- A
Increase the number of layers in the network.
Why wrong: Adding layers does not address catastrophic forgetting; it may even introduce more parameters that can overwrite previous knowledge.
- B
Use elastic weight consolidation (EWC) to regularize important weights.
EWC selectively slows down learning on important weights for previous tasks, preserving simulated knowledge.
- C
Train the model from scratch using only real-world data.
Why wrong: This discards all simulation knowledge, which is wasteful and may lead to poor performance due to limited real data.
- D
Increase the learning rate during fine-tuning.
Why wrong: A higher learning rate would cause larger weight updates, accelerating forgetting.
Question 842 of 1,000
Catastrophic Forgetting — Elastic Weight Consolidation | CompTIA AI+ Explained
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.
Quick Answer
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.
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
✓
Use elastic weight consolidation (EWC) to regularize important weights.
Elastic Weight Consolidation (EWC) is a regularization technique specifically designed to prevent catastrophic forgetting when fine-tuning a neural network on a new task. It identifies the weights that are most important for the original task (simulated driving) and penalizes large changes to those weights during fine-tuning on real-world data, thereby preserving the learned knowledge while adapting to the new domain.
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.
- ✗
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
Read the scenario before looking for a memorised answer.
- ✗
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: answer the scenario, not the keyword
CompTIA often tests the concept of catastrophic forgetting by presenting fine-tuning as a solution and then offering tempting but incorrect options like increasing learning rate or network depth, which candidates might mistakenly associate with improving generalization or capacity.
Detailed technical explanation
How to think about this question
EWC works by adding a quadratic penalty term to the loss function for each parameter, scaled by the Fisher information matrix, which quantifies the importance of each weight to the original task. During fine-tuning, this penalty constrains the model from deviating far from the optimal parameters for the simulated environment, effectively creating a 'memory' of the old task without requiring the old data. In practice, EWC is particularly useful in continual learning scenarios where an agent must adapt to shifting distributions, such as a self-driving car transitioning from simulation to real-world conditions.
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 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
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FAQ
Questions learners often ask
What does this AI0-001 question test?
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use elastic weight consolidation (EWC) to regularize important weights. — Elastic Weight Consolidation (EWC) is a regularization technique specifically designed to prevent catastrophic forgetting when fine-tuning a neural network on a new task. It identifies the weights that are most important for the original task (simulated driving) and penalizes large changes to those weights during fine-tuning on real-world data, thereby preserving the learned knowledge while adapting to the new domain.
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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