Question 1,327 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
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?
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
✓
Check the table schema in AWS Glue Data Catalog to ensure the column data type is correct.
Checking the table schema in the AWS Glue Data Catalog helps identify data type mismatches. If Athena returns zero rows for a numeric column, it may be because the column's data type in the catalog is incorrect (e.g., string instead of int). Fixing the schema to match the actual data allows Athena to parse the values correctly. Option A is incorrect because encryption does not affect query results. Option B is incorrect because running a crawler will only re-infer the schema, which may not solve the issue if the underlying data format is inconsistent. Option C is incorrect because partitioning is unrelated to data type issues.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Verify that the S3 bucket has encryption enabled.
Why it's wrong here
Encryption does not affect query results.
- ✗
Run an AWS Glue crawler to update the table schema.
Why it's wrong here
Crawling may not fix existing data type issues if the source data is problematic.
- ✗
Add a partition to the table for the date column.
Why it's wrong here
Partitioning does not address data type mismatches.
- ✓
Check the table schema in AWS Glue Data Catalog to ensure the column data type is correct.
Why this is correct
Incorrect data type can cause Athena to return null values.
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 |
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Last reviewed: Jun 20, 2026
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.
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