- A
Automatically generating descriptive captions for images using a pre-trained model
Why wrong: Image captioning is a vision task — AI-assisted labelling is about pre-populating annotation labels for human review and correction.
- B
Using a partially trained model to pre-populate labels that human annotators verify and correct
AI-assisted labelling speeds annotation by having the model guess labels — humans only review and fix, reducing effort dramatically.
- C
Deploying a model to production without any human review of its outputs
Why wrong: Unreviewed deployment is autonomous AI — AI-assisted labelling specifically involves human review of model-suggested labels.
- D
Using AI to detect and remove incorrectly labelled examples from a completed dataset
Why wrong: Label quality cleaning is data validation — AI-assisted labelling is a real-time workflow that pre-populates labels during annotation.
Quick Answer
The correct answer is that AI-assisted labelling in Azure Machine Learning data labelling uses a partially trained model to pre-populate labels that human annotators verify and correct. This technique leverages active learning, where the model is initially trained on a small set of manually labelled data, then predicts labels for the remaining unlabelled dataset. Human reviewers step in to confirm or adjust these suggestions, and the corrected labels are fed back into the model to iteratively improve its accuracy, dramatically reducing the manual effort required. On the AI-900 exam, this concept tests your understanding of how Azure accelerates the labelling workflow without sacrificing quality—a common trap is confusing it with fully automated labelling, which lacks human verification. Remember the memory tip: “AI suggests, human corrects, model learns.”
AI-900 Practice Question: Describe Artificial Intelligence workloads and considerations
This AI-900 practice question tests your understanding of describe artificial intelligence workloads and considerations. 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.
What is 'AI-assisted labelling' in Azure Machine Learning data labelling?
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
Using a partially trained model to pre-populate labels that human annotators verify and correct
AI-assisted labelling in Azure Machine Learning uses a partially trained model to automatically suggest labels for unlabelled data. Human annotators then review and correct these suggestions, which speeds up the labelling process while maintaining quality. This is a form of active learning where the model iteratively improves as more labelled data is verified.
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.
- ✗
Automatically generating descriptive captions for images using a pre-trained model
Why it's wrong here
Image captioning is a vision task — AI-assisted labelling is about pre-populating annotation labels for human review and correction.
- ✓
Using a partially trained model to pre-populate labels that human annotators verify and correct
Why this is correct
AI-assisted labelling speeds annotation by having the model guess labels — humans only review and fix, reducing effort dramatically.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Deploying a model to production without any human review of its outputs
Why it's wrong here
Unreviewed deployment is autonomous AI — AI-assisted labelling specifically involves human review of model-suggested labels.
- ✗
Using AI to detect and remove incorrectly labelled examples from a completed dataset
Why it's wrong here
Label quality cleaning is data validation — AI-assisted labelling is a real-time workflow that pre-populates labels during annotation.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is confusing AI-assisted labelling with fully automated AI tasks (like image captioning or model deployment) and overlooking the critical human-in-the-loop verification step that distinguishes this feature from pure automation.
Detailed technical explanation
How to think about this question
Under the hood, Azure ML's AI-assisted labelling uses active learning to train a model on a small initial set of human-labelled data, then applies that model to predict labels for the remaining unlabelled data. The system selects the most uncertain predictions (low confidence) for human review, maximizing the efficiency of the labelling effort. This iterative process reduces the total number of manual labels needed by up to 50% in real-world scenarios like medical imaging or document classification.
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 cloud solutions architect for a retail company is evaluating services for a new workload. The correct answer here reflects best practice for the specific scenario described — not a general cloud recommendation. Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option. Cloud exam questions reward reading the constraint carefully: the same technology can be right or wrong depending on the use case.
What to study next
Got this wrong? Here's your next step.
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Describe Artificial Intelligence workloads and considerations — study guide chapter
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe Artificial Intelligence workloads and considerations — This question tests Describe Artificial Intelligence workloads and considerations — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Using a partially trained model to pre-populate labels that human annotators verify and correct — AI-assisted labelling in Azure Machine Learning uses a partially trained model to automatically suggest labels for unlabelled data. Human annotators then review and correct these suggestions, which speeds up the labelling process while maintaining quality. This is a form of active learning where the model iteratively improves as more labelled data is verified.
What should I do if I get this AI-900 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: Jun 11, 2026
This AI-900 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-900 exam.
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