- A
Collect more labeled scans from the new hospital and train a new model from scratch.
Why wrong: Training from scratch requires more data and time; 500 scans may be insufficient.
- B
Create a new Custom Vision project and train only on the 500 new scans.
Why wrong: 500 scans are insufficient for training from scratch, leading to poor performance.
- C
Apply image preprocessing to normalize the new hospital's scans to match the old hospital's style, then use the existing model.
Why wrong: Normalization may not address all differences and requires extra processing steps.
- D
Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
Transfer learning with new data quickly adapts the model to the new domain with minimal effort.
AI-102 Implement computer vision solutions Practice Question
This AI-102 practice question tests your understanding of implement computer vision solutions. Match the stated requirement to the specific cloud service, access model, or configuration option — many options are valid in isolation but not for this scenario. 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.
You are a data scientist at a healthcare startup. You have deployed a custom object detection model using Azure Custom Vision to detect tumors in MRI scans. The model was trained on 10,000 labeled scans from a single hospital. After deployment, the model performs well on scans from that hospital but poorly on scans from a different hospital with a different MRI machine. The new hospital's scans have slightly different contrast and resolution. The model's precision drops from 0.92 to 0.65, and recall drops from 0.88 to 0.50. You have access to 500 labeled scans from the new hospital. You need to improve the model's performance on the new hospital's data as quickly as possible with minimal effort. What should you do?
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 the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
Option D is correct because Azure Custom Vision supports transfer learning, allowing you to take an existing trained model and retrain it with new labeled data. By using the 500 labeled scans from the new hospital as a training set, you can fine-tune the model to adapt to the different contrast and resolution characteristics without starting from scratch. This approach is the fastest and requires minimal effort, leveraging the previously learned features while incorporating domain-specific adjustments.
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.
- ✗
Collect more labeled scans from the new hospital and train a new model from scratch.
Why it's wrong here
Training from scratch requires more data and time; 500 scans may be insufficient.
- ✗
Create a new Custom Vision project and train only on the 500 new scans.
Why it's wrong here
500 scans are insufficient for training from scratch, leading to poor performance.
- ✗
Apply image preprocessing to normalize the new hospital's scans to match the old hospital's style, then use the existing model.
Why it's wrong here
Normalization may not address all differences and requires extra processing steps.
- ✓
Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital.
Why this is correct
Transfer learning with new data quickly adapts the model to the new domain with minimal effort.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may overestimate the need for large datasets or manual preprocessing, failing to recognize that Azure Custom Vision's built-in transfer learning is designed to efficiently adapt models with minimal new data.
Detailed technical explanation
How to think about this question
Transfer learning in Azure Custom Vision works by using a pre-trained neural network (e.g., ResNet or MobileNet) as a feature extractor and then fine-tuning the final layers on the new dataset. The 500 labeled scans provide enough examples to adjust the decision boundaries for the new domain, while the base features (e.g., edge detection, texture patterns) remain largely intact. In real-world scenarios, this technique is critical for medical imaging where data from different institutions often has distribution shifts due to varying acquisition parameters.
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 media company stores terabytes of video archives that are accessed once a year for audit purposes. Moving these objects to a cold storage tier (Azure Archive, S3 Glacier, or Google Nearline) costs a fraction of hot storage. Questions like this test whether you understand storage tiers, access frequency tradeoffs, and retrieval latency requirements.
What to study next
Got this wrong? Here's your next step.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Implement computer vision solutions — study guide chapter
Learn the concepts, then practise the questions
- →
Implement computer vision solutions practice questions
Targeted practice on this topic area only
- →
All AI-102 questions
988 questions across all exam domains
- →
Microsoft Azure AI Engineer Associate AI-102 study guide
Full concept coverage aligned to exam objectives
- →
AI-102 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-102 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Implement an agentic solution practice questions
Practise AI-102 questions linked to Implement an agentic solution.
Implement computer vision solutions practice questions
Practise AI-102 questions linked to Implement computer vision solutions.
Implement knowledge mining and information extraction solutions practice questions
Practise AI-102 questions linked to Implement knowledge mining and information extraction solutions.
Implement image and video processing solutions practice questions
Practise AI-102 questions linked to Implement image and video processing solutions.
Implement natural language processing solutions practice questions
Practise AI-102 questions linked to Implement natural language processing solutions.
Implement generative AI solutions practice questions
Practise AI-102 questions linked to Implement generative AI solutions.
Implement agentic AI solutions practice questions
Practise AI-102 questions linked to Implement agentic AI solutions.
Implement knowledge mining and document intelligence solutions practice questions
Practise AI-102 questions linked to Implement knowledge mining and document intelligence solutions.
Plan and manage an Azure AI solution practice questions
Practise AI-102 questions linked to Plan and manage an Azure AI solution.
Implement content moderation solutions practice questions
Practise AI-102 questions linked to Implement content moderation solutions.
AI-102 fundamentals practice questions
Practise AI-102 questions linked to AI-102 fundamentals.
AI-102 scenario practice questions
Practise AI-102 questions linked to AI-102 scenario.
Practice this exam
Start a free AI-102 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 AI-102 question test?
Implement computer vision solutions — This question tests Implement computer vision solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use the existing model as a starting point and retrain it with the 500 labeled scans from the new hospital. — Option D is correct because Azure Custom Vision supports transfer learning, allowing you to take an existing trained model and retrain it with new labeled data. By using the 500 labeled scans from the new hospital as a training set, you can fine-tune the model to adapt to the different contrast and resolution characteristics without starting from scratch. This approach is the fastest and requires minimal effort, leveraging the previously learned features while incorporating domain-specific adjustments.
What should I do if I get this AI-102 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.
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 11, 2026
This AI-102 practice question is part of Courseiva's free Microsoft 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 AI-102 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.