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.
Option A is correct because versioning both the training dataset and the code with Git and DVC (Data Version Control) captures the exact data and source revisions used, which is essential for reproducing and tracing a model. Option E is correct because packaging the model in a Docker container with a fixed (pinned) base image locks down the OS libraries, dependencies, and runtime environment, ensuring the model behaves identically across environments and builds. Option B is not appropriate because manual deployment after approval is error-prone and not automated or traceable, undermining CI/CD reproducibility. Option C is wrong because storing only the final model artifact in a shared drive loses the training data, code, and environment context needed for reproducibility. Option D is wrong because a spreadsheet is a manual, non-versioned record that cannot reliably or automatically trace model versions.
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
Versioning both datasets and training code in Git and DVC pins the exact inputs and logic behind each model artefact. That linkage is what makes a training run reproducible and traceable to a specific commit and data revision.
- ✗
Manually deploy the model to production after approval.
Why it's wrong here
Manual deployment inserts an unrepeatable human step, so identical inputs can yield different releases and no automated audit trail links artefact to pipeline run. It is tempting because approval gates are legitimate governance controls, and manual release would be acceptable where no CI/CD automation exists and deployments are infrequent.
- ✗
Store only the final model artifact in a shared drive.
Why it's wrong here
A single final artifact records no training data, hyperparameters, code version or environment, so runs cannot be reproduced or traced. It is tempting because shared drives centralise storage and simplify distribution, which works for archiving released models rather than for pipeline reproducibility.
- ✗
Use a spreadsheet to record model version numbers.
Why it's wrong here
A spreadsheet records version numbers as static text, so it cannot link a model artefact to the exact code commit, dataset and parameters that produced it, breaking automated traceability in the pipeline. It is tempting because spreadsheets suit ad-hoc manual logging, and would suffice for a small team tracking a handful of experiments without CI/CD.
- ✓
Package the model in a Docker container with a fixed base image.
Why this is correct
Packaging the model in a Docker container with a fixed base image pins the runtime, libraries and dependencies, so every pipeline run produces an identical environment. This directly satisfies the reproducibility constraint, since the same image digest yields the same inference behaviour regardless of the host.
About these practice questions
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JA
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.