AIF-C01 Fundamentals of AI and ML Practice Question
An ML engineer wants to store training data in a format optimized for linear data scanning and columnar access in SageMaker. Which format is most appropriate?
⚠ Common exam trap
AWS often tests the misconception that CSV is the most efficient format for training data, but Parquet's columnar storage and compression provide superior performance for linear scanning and columnar access in distributed ML pipelines.
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 optimized for both linear data scanning and columnar access, making it ideal for training data in SageMaker. It reduces I/O by storing data by columns rather than rows, enabling efficient retrieval of specific features during model training.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
JSON
Why it's wrong here
JSON is also row-oriented and has overhead.
- ✗
Image (JPEG/PNG)
Why it's wrong here
Image formats are not suitable for tabular data.
- ✓
Parquet
Why this is correct
Parquet is columnar and optimized for analytical queries.
- ✗
CSV
Why it's wrong here
CSV is row-oriented and less efficient for columnar access.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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