- A
Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.
Separate projects ensure optimal language-specific models and accurate answers.
- B
Use Azure Cognitive Search with semantic ranking to index the QnA pairs.
Why wrong: Cognitive Search is a different service; it does not provide the same question-answering capabilities as Custom Question Answering.
- C
Add synonyms in the project for French and German terms to improve matching.
Why wrong: Synonyms help within a language but do not address the core issue of mixing languages.
- D
Deploy the same project to multiple regions and use traffic manager.
Why wrong: Regional deployment does not improve language accuracy within a single project.
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 developer at a global e-commerce company. You are building a multilingual chatbot using Azure AI Language that supports English, French, German, and Spanish. The chatbot must answer frequently asked questions about order status, returns, and shipping. You plan to use Custom Question Answering with a single project containing questions and answers in all four languages. However, during testing, you notice that queries in French and German often return incorrect answers or no answer, while English and Spanish work well. You need to ensure accurate answers across all four languages. 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
Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.
Option A is correct because Custom Question Answering (CQA) projects are language-specific; a single project cannot reliably handle multiple languages due to differences in tokenization, stemming, and stop-word handling. By creating a separate project per language and routing queries based on language detection (e.g., using Azure AI Language's language detection API), you ensure that each project's model is optimized for its respective language, improving answer accuracy for French and German.
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.
- ✓
Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection.
Why this is correct
Separate projects ensure optimal language-specific models and accurate answers.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Use Azure Cognitive Search with semantic ranking to index the QnA pairs.
Why it's wrong here
Cognitive Search is a different service; it does not provide the same question-answering capabilities as Custom Question Answering.
- ✗
Add synonyms in the project for French and German terms to improve matching.
Why it's wrong here
Synonyms help within a language but do not address the core issue of mixing languages.
- ✗
Deploy the same project to multiple regions and use traffic manager.
Why it's wrong here
Regional deployment does not improve language accuracy within a single project.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates assume a single Custom Question Answering project can handle multiple languages by simply adding translated QnA pairs, overlooking that the underlying NLP pipeline is language-specific and cannot correctly process queries in languages other than the project's configured primary language.
Detailed technical explanation
How to think about this question
Custom Question Answering uses a language-specific ranker that applies stemming, lemmatization, and stop-word removal tailored to the project's primary language. When multiple languages are mixed in one project, the ranker applies the default language's processing (e.g., English) to all queries, causing poor matching for French and German due to incorrect tokenization and morphological analysis. Language detection via Azure AI Language's API (which uses a pre-trained model) can route queries to language-specific projects, each with its own trained model and language-appropriate NLP pipeline.
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
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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: Create a separate Custom Question Answering project for each language and route user queries to the appropriate project based on language detection. — Option A is correct because Custom Question Answering (CQA) projects are language-specific; a single project cannot reliably handle multiple languages due to differences in tokenization, stemming, and stop-word handling. By creating a separate project per language and routing queries based on language detection (e.g., using Azure AI Language's language detection API), you ensure that each project's model is optimized for its respective language, improving answer accuracy for French and German.
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
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Last reviewed: Jul 4, 2026
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