- A
Roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain
Reverting removes the damage, cleaning ensures future data is correct, and retraining updates the model.
- B
Switch to a simpler linear regression model that is less sensitive to data quality issues
Why wrong: Simpler model may not capture necessary patterns.
- C
Retrain the model using all available data, including the corrupted sensor data
Why wrong: Including corrupted data will degrade the model.
- D
Apply a weight to sensor data from that plant to reduce its influence
Why wrong: Weighting may mitigate but not eliminate the impact.
Question 191 of 1,000
Data Corruption in Predictive Maintenance: Rollback and Cleanse
This AI0-001 practice question tests your understanding of ai security, ethics and governance. 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.
A manufacturing company uses a predictive maintenance AI system to schedule equipment repairs. The system was trained on sensor data from machinery. Recently, the system has been missing failures, leading to unexpected downtime. An investigation reveals that the sensor data from one plant has been corrupted due to a sensor malfunction. The corrupted data was used in retraining. The company needs to restore system accuracy quickly. The data science team can access the training logs. What is the best course of action?
Quick Answer
The correct answer is to roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain. This approach directly addresses the root cause of the accuracy drop by isolating and removing the corrupted sensor data from the model’s training history, preventing it from influencing future predictions. In the context of handling data corruption in AI predictive maintenance, rolling back restores the last known reliable state, while cleansing ensures the retraining dataset is clean, which is essential for recovering failure detection accuracy. On the CompTIA AI+ AI0-001 exam, this scenario tests your understanding of model lifecycle management and data integrity—a common trap is assuming you can simply retrain with mixed data or reweight samples, but corruption often requires a full rollback to avoid lingering bias. A useful memory tip: “Rollback first, then cleanse—never patch a poisoned model.”
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
✓
Roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain
Rolling back to the previous model version isolates the system from the corrupted sensor data that caused accuracy degradation. Cleaning the sensor data before retraining ensures the model learns from accurate patterns, restoring predictive maintenance reliability. This approach directly addresses the root cause—data corruption—without introducing new risks.
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.
- ✓
Roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain
Why this is correct
Reverting removes the damage, cleaning ensures future data is correct, and retraining updates the model.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Switch to a simpler linear regression model that is less sensitive to data quality issues
Why it's wrong here
Simpler model may not capture necessary patterns.
- ✗
Retrain the model using all available data, including the corrupted sensor data
Why it's wrong here
Including corrupted data will degrade the model.
- ✗
Apply a weight to sensor data from that plant to reduce its influence
Why it's wrong here
Weighting may mitigate but not eliminate the impact.
Common exam traps
Common exam trap: answer the scenario, not the keyword
CompTIA often tests the misconception that simpler models are inherently more robust to data issues, but the trap here is that model complexity is not the root cause—data integrity is, so the correct fix is to revert and clean the data, not change the algorithm.
Detailed technical explanation
How to think about this question
In predictive maintenance, sensor data often exhibits non-linear relationships and temporal dependencies that complex models (e.g., gradient-boosted trees or LSTMs) capture effectively. Corrupted data introduces label noise and feature distortion, which can cause the model to overfit to spurious correlations. Rolling back to a checkpoint before corruption and retraining on cleaned data is analogous to version control best practices in MLOps, where model artifacts are immutable and data lineage is tracked via tools like DVC or MLflow.
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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Roll back to the previous model version before the corrupt data was ingested, then clean the sensor data and retrain — Rolling back to the previous model version isolates the system from the corrupted sensor data that caused accuracy degradation. Cleaning the sensor data before retraining ensures the model learns from accurate patterns, restoring predictive maintenance reliability. This approach directly addresses the root cause—data corruption—without introducing new risks.
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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Last reviewed: Jul 4, 2026
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