- A
Integration with Microsoft Purview for data governance.
Why wrong: Both services can integrate with Purview; it's not a deciding factor.
- B
Latency requirements: Azure OpenAI may offer lower latency for standard models.
Azure OpenAI endpoints are optimized for low latency, whereas Azure ML may require additional optimization.
- C
Ability to scale to thousands of concurrent requests.
Why wrong: Both services can scale; scaling is not a distinguishing factor.
- D
Need for custom model architecture: Azure ML supports custom models, Azure OpenAI uses pre-trained.
If you need a custom model, Azure ML is required; Azure OpenAI provides only pre-trained models.
- E
Operational overhead: Azure OpenAI is a fully managed service.
Azure OpenAI reduces management tasks compared to deploying on Azure ML.
AI-102 Implement generative AI solutions Practice Question
This AI-102 practice question tests your understanding of implement generative ai 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.
Which THREE factors should you consider when choosing between Azure OpenAI Service and Azure Machine Learning for deploying a generative AI model?
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
Latency requirements: Azure OpenAI may offer lower latency for standard models.
Options A, C, and E are correct. A: Azure OpenAI is fully managed with less operational overhead. C: Azure OpenAI offers pre-trained models, while Azure ML allows custom models. E: Azure OpenAI may have lower latency for similar model sizes. B is wrong because both services support scaling. D is wrong because both services support governance.
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.
- ✗
Integration with Microsoft Purview for data governance.
Why it's wrong here
Both services can integrate with Purview; it's not a deciding factor.
- ✓
Latency requirements: Azure OpenAI may offer lower latency for standard models.
Why this is correct
Azure OpenAI endpoints are optimized for low latency, whereas Azure ML may require additional optimization.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Ability to scale to thousands of concurrent requests.
Why it's wrong here
Both services can scale; scaling is not a distinguishing factor.
- ✓
Need for custom model architecture: Azure ML supports custom models, Azure OpenAI uses pre-trained.
Why this is correct
If you need a custom model, Azure ML is required; Azure OpenAI provides only pre-trained models.
Related concept
Read the scenario before looking for a memorised answer.
- ✓
Operational overhead: Azure OpenAI is a fully managed service.
Why this is correct
Azure OpenAI reduces management tasks compared to deploying on Azure ML.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Detailed technical explanation
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
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Implement generative AI solutions — study guide chapter
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement generative AI solutions — This question tests Implement generative AI solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Latency requirements: Azure OpenAI may offer lower latency for standard models. — Options A, C, and E are correct. A: Azure OpenAI is fully managed with less operational overhead. C: Azure OpenAI offers pre-trained models, while Azure ML allows custom models. E: Azure OpenAI may have lower latency for similar model sizes. B is wrong because both services support scaling. D is wrong because both services support governance.
What should I do if I get this AI-102 question wrong?
Identify which AI-102 exam domain this question belongs to, then review the specific concept being tested. Practise related questions in that domain and focus on understanding why each wrong answer is tempting — not just why the correct answer is right.
What is the key concept behind this question?
Read the scenario before looking for a memorised answer.
About these practice questions
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Last reviewed: Jun 20, 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.
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