Question 646 of 754
AI0-001 AI Implementation and Operations Practice Question
A DevOps team is deploying a machine learning model using a CI/CD pipeline. They want to ensure the model is reproducible and traceable. Which TWO practices should they implement?
⚠ Common exam trap
The AI0-001 exam often tests the misconception that manual steps or simple documentation (like spreadsheets) are sufficient for traceability, when in fact automated version control and containerization are required for true reproducibility in a CI/CD pipeline.
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
✓
Version the training dataset and code using Git and DVC.
Versioning the training dataset and code using Git and DVC (Data Version Control) ensures that every model training run can be exactly reproduced by tracking changes to both the source code and the data. DVC stores metadata pointers in Git while the actual data is stored in a remote cache, enabling precise rollback and audit trails. This practice directly supports reproducibility and traceability, which are core requirements for MLOps and CI/CD pipelines.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Version the training dataset and code using Git and DVC.
Why this is correct
Version control for data and code ensures exact reproduction of training.
- ✗
Manually deploy the model to production after approval.
Why it's wrong here
Manual steps break traceability and reproducibility.
- ✗
Store only the final model artifact in a shared drive.
Why it's wrong here
Without versioning data and code, the model cannot be reproduced.
- ✗
Use a spreadsheet to record model version numbers.
Why it's wrong here
Spreadsheets are not automated and prone to errors; version control tools are better.
- ✓
Package the model in a Docker container with a fixed base image.
Why this is correct
Containerization ensures consistent runtime environment.
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Last reviewed: Jul 4, 2026
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