The answer is to add more training data, reduce model complexity, and apply regularization. These three actions directly address high variance by forcing the model to generalize rather than memorize noise. Adding more training data exposes the model to a broader range of patterns, while reducing complexity—such as using fewer features or a simpler algorithm—limits its ability to overfit. Regularization penalizes overly large coefficients, further constraining the model’s flexibility. On the CompTIA AI+ AI0-001 exam, this question tests your understanding of the bias-variance tradeoff, a core concept in model evaluation. A common trap is confusing variance reduction with increasing model complexity or relying on outlier removal, which is not a standard primary technique. Remember the mnemonic “MORE Data, LESS Complexity, REGularize” to recall the three pillars of reducing high variance in machine learning.
AI0-001 Machine Learning and Deep Learning Practice Question
This AI0-001 practice question tests your understanding of machine learning and deep learning. 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 company is deploying a machine learning model that predicts customer churn. The model currently has high variance. Which THREE actions should the data scientist take to reduce variance? (Select THREE.)
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Reduce model complexity (e.g., fewer features, simpler model).
Options B, C, and D are correct. Reducing model complexity (B) (e.g., fewer features, simpler model) directly limits variance. Adding more training data (C) helps the model learn a more general pattern, reducing variance. Regularization (D) penalizes large weights, controlling model complexity. Option A (increasing complexity) would increase variance. Option E (removing outliers) can sometimes reduce variance but is not a standard or primary technique; it may also reduce bias but is less reliable.
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.
✓
Reduce model complexity (e.g., fewer features, simpler model).
Why this is correct
Simpler models have lower variance.
Related concept
Static NAT maps one inside address to one outside address.
✓
Use regularization.
Why this is correct
Regularization (e.g., L1/L2) penalizes large coefficients, reducing variance.
Related concept
Static NAT maps one inside address to one outside address.
✓
Add more training data.
Why this is correct
More data helps the model generalize, reducing variance.
Related concept
Static NAT maps one inside address to one outside address.
✗
Remove outliers from the training data.
Why it's wrong here
Removing outliers can sometimes reduce variance but may also introduce bias; it is not a primary variance reduction technique.
✗
Increase model complexity.
Why it's wrong here
Increasing complexity (e.g., more features, deeper network) typically increases variance.
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.
Machine Learning and Deep Learning — This question tests Machine Learning and Deep Learning — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Reduce model complexity (e.g., fewer features, simpler model). — Options B, C, and D are correct. Reducing model complexity (B) (e.g., fewer features, simpler model) directly limits variance. Adding more training data (C) helps the model learn a more general pattern, reducing variance. Regularization (D) penalizes large weights, controlling model complexity. Option A (increasing complexity) would increase variance. Option E (removing outliers) can sometimes reduce variance but is not a standard or primary technique; it may also reduce bias but is less reliable.
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.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
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Question Discussion
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