- A
Use Amazon SageMaker Notebook with pandas to load the dataset and compute missing percentages.
Why wrong: SageMaker Notebook requires manual coding and is less efficient for quick checks.
- B
Use Amazon QuickSight to connect to S3 and calculate missing value percentages via calculated fields.
Why wrong: QuickSight is a visualization tool, not for direct SQL-based analysis.
- C
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.
- D
Use AWS Glue Crawler to infer schema and view missing values statistics in the AWS Glue Data Catalog.
Why wrong: AWS Glue Crawler only catalogs metadata, not performing data analysis.
Detecting Missing Values Percentage with Amazon Athena
This MLS-C01 practice question tests your understanding of exploratory data analysis. 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. A key principle to apply: amazon Athena. 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 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.
Option C is correct because 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.
Key principle: Amazon Athena
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.
Related concept
Amazon Athena
- ✗
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.
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
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Amazon Athena
- Missing value analysis
- Amazon S3
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
Amazon Athena
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.
Review amazon Athena, then practise related MLS-C01 questions on the same topic to reinforce the concept.
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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 — Amazon Athena.
What is the correct answer to this question?
The correct answer is: Use Amazon Athena to query the data with SQL using COUNT(*) and CASE statements to compute missing percentage per column. — Option C is correct because 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.
What should I do if I get this MLS-C01 question wrong?
Review amazon Athena, then practise related MLS-C01 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Amazon Athena
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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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