MLS-C01 Exploratory Data Analysis Practice Question
A data scientist is performing EDA on a dataset containing customer transaction records. The dataset includes columns: 'transaction_id', 'customer_id', 'transaction_amount', 'transaction_date', and 'product_category'. The data scientist wants to check for duplicate transactions and identify any suspicious patterns, such as multiple transactions from the same customer on the same day with the same amount. The dataset has 5 million rows. The data scientist is using a SageMaker Studio notebook with a ml.t3.medium instance. The data is stored in S3. What is the most efficient way to perform 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
✓
Use a SageMaker Spark processing job with PySpark to aggregate and detect duplicates.
SageMaker Spark processing jobs distribute the workload across multiple nodes, allowing efficient handling of the 5-million-row dataset within the memory limits of the ml.t3.medium instance. Option B (Athena) is less efficient due to query costs and the need for external setup, and it may not be as flexible for custom duplicate detection logic. Option C (pandas) would likely cause out-of-memory errors on the small instance. Option D (DataBrew) is designed for profiling and basic transformations, not for custom duplicate analysis.
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 a SageMaker Spark processing job with PySpark to aggregate and detect duplicates.
Why this is correct
Spark can handle large data efficiently.
- ✗
Use Amazon Athena to run SQL queries to find duplicates.
Why it's wrong here
Athena is query-only, not for interactive EDA with custom logic.
- ✗
Load the entire dataset into a pandas DataFrame and use groupby operations.
Why it's wrong here
Memory may be insufficient.
- ✗
Use AWS Glue DataBrew to create a profile and manually inspect.
Why it's wrong here
DataBrew may not support custom duplicate detection logic.
Quick reference
AWS S3 Storage Class Comparison
| Storage Class | Min Duration | Retrieval | Use Case |
|---|---|---|---|
| S3 Standard | None | Immediate | Frequently accessed data |
| S3 Standard-IA | 30 days | Immediate | Infrequent access, rapid retrieval |
| S3 One Zone-IA | 30 days | Immediate | Non-critical infrequent data |
| S3 Intelligent-Tiering | None | Immediate–hours | Unknown or changing access patterns |
| S3 Glacier Instant | 90 days | Milliseconds | Archive with instant retrieval |
| S3 Glacier Flexible | 90 days | Minutes–hours | Archive, flexible retrieval |
| S3 Glacier Deep Archive | 180 days | Hours | Long-term compliance archive |
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