- A
Use active learning to select the most uncertain predictions from the new hospital's data, then have an expert radiologist correct those labels
Active learning targets the most informative samples, maximizing improvement per expert effort.
- B
Randomly select 1,000 scans from the new hospital and have them re-labeled by the radiologist
Why wrong: Random selection may not focus on the most impactful corrections, and 1,000 may be insufficient.
- C
Collect 20,000 more scans with the same semi-automated labeling process
Why wrong: Adding more noisy labels will not correct the underlying label noise and may worsen accuracy.
- D
Reduce the CNN's number of layers and apply dropout to combat overfitting
Why wrong: This addresses overfitting but not label noise or domain shift; accuracy drop is likely due to distribution change.
Quick Answer
The answer is to use active learning to select the most uncertain predictions from the new hospital’s data, then have an expert radiologist correct those labels. This strategy directly addresses both domain shift and label noise in medical imaging by focusing expert effort on the samples where the model is least confident, which are most likely to represent the new distribution or contain mislabeled examples. On the CompTIA AI+ AI0-001 exam, this tests your understanding of how active learning efficiently improves model robustness when labeled data is unreliable and the target domain shifts—a common trap is choosing to add more noisy labels or randomly sampling, which waste resources or amplify errors. Remember the memory tip: “Uncertainty beats quantity” when fighting domain shift and label noise.
AI0-001 AI Models and Data Engineering Practice Question
This AI0-001 practice question tests your understanding of ai models and data engineering. 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 medical imaging team is developing an AI model to detect tumors from CT scans. They have 10,000 labeled scans, but the labels were created by a semi-automated process with an estimated 20% error rate (mislabeled tumor vs. no tumor). The team trains a convolutional neural network (CNN) and achieves 90% accuracy on a held-out test set that was carefully validated by an expert radiologist. However, when deployed to a new hospital's patient population, the accuracy drops to 70%. The team suspects domain shift and label noise. Which strategy is most likely to improve model robustness for the new hospital?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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 active learning to select the most uncertain predictions from the new hospital's data, then have an expert radiologist correct those labels
Option C is correct. Active learning helps select the most informative samples (e.g., uncertain predictions) for expert review, efficiently improving the model with limited expert effort. Option A is wrong because simply adding more noisy labels will amplify errors. Option B is wrong because random sampling may not capture the most valuable corrections. Option D is wrong because reducing model complexity may underfit, and dropouts fine-tuning might not address label noise or domain shift.
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.
- ✓
Use active learning to select the most uncertain predictions from the new hospital's data, then have an expert radiologist correct those labels
Why this is correct
Active learning targets the most informative samples, maximizing improvement per expert effort.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Static NAT maps one inside address to one outside address.
- ✗
Randomly select 1,000 scans from the new hospital and have them re-labeled by the radiologist
Why it's wrong here
Random selection may not focus on the most impactful corrections, and 1,000 may be insufficient.
- ✗
Collect 20,000 more scans with the same semi-automated labeling process
Why it's wrong here
Adding more noisy labels will not correct the underlying label noise and may worsen accuracy.
- ✗
Reduce the CNN's number of layers and apply dropout to combat overfitting
Why it's wrong here
This addresses overfitting but not label noise or domain shift; accuracy drop is likely due to distribution change.
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 Models and Data Engineering — study guide chapter
Learn the concepts, then practise the questions
- →
AI Models and Data Engineering practice questions
Targeted practice on this topic area only
- →
All AI0-001 questions
500 questions across all exam domains
- →
CompTIA AI+ AI0-001 study guide
Full concept coverage aligned to exam objectives
- →
AI0-001 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI0-001 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
AI Concepts and Foundations practice questions
Practise AI0-001 questions linked to AI Concepts and Foundations.
Machine Learning and Deep Learning practice questions
Practise AI0-001 questions linked to Machine Learning and Deep Learning.
AI Models and Data Engineering practice questions
Practise AI0-001 questions linked to AI Models and Data Engineering.
AI Implementation and Operations practice questions
Practise AI0-001 questions linked to AI Implementation and Operations.
AI Security, Ethics and Governance practice questions
Practise AI0-001 questions linked to AI Security, Ethics and Governance.
CompTIA A+ hardware practice questions
Practise AI0-001 questions linked to CompTIA A+ hardware.
CompTIA A+ mobile devices practice questions
Practise AI0-001 questions linked to CompTIA A+ mobile devices.
CompTIA A+ networking practice questions
Practise AI0-001 questions linked to CompTIA A+ networking.
CompTIA A+ operating systems practice questions
Practise AI0-001 questions linked to CompTIA A+ operating systems.
CompTIA A+ security practice questions
Practise AI0-001 questions linked to CompTIA A+ security.
CompTIA A+ software troubleshooting questions
Practise AI0-001 questions linked to CompTIA A+ software troubleshooting questions.
CompTIA A+ operational procedures questions
Practise AI0-001 questions linked to CompTIA A+ operational procedures questions.
Practice this exam
Start a free AI0-001 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
FAQ
Questions learners often ask
What does this AI0-001 question test?
AI Models and Data Engineering — This question tests AI Models and Data Engineering — Static NAT maps one inside address to one outside address..
What is the correct answer to this question?
The correct answer is: Use active learning to select the most uncertain predictions from the new hospital's data, then have an expert radiologist correct those labels — Option C is correct. Active learning helps select the most informative samples (e.g., uncertain predictions) for expert review, efficiently improving the model with limited expert effort. Option A is wrong because simply adding more noisy labels will amplify errors. Option B is wrong because random sampling may not capture the most valuable corrections. Option D is wrong because reducing model complexity may underfit, and dropouts fine-tuning might not address label noise or domain shift.
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: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
What is the key concept behind this question?
Static NAT maps one inside address to one outside address.
About these practice questions
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Last reviewed: Jun 23, 2026
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
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.