MLS-C01 Cheat Sheet

#	Domain	Weight	Questions	Practice
2.0	Exploratory Data Analysis	24%	523	Practice →
Total	24%	2,228

Domain

Weight

Questions

Practice

2.0

Exploratory Data Analysis

24%

523

Practice →

Total

24%

2,228

Domain Quick Reference

2.0Exploratory Data Analysis

Query S3 with Athena, catalog via Glue Crawler, transform in SageMaker Data Wrangler, and label with Ground Truth. Get the tool-to-task mapping right: Athena for SQL, Glue for ETL, Data Wrangler for feature prep.

24%

Key concepts

✓Query S3 data with Amazon Athena and catalog schemas using AWS Glue Crawlers
✓Build visual transformations and detect anomalies with SageMaker Data Wrangler
✓Compute summary statistics and correlations using pandas, NumPy, and SageMaker notebooks
✓Label datasets with SageMaker Ground Truth and evaluate class imbalance or outliers

Watch out for

⚠Confusing AWS Glue Data Catalog metadata storage with actual ETL execution, and assuming Crawlers transform data rather than infer schemas
⚠Treating SageMaker Ground Truth as a data-cleaning tool instead of a labeling service with human or automated annotators
⚠Ignoring class imbalance and outlier handling during EDA, then selecting accuracy as the metric for skewed datasets

Exam Day Reminders

→Read every question stem fully — look for qualifiers like 'MOST likely,' 'BEST,' or 'EXCEPT.'

→Flag uncertain questions and come back — don't waste time on one question.

→Eliminate obviously wrong options first, then choose between remaining ones.

→Trust your first instinct unless you have a specific reason to change.

→For MLS-C01, scenarios typically have one clearly best answer — look for the option that matches the specific constraints in the question.

#	Domain	Weight	Questions	Practice
2.0	Exploratory Data Analysis	24%	523	Practice →
Total	24%	2,228

Domain

Weight

Questions

Practice

2.0

Exploratory Data Analysis

24%

523

Practice →

Total

24%

2,228

Domain Quick Reference

2.0Exploratory Data Analysis

24%

Key concepts

✓Query S3 data with Amazon Athena and catalog schemas using AWS Glue Crawlers
✓Build visual transformations and detect anomalies with SageMaker Data Wrangler
✓Compute summary statistics and correlations using pandas, NumPy, and SageMaker notebooks
✓Label datasets with SageMaker Ground Truth and evaluate class imbalance or outliers

Watch out for

⚠Confusing AWS Glue Data Catalog metadata storage with actual ETL execution, and assuming Crawlers transform data rather than infer schemas
⚠Treating SageMaker Ground Truth as a data-cleaning tool instead of a labeling service with human or automated annotators
⚠Ignoring class imbalance and outlier handling during EDA, then selecting accuracy as the metric for skewed datasets

Exam Day Reminders

→Read every question stem fully — look for qualifiers like 'MOST likely,' 'BEST,' or 'EXCEPT.'

→Flag uncertain questions and come back — don't waste time on one question.

→Eliminate obviously wrong options first, then choose between remaining ones.

→Trust your first instinct unless you have a specific reason to change.

→For MLS-C01, scenarios typically have one clearly best answer — look for the option that matches the specific constraints in the question.

MLS-C01 Exam Blueprint — At a Glance

Domain Quick Reference

2.0Exploratory Data Analysis

Exam Day Reminders

More MLS-C01 resources

MLS-C01 Cheat Sheet

MLS-C01 Exam Blueprint — At a Glance

Domain Quick Reference

2.0Exploratory Data Analysis

Exam Day Reminders

More MLS-C01 resources