MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is working with a dataset containing geospatial coordinates (latitude and longitude) of customer locations. The scientist wants to engineer features such as distance to the nearest store, and cluster customers into regions. Which AWS service is best suited for performing geospatial analysis and clustering during exploratory data 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
✓
Amazon SageMaker with custom Python scripts using scikit-learn and Geopy
Amazon SageMaker notebooks allow custom Python scripts using libraries like scikit-learn for clustering (e.g., K-Means) and Geopy for distance calculations, making it ideal for geospatial feature engineering and clustering during EDA. Option B is incorrect: Amazon Athena with PostGIS is for querying geospatial data, not for iterative analysis or clustering. Option C is incorrect: AWS Glue is an ETL service, not suited for interactive exploration and clustering. Option D is incorrect: Amazon Location Service provides maps and location tracking APIs, not a platform for analytical clustering.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Amazon SageMaker with custom Python scripts using scikit-learn and Geopy
Why this is correct
SageMaker allows custom code for distance calculations and clustering using libraries like scikit-learn.
- ✗
Amazon Athena with PostGIS extensions
Why it's wrong here
Athena can query geospatial data but does not provide clustering algorithms.
- ✗
AWS Glue with geospatial transforms
Why it's wrong here
Glue is for ETL, not for analytical clustering.
- ✗
Amazon Location Service
Why it's wrong here
Location Service provides APIs for maps and tracking, not for clustering analysis.
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