The correct choice is elastic weight consolidation (EWC) to regularize important weights. EWC is a regularization technique that identifies which neural network weights are most critical for previously learned tasks—in this case, the simulated driving environment—and penalizes large changes to those weights during fine-tuning on new real-world data. This directly mitigates catastrophic forgetting by preserving the knowledge that was essential for high rewards in simulation while still allowing the model to adapt to real-world obstacles. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of continual learning and transfer learning pitfalls; a common trap is assuming that increasing the learning rate (Option A) helps adaptation, when in fact it accelerates forgetting. A useful memory tip: think of EWC as a “weight anchor” that holds the most important knowledge steady while the rest of the network learns new patterns.
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 self-driving car company uses a reinforcement learning agent to navigate. The agent was trained in a simulated environment and achieved high rewards. When deployed in the real world, the agent fails to avoid obstacles. The team collects real-world driving data and uses it to fine-tune the model. However, fine-tuning leads to catastrophic forgetting of the simulated knowledge. Which technique should the team use to mitigate this? A. Increase the learning rate during fine-tuning. B. Use elastic weight consolidation (EWC) to regularize important weights. C. Train the model from scratch using only real-world data. D. Increase the number of layers in the network.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use elastic weight consolidation (EWC) to regularize important weights.
Option B is correct. Elastic weight consolidation (EWC) is a regularization technique that penalizes changes to weights that are important for previous tasks (simulation), thereby preventing catastrophic forgetting. Option A (increasing learning rate) would make forgetting worse. Option C (training from scratch) discards the valuable simulation knowledge. Option D (adding layers) may increase capacity but does not address forgetting.
Key principle: ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
✗
Increase the number of layers in the network.
Why it's wrong here
Adding layers does not address catastrophic forgetting; it may even introduce more parameters that can overwrite previous knowledge.
✓
Use elastic weight consolidation (EWC) to regularize important weights.
Why this is correct
EWC selectively slows down learning on important weights for previous tasks, preserving simulated knowledge.
Related concept
Standard ACLs match source addresses.
✗
Train the model from scratch using only real-world data.
Why it's wrong here
This discards all simulation knowledge, which is wasteful and may lead to poor performance due to limited real data.
✗
Increase the learning rate during fine-tuning.
Why it's wrong here
A higher learning rate would cause larger weight updates, accelerating forgetting.
Common exam traps
Common exam trap: ACLs stop at the first match
ACLs are processed top to bottom. The first matching entry wins, and an implicit deny usually exists at the end.
Detailed technical explanation
How to think about this question
ACL questions test precision: source, destination, protocol, port and direction. A generally correct ACL can still fail if it is applied on the wrong interface or in the wrong direction.
KKey Concepts to Remember
Standard ACLs match source addresses.
Extended ACLs can match source, destination, protocol and ports.
The first matching ACL entry is used.
There is usually an implicit deny at the end.
TExam Day Tips
→Check inbound versus outbound direction.
→Read the ACL from top to bottom.
→Look for a broader permit or deny above the intended line.
Key takeaway
ACLs process entries top to bottom and stop at the first match. Entry order and interface direction matter as much as the permit or deny statement.
Real-world example
How this comes up in practice
A security administrator must allow nursing staff to reach a patient records server while blocking access from the guest Wi-Fi VLAN. After applying an extended ACL, traffic is still blocked from nursing workstations. The ACL was applied outbound instead of inbound on the wrong interface. Questions like this test ACL direction and placement rules.
What to study next
Got this wrong? Here's your next step.
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related AI0-001 ACL questions on filtering logic and placement.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Standard ACLs match source addresses..
What is the correct answer to this question?
The correct answer is: Use elastic weight consolidation (EWC) to regularize important weights. — Option B is correct. Elastic weight consolidation (EWC) is a regularization technique that penalizes changes to weights that are important for previous tasks (simulation), thereby preventing catastrophic forgetting. Option A (increasing learning rate) would make forgetting worse. Option C (training from scratch) discards the valuable simulation knowledge. Option D (adding layers) may increase capacity but does not address forgetting.
What should I do if I get this AI0-001 question wrong?
Review ACL processing order, placement rules (standard near destination, extended near source), and inbound vs outbound direction. Study wildcard masks and implicit deny. Then practise related AI0-001 ACL questions on filtering logic and placement.
What is the key concept behind this question?
Standard ACLs match source addresses.
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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.
Question Discussion
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