AI0-001 AI Models and Data Engineering Practice Question
A data engineer needs to store training data in a format that supports columnar pruning during model training. Which storage format should they use?
⚠ Common exam trap
CompTIA often tests the misconception that JSON or CSV are acceptable for columnar pruning because they are common and human-readable, but the trap here is that only columnar formats like Parquet or ORC support efficient column-level access, while row-oriented formats require full record scans.
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 the correct choice because it is a columnar storage format that enables column pruning, allowing the training process to read only the columns needed for model training rather than entire rows. This reduces I/O and speeds up data loading, which is critical for large-scale AI/ML workloads. Unlike row-oriented formats, Parquet stores data by columns, making it efficient for analytical queries and feature selection.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Parquet
Why this is correct
Parquet is columnar, enabling compression and pruning, reducing I/O.
- ✗
XML
Why it's wrong here
XML is row-oriented and heavily nested, inefficient for columnar operations.
- ✗
JSON
Why it's wrong here
JSON is row-oriented and verbose, not optimized for columnar access.
- ✗
CSV
Why it's wrong here
CSV is row-oriented; reading all columns is required even if only a few are needed.
About these practice questions
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI0-001 practice question is part of Courseiva's free CompTIA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI0-001 exam.