Question 393 of 1,672
MLS-C01 Exploratory Data Analysis Practice Question
A data engineer is running an Amazon SageMaker Data Wrangler flow on a dataset with 5 million rows. The flow includes several transformations. The engineer wants to validate the data quality by checking for missing values and outliers before training. Which approach is most efficient?
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 Data Wrangler's data quality and insights report to generate a report with statistics and visualizations.
Using Data Wrangler's built-in data quality and insights report is the most efficient way to get statistics and detect issues without custom code. Option B (Athena) requires writing SQL queries. Option C (QuickSight) needs exporting. Option D (EMR) is overkill.
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 Data Wrangler's data quality and insights report to generate a report with statistics and visualizations.
Why this is correct
Data Wrangler has a built-in report for data quality.
- ✗
Export the transformed data to S3 and query with Amazon Athena.
Why it's wrong here
Athena can check but requires manual SQL queries.
- ✗
Use Amazon EMR with Spark to compute statistics.
Why it's wrong here
Using Amazon EMR with Spark for statistics is redundant because SageMaker Data Wrangler natively provides integrated data quality reports and visualisations to detect missing values and outliers directly within its flow. This eliminates the need for an external compute environment. EMR with Spark is powerful for highly custom, large-scale data transformations and complex analytical workloads that extend beyond Data Wrangler's integrated capabilities, or for processing petabyte-scale datasets before ingestion into other services.
- ✗
Import the data into Amazon QuickSight and create dashboards.
Why it's wrong here
QuickSight is for visualization, not automated data quality checks.
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Last reviewed: Jun 20, 2026
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