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.”
AI0-001 AI Security, Ethics and Governance Practice Question
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?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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
Option D is correct because reverting to the last good model and then cleaning the data ensures the corruption is removed. Option A is wrong because retraining with both corrupted and good data may not remove the influence. Option B is wrong because reweighting may not fully correct. Option C is wrong because using a simpler model may not capture complex patterns.
Key principle: NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
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.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
✗
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: NAT rules depend on direction and matching traffic
NAT is not only about the public address. The inside/outside interface roles and the ACL or rule that matches traffic are just as important.
Detailed technical explanation
How to think about this question
NAT questions usually test address translation, overload/PAT behaviour, static mappings and whether the right traffic is being translated. Read the interface direction and address terms carefully.
KKey Concepts to Remember
Static NAT maps one inside address to one outside address.
PAT allows many inside hosts to share one public address using ports.
Inside local and inside global describe the private and translated addresses.
NAT ACLs identify traffic for translation, not always security filtering.
TExam Day Tips
→Identify inside and outside interfaces first.
→Check whether the scenario needs static NAT, dynamic NAT or PAT.
→Do not confuse NAT matching ACLs with normal packet-filtering intent.
Key takeaway
NAT direction and interface roles matter as much as the IP address mapping. Inside/outside designation controls which traffic is translated.
Real-world example
How this comes up in practice
A small business has 20 workstations on the 192.168.1.0/24 network and one public IP from its ISP. The router uses PAT (NAT overload) so all 20 devices share one public address using different source ports. NAT questions test whether you understand the four address terms and which direction each translation applies.
What to study next
Got this wrong? Here's your next step.
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
AI Security, Ethics and Governance — This question tests AI Security, Ethics and Governance — Static NAT maps one inside address to one outside address..
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 — Option D is correct because reverting to the last good model and then cleaning the data ensures the corruption is removed. Option A is wrong because retraining with both corrupted and good data may not remove the influence. Option B is wrong because reweighting may not fully correct. Option C is wrong because using a simpler model may not capture complex patterns.
What should I do if I get this AI0-001 question wrong?
Review the four NAT address types (inside local, inside global, outside local, outside global), PAT port overload, and static vs dynamic NAT use cases. Then practise related AI0-001 NAT questions on configuration and troubleshooting.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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