MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is exploring a dataset with mixed data types (numeric, categorical, text). The dataset has 5 million rows. The scientist wants to understand the relationships between variables and identify potential data quality issues. Which THREE tools are suitable for this 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
✓
AWS Glue DataBrew
Options A, C, and D are correct. AWS Glue DataBrew can profile data, visualize distributions, and detect anomalies. Amazon SageMaker Data Wrangler provides interactive data preparation and visualization. Amazon Athena can be used to run SQL queries for data quality checks. Option B (AWS Data Pipeline) is wrong because it is for workflow orchestration, not EDA. Option E (Amazon Kinesis Data Analytics) is wrong because it is for streaming data, not batch EDA.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
AWS Glue DataBrew
Why this is correct
Data profiling and visualization.
- ✗
AWS Data Pipeline
Why it's wrong here
For data movement and transformation scheduling.
- ✓
Amazon SageMaker Data Wrangler
Why this is correct
Interactive data preparation and analysis.
- ✓
Amazon Athena
Why this is correct
SQL queries for data exploration.
- ✗
Amazon Kinesis Data Analytics
Why it's wrong here
For real-time streaming analytics.
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