The correct answer is to redeploy version 1 by updating the production deployment to use its artifact. This is the fastest rollback method because the model registry stores every version’s artifact—the serialized model file—so you can simply point the existing production endpoint to version 1’s artifact without rebuilding, retraining, or re-deploying the entire pipeline. On the CompTIA AI+ AI0-001 exam, this tests your understanding of artifact registry integration and deployment lifecycle management, where the key insight is that a rollback is a pointer update, not a full redeployment. A common trap is thinking you must re-deploy from scratch or retrain the old model, which wastes time and resources. Remember the mnemonic: “Point, don’t rebuild—the artifact is the key to the rollback.”
AI0-001 AI Implementation and Operations Practice Question
This AI0-001 practice question tests your understanding of ai implementation and operations. Read the scenario carefully and evaluate each option against the stated constraints before committing to an answer. After answering, compare your reasoning against the explanation and wrong-answer breakdown below. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
An ML team uses the model registry above. After deploying version 3 to production, they discover it has a critical bug. What is the fastest way to roll back to a stable version without re-deploying from scratch?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Redeploy version 1 by updating the production deployment to use its artifact
Option B is correct because it directly updates the production deployment to reference the artifact of version 1, which is a stable, previously validated version. This avoids the overhead of re-deploying from scratch by simply pointing the existing deployment to a known good artifact in the model registry, leveraging the registry's artifact storage and deployment integration.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Promote version 2 from Staging to Production
Why it's wrong here
Version 2 is in Staging and may not be validated for production.
✓
Redeploy version 1 by updating the production deployment to use its artifact
Why this is correct
Version 1 is still in Production and can be quickly redeployed.
Related concept
Read the scenario before looking for a memorised answer.
✗
Retrain the model with corrected data
Why it's wrong here
Retraining is time-consuming and not the fastest rollback.
✗
Delete version 3 from the registry
Why it's wrong here
Deleting does not change the deployed model.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that deleting a model version or promoting a staging version is the fastest rollback, when in reality the fastest method is to update the existing deployment's artifact reference to a known stable version.
Detailed technical explanation
How to think about this question
In model registries like MLflow or Vertex AI, each model version is associated with a specific artifact (e.g., a serialized model file). A deployment is a separate entity that references a model version's artifact. Rolling back involves updating the deployment's pointer to a different artifact, which can be done via a single API call (e.g., `update_deployment(model_version='1')`) without retraining or re-deploying infrastructure. This is analogous to a Kubernetes deployment updating its container image tag, where the underlying pod spec changes without recreating the deployment object.
KKey Concepts to Remember
Read the scenario before looking for a memorised answer.
Find the constraint that changes the correct option.
Eliminate answers that are true in general but not in this case.
TExam Day Tips
→Watch for words such as best, first, most likely and least administrative effort.
→Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
How this comes up in practice
A practitioner preparing for the AI0-001 exam encounters this exact type of scenario on the job. The correct answer here is not the most general option — it is the best answer for the specific constraint described. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Real exam questions reward reading the full scenario before eliminating options, because the constraint defines which answer fits.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
AI Implementation and Operations — This question tests AI Implementation and Operations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Redeploy version 1 by updating the production deployment to use its artifact — Option B is correct because it directly updates the production deployment to reference the artifact of version 1, which is a stable, previously validated version. This avoids the overhead of re-deploying from scratch by simply pointing the existing deployment to a known good artifact in the model registry, leveraging the registry's artifact storage and deployment integration.
What should I do if I get this AI0-001 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
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