the schema and data type conversion can reveal issues like unquoted commas or wrong format. Option A is wrong because the issue is likely with data types, not encryption. Option C is wrong because adding a partition won't fix data type issues. Option D is wrong because crawling does not change data types if schema is inferred incorrectly.
Variation 2. A data scientist is using Amazon Athena to query a CSV file stored in S3. The above error occurs. What is the most likely cause?
hard
A.The CSV file uses a different delimiter than comma.
B.The CSV file is missing a header row.
C.The CSV file is too large for Athena to process.
✓ D.The CSV file has inconsistent number of columns in some rows.
Why D: Option A is correct because the error clearly states that a row has more fields than the header. Option B is wrong because the error is about field count mismatch, not encoding. Option C is wrong because the error mentions row number, but the issue is field count. Option D is wrong because the header is present and read correctly.
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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.
Question Discussion
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