MLS-C01 Exploratory Data Analysis Practice Question
This MLS-C01 practice question tests your understanding of exploratory data analysis. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. 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 performing exploratory data analysis on a dataset with mixed data types: numerical, categorical, and text. They want to use Amazon SageMaker Data Wrangler to create a quick visualization dashboard. Which set of transformations should they apply in Data Wrangler to handle all data types appropriately?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.
Option D is correct because Data Wrangler's built-in analysis includes summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These are appropriate for initial EDA. Option A is incorrect because PCA is for dimensionality reduction, not EDA. Option B is incorrect because TF-IDF is a feature engineering step, not EDA. Option C is incorrect because clustering is a modeling step, not EDA.
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 the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical.
Why this is correct
These are appropriate EDA visualizations for different data types.
Related concept
Read the scenario before looking for a memorised answer.
✗
Convert all features to numerical using one-hot encoding and then create a scatter matrix.
Why it's wrong here
One-hot encoding is a transformation; scatter matrix may not be suitable for high-cardinality categorical features.
✗
Apply TF-IDF vectorization to text and then run k-means clustering.
Why it's wrong here
TF-IDF and clustering are modeling steps.
✗
Use PCA to reduce dimensionality and then visualize the first two components.
Why it's wrong here
PCA is for dimensionality reduction, not initial EDA.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
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 the built-in analysis: summary statistics for numerical, word cloud for text, and frequency for categorical. — Option D is correct because Data Wrangler's built-in analysis includes summary statistics for numerical features, word clouds for text, and frequency counts for categorical features. These are appropriate for initial EDA. Option A is incorrect because PCA is for dimensionality reduction, not EDA. Option B is incorrect because TF-IDF is a feature engineering step, not EDA. Option C is incorrect because clustering is a modeling step, not EDA.
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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Question Discussion
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