Question 110 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing exploratory data analysis on a dataset containing customer transactions. The dataset has 1 million rows with 50 features, including numerical and categorical variables. The goal is to identify patterns and potential data quality issues before building a model. Which approach should the data scientist take to efficiently explore the data?
⚠ Common exam trap
The MLS-C01 exam often tests the distinction between tools for exploratory data analysis versus tools for data transformation or labeling, leading candidates to confuse SageMaker Data Wrangler (feature engineering) or Athena (SQL querying) with a dedicated profiling tool like DataBrew.
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 DataBrew to profile the dataset, view data quality reports, and visualize distributions.
AWS Glue DataBrew is purpose-built for visual data preparation and profiling without writing code. It can directly profile the 1-million-row dataset, automatically generate data quality reports (e.g., missing values, outliers, data types), and provide distribution visualizations for both numerical and categorical features, making it the most efficient choice for exploratory 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 AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.
Why this is correct
DataBrew provides an interactive interface for data profiling, cleaning, and visualization, making it suitable for EDA.
- ✗
Use Amazon Athena to run SQL queries and generate summary statistics.
Why it's wrong here
Athena can query data but lacks built-in profiling and visualization features.
- ✗
Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.
Why it's wrong here
Data Wrangler is more focused on feature engineering and requires building a flow, not initial EDA.
- ✗
Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.
Why it's wrong here
Ground Truth is for creating training datasets with human labelers, not for general EDA.
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 11, 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.