MLA-C01 Data Preparation for Machine Learning Practice Question
A data engineer is preparing a large dataset of 10 TB for ML training on Amazon SageMaker. The data is stored in Amazon S3 as CSV files. To reduce training time and cost, the engineer wants to use a columnar format that is optimized for analytical queries. Which format should the engineer convert the data to?
⚠ Common exam trap
AWS often tests the distinction between columnar formats (Parquet vs. ORC) by making both appear correct, but the trap here is that ORC is tightly coupled with Hive and less commonly used with SageMaker, while Parquet is the de facto standard for AWS-native ML and analytics services.
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
✓
Parquet
Parquet is a columnar storage format that is highly optimized for analytical queries and is natively supported by Amazon SageMaker for efficient data loading. By converting the 10 TB of CSV data to Parquet, the data engineer can reduce I/O and storage costs because columnar formats allow SageMaker to read only the columns needed for training, rather than scanning entire rows. This directly addresses the goal of reducing training time and cost for ML workloads.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
XML
Why it's wrong here
XML is verbose and not columnar.
- ✓
Parquet
Why this is correct
Parquet is a columnar format that speeds up data access and reduces storage costs.
- ✗
ORC
Why it's wrong here
ORC is columnar but less commonly used with SageMaker compared to Parquet.
- ✗
JSON Lines
Why it's wrong here
JSON Lines is a row-oriented format, not optimal for columnar access.
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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