Question 567 of 1,672
MLS-C01 Amazon Athena Practice Question
A data scientist is analyzing a dataset with missing values in several columns. The dataset is stored in an S3 bucket. What is the most efficient method to identify the percentage of missing values per column using AWS services?
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
✓
Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column.
Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data. Option A is wrong because Amazon SageMaker Notebook requires manual coding and is less efficient for quick checks. Option B is wrong because Amazon QuickSight is a visualization tool, not for direct SQL-based analysis. Option D is wrong because AWS Glue Crawler only catalogs metadata, not performing data analysis.
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 Amazon SageMaker Notebook with pandas to load the dataset and compute missing percentages.
Why it's wrong here
SageMaker Notebook requires manual coding and is less efficient for quick checks.
- ✗
Use Amazon QuickSight to connect to S3 and calculate missing value percentages via calculated fields.
Why it's wrong here
QuickSight is a visualization tool, not for direct SQL-based analysis.
- ✓
Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column.
Why this is correct
Amazon Athena allows running SQL queries directly on data in S3, and the COUNT and CASE statements can compute missing value percentages efficiently without moving data.
- ✗
Use AWS Glue Crawler to infer schema and view missing values statistics in the AWS Glue Data Catalog.
Why it's wrong here
AWS Glue Crawler only catalogs metadata, not performing data analysis.
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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