- A
Enable the 'Multiple intents' setting in the Language service project.
This setting allows the model to predict multiple intents for a single utterance.
- B
Upgrade the Language service tier from Standard to Custom.
Why wrong: The Standard tier already supports multiple intents and pre-built entities; upgrading is unnecessary.
- C
Configure the project to use Orchestration workflow.
Why wrong: Orchestration workflow is used to route utterances to different projects, not to enable multiple intents within a single project.
- D
Set the project language to 'Multilingual' to enable entity recognition.
Why wrong: The multilingual option is for supporting multiple languages, not for enabling multiple intents or pre-built entities.
- E
Define list entities for numbers and dates.
Why wrong: Pre-built entities like numbers and dates are automatically recognized without needing to define them as list entities.
Quick Answer
The correct action is to enable the 'Multiple intents' setting in the Language service project. This configuration is essential because Conversational Language Understanding (CLU) in Azure Language Service allows a single utterance to be classified with more than one intent when this setting is activated, enabling the model to parse compound user inputs like "Book a flight and check the weather." Additionally, prebuilt entities for numbers and dates are supported out of the box in CLU projects, requiring no special configuration beyond selecting them in the entity schema. On the AI-102 exam, this scenario tests your understanding of CLU project settings versus custom entity creation—a common trap is assuming you must build custom entities for standard data types or that multiple intents require separate utterances. Remember the memory tip: "Multiple intents, one switch; prebuilt entities, just a click."
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. 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.
A company is building a chatbot using Azure Language Service and wants to ensure that the chatbot can understand user intents and extract entities from user utterances. The chatbot must be able to handle multiple intents in a single utterance and must support pre-built entities such as numbers and dates. Which action should the developer take to configure the Language service accordingly?
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
Enable the 'Multiple intents' setting in the Language service project.
Option A is correct because the Azure Language Service's Conversational Language Understanding (CLU) project includes a 'Multiple intents' setting that, when enabled, allows the model to predict more than one intent per utterance. This is essential for handling compound user inputs where the user expresses multiple goals in a single sentence.
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.
- ✓
Enable the 'Multiple intents' setting in the Language service project.
Why this is correct
This setting allows the model to predict multiple intents for a single utterance.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Upgrade the Language service tier from Standard to Custom.
Why it's wrong here
The Standard tier already supports multiple intents and pre-built entities; upgrading is unnecessary.
- ✗
Configure the project to use Orchestration workflow.
Why it's wrong here
Orchestration workflow is used to route utterances to different projects, not to enable multiple intents within a single project.
- ✗
Set the project language to 'Multilingual' to enable entity recognition.
Why it's wrong here
The multilingual option is for supporting multiple languages, not for enabling multiple intents or pre-built entities.
- ✗
Define list entities for numbers and dates.
Why it's wrong here
Pre-built entities like numbers and dates are automatically recognized without needing to define them as list entities.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse the 'Multiple intents' setting with Orchestration workflow or assume that pre-built entities require manual list definitions, when in fact the former is a project-level toggle and the latter are automatically available without any custom configuration.
Detailed technical explanation
How to think about this question
Under the hood, when 'Multiple intents' is enabled, the CLU model uses a multi-label classification approach, where each utterance can be assigned to multiple intent classes with associated confidence scores. Pre-built entities like numbers and dates are automatically recognized by the Language service's built-in entity extractors, which leverage pre-trained models from Microsoft's NLP stack, and they do not require any custom entity definition or list configuration.
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
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement natural language processing solutions — This question tests Implement natural language processing solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Enable the 'Multiple intents' setting in the Language service project. — Option A is correct because the Azure Language Service's Conversational Language Understanding (CLU) project includes a 'Multiple intents' setting that, when enabled, allows the model to predict more than one intent per utterance. This is essential for handling compound user inputs where the user expresses multiple goals in a single sentence.
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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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.
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