The answer is using pandas .isnull().sum() in a SageMaker notebook, as this method directly computes the count of null entries per column, which can then be divided by the dataframe’s length to calculate the missing values percentage per column. This approach is correct because pandas provides vectorized operations for exploratory data analysis, making it the most straightforward way to identify missing values percentage per column without external services. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this question tests your ability to distinguish between core data analysis tools and AWS-specific services; a common trap is choosing S3 Select or Athena, which are for querying data in place rather than for in-memory EDA, or QuickSight, which is a visualization layer. Remember the mnemonic “IS null SUM it up” to recall that .isnull().sum() is your go-to for missing value counts in a notebook environment.
MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Compare every option against the stated constraints before choosing — the best answer satisfies all requirements, not just the most obvious one. 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 data scientist is exploring a dataset and wants to check for missing values. Which method is most appropriate to identify the percentage of missing values per column?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use pandas .isnull().sum() in a SageMaker notebook
Using pandas .isnull().sum() in a SageMaker notebook is a standard approach to count missing values per column. Option A is wrong because S3 Select is for filtering S3 objects, not for data analysis. Option B is wrong because QuickSight is for visualization but not for programmatic missing value analysis. Option D is wrong because Athena requires SQL and is less direct for EDA. Option E is wrong because Glue Crawler discovers schema, not missing values.
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.
✗
Use Amazon S3 Select to query missing values
Why it's wrong here
S3 Select is for retrieving subsets of data, not for computing missing percentages.
✗
Use Amazon Athena to run a SELECT COUNT(*) query
Why it's wrong here
Athena is more suited for SQL-based analysis but requires more setup.
✗
Use Amazon QuickSight to create a missing value dashboard
Why it's wrong here
QuickSight can visualize missing data but is not the most direct method for initial EDA.
✗
Use AWS Glue Crawler to detect missing values
Why it's wrong here
Glue Crawler infers schema and partitions, not missing values.
✓
Use pandas .isnull().sum() in a SageMaker notebook
Why this is correct
This is a direct and efficient way to count missing values per column.
Related concept
Read the scenario before looking for a memorised answer.
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: Use pandas .isnull().sum() in a SageMaker notebook — Using pandas .isnull().sum() in a SageMaker notebook is a standard approach to count missing values per column. Option A is wrong because S3 Select is for filtering S3 objects, not for data analysis. Option B is wrong because QuickSight is for visualization but not for programmatic missing value analysis. Option D is wrong because Athena requires SQL and is less direct for EDA. Option E is wrong because Glue Crawler discovers schema, not missing values.
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.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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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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