AI-102 Plan and manage an Azure AI solution Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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.
Your team is developing a chatbot using Azure AI Bot Service with language understanding via Azure AI Language (CLU). You need to ensure that the chatbot can handle multiple intents in a single user utterance and return confidence scores for each. Which configuration should you use?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Use a Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled.
Option A is correct because Azure AI Language's Conversational Language Understanding (CLU) supports a 'Multiple intents' setting that allows the model to predict multiple intents from a single utterance, each with its own confidence score. This is the native way to handle multi-intent scenarios without requiring orchestration or custom logic.
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.
✓
Use a Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled.
Why this is correct
CLU supports multiple intents per utterance with confidence scores.
Related concept
Read the scenario before looking for a memorised answer.
✗
Use an Orchestration workflow project that connects to multiple CLU projects.
Why it's wrong here
Orchestration routes to different projects but does not return multiple intents from a single utterance.
✗
Use a standard CLU project and manually combine intents.
Why it's wrong here
Standard CLU returns a single predicted intent per utterance.
✗
Use Azure AI QnA Maker with custom logic to detect intents.
Why it's wrong here
QnA Maker does not support intent detection.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse Orchestration workflow (which routes to different projects) with the ability to handle multiple intents within a single CLU project, leading them to choose Option B incorrectly.
Detailed technical explanation
How to think about this question
Under the hood, CLU's multiple intents feature uses a multi-label classification model that outputs a probability distribution across all intents, allowing the service to return confidence scores for each intent that meets a configurable threshold. This is particularly useful in scenarios like a travel chatbot where a user says 'Book a flight and check hotel availability,' requiring both intents to be recognized simultaneously. The confidence scores are computed using softmax normalization, and the model can be trained with utterances that contain multiple intent labels.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Use a Conversational Language Understanding (CLU) project with the 'Multiple intents' setting enabled. — Option A is correct because Azure AI Language's Conversational Language Understanding (CLU) supports a 'Multiple intents' setting that allows the model to predict multiple intents from a single utterance, each with its own confidence score. This is the native way to handle multi-intent scenarios without requiring orchestration or custom logic.
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.
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Question Discussion
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