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Question 110 of 1,755

Exploratory Data Analysis →mediumMultiple ChoiceObjective-mapped

Quick Answer

The correct approach is to use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions. This is the most efficient choice because DataBrew is a purpose-built, serverless service for visual data preparation and profiling without writing any code, capable of directly handling a 1-million-row dataset with 50 features. It automatically generates comprehensive data quality reports that highlight missing values, outliers, and data type inconsistencies, while also providing distribution visualizations for both numerical and categorical variables—all essential for exploratory data analysis. On the AWS Certified Machine Learning Specialty MLS-C01 exam, this scenario tests your ability to match the right tool to the task, often appearing as a trap where candidates might over-engineer a solution with Athena or SageMaker notebooks when a simpler, code-free profiling tool is available. Remember the memory tip: for quick, visual profiling of large datasets, think “DataBrew does the review.”

MLS-C01 Exploratory Data Analysis Practice Question

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. 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 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?

Question 1mediummultiple choice

Read the full NAT/PAT explanation →

A
Use AWS Glue DataBrew to profile the dataset, view data quality reports, and visualize distributions.
DataBrew provides an interactive interface for data profiling, cleaning, and visualization, making it suitable for EDA.
B
Use Amazon Athena to run SQL queries and generate summary statistics.
Why wrong: Athena can query data but lacks built-in profiling and visualization features.
C
Use Amazon SageMaker Data Wrangler to import the data and create a flow for feature engineering.
Why wrong: Data Wrangler is more focused on feature engineering and requires building a flow, not initial EDA.
D
Use Amazon SageMaker Ground Truth to label the data and then analyze the labels.
Why wrong: Ground Truth is for creating training datasets with human labelers, not for general EDA.

Full breakdown with real-world context →

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.

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 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.
Related concept
Read the scenario before looking for a memorised answer.
✗
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.

Common exam traps

Common exam trap: answer the scenario, not the keyword

Cisco 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.

Detailed technical explanation

How to think about this question

Under the hood, AWS Glue DataBrew uses a serverless Spark engine to compute profile statistics such as distinct counts, null ratios, min/max, standard deviation, and histograms for each column, all while handling up to 50 features and 1 million rows efficiently. It also automatically detects data types and suggests transformations, which can reveal subtle data quality issues like mixed data types in a column or unexpected cardinality in categorical features. In a real-world scenario, a data scientist might use DataBrew to quickly identify that a 'transaction_amount' column contains negative values or that a 'customer_id' column has 99% missing values, guiding downstream cleaning decisions.

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.

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

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 exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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 — Read the scenario before looking for a memorised answer..

What is the correct answer to this question?

The correct answer is: 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.

What should I do if I get this MLS-C01 question wrong?

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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.