- A
Use Amazon SageMaker Data Wrangler to create a data flow that imputes missing values and export the transformed dataset to S3.
Why wrong: SageMaker Data Wrangler is designed for interactive, visual data preparation and is not suitable for automated, large-scale batch processing.
- B
Use AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset.
AWS Glue provides native transforms like DropNullFields and FillWithValue, and custom scripts allow handling missing values efficiently at scale.
- C
Use Amazon Redshift Spectrum with an external table to query the data and use SQL COALESCE to handle missing values on the fly.
Why wrong: Redshift Spectrum is for querying external data, not for cleaning or imputing missing values; it would not persist the transformations.
- D
Use Amazon Athena to run SQL queries that impute missing values and write the results to a new table.
Why wrong: Athena is a query engine and cannot modify data; it can only read and write results of queries, but not update data in place.
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 data scientist is analyzing a dataset with missing values in several features. The dataset is large (10 million rows) and stored in an S3 bucket as CSV files. The scientist wants to use AWS Glue to catalog the data and then use Amazon Athena to query it. However, the missing values are causing errors in downstream machine learning models. Which approach should the scientist take to handle missing values during exploratory data analysis?
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 AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset.
Option C is correct because AWS Glue provides built-in transforms to handle missing values during the ETL process, and using a custom script with the AWS Glue library allows fine-grained control. Option A is wrong because Athena cannot modify data; it is only a query engine. Option B is wrong because SageMaker Data Wrangler is for interactive data preparation, not for large-scale automated ETL. Option D is wrong because Redshift Spectrum is for querying, not for cleaning 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 SageMaker Data Wrangler to create a data flow that imputes missing values and export the transformed dataset to S3.
Why it's wrong here
SageMaker Data Wrangler is designed for interactive, visual data preparation and is not suitable for automated, large-scale batch processing.
- ✓
Use AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset.
Why this is correct
AWS Glue provides native transforms like DropNullFields and FillWithValue, and custom scripts allow handling missing values efficiently at scale.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Amazon Redshift Spectrum with an external table to query the data and use SQL COALESCE to handle missing values on the fly.
Why it's wrong here
Redshift Spectrum is for querying external data, not for cleaning or imputing missing values; it would not persist the transformations.
- ✗
Use Amazon Athena to run SQL queries that impute missing values and write the results to a new table.
Why it's wrong here
Athena is a query engine and cannot modify data; it can only read and write results of queries, but not update data in place.
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 — study guide chapter
Learn the concepts, then practise the questions
- →
Exploratory Data Analysis practice questions
Targeted practice on this topic area only
- →
All MLS-C01 questions
1,755 questions across all exam domains
- →
AWS Certified Machine Learning Specialty MLS-C01 study guide
Full concept coverage aligned to exam objectives
- →
MLS-C01 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related MLS-C01 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Data Engineering practice questions
Practise MLS-C01 questions linked to Data Engineering.
Machine Learning Implementation and Operations practice questions
Practise MLS-C01 questions linked to Machine Learning Implementation and Operations.
Modeling practice questions
Practise MLS-C01 questions linked to Modeling.
Exploratory Data Analysis practice questions
Practise MLS-C01 questions linked to Exploratory Data Analysis.
MLS-C01 fundamentals practice questions
Practise MLS-C01 questions linked to MLS-C01 fundamentals.
MLS-C01 scenario practice questions
Practise MLS-C01 questions linked to MLS-C01 scenario.
MLS-C01 troubleshooting practice questions
Practise MLS-C01 questions linked to MLS-C01 troubleshooting.
Practice this exam
Start a free MLS-C01 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this MLS-C01 question test?
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 AWS Glue ETL jobs with a custom transformation script that uses the AWS Glue library to drop or impute missing values before writing to a new dataset. — Option C is correct because AWS Glue provides built-in transforms to handle missing values during the ETL process, and using a custom script with the AWS Glue library allows fine-grained control. Option A is wrong because Athena cannot modify data; it is only a query engine. Option B is wrong because SageMaker Data Wrangler is for interactive data preparation, not for large-scale automated ETL. Option D is wrong because Redshift Spectrum is for querying, not for cleaning 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.
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 →
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.