- A
Increase the confidence score threshold in the project settings.
Why wrong: This may cause many valid queries to be unanswered.
- B
Reduce the number of QnA pairs to decrease ambiguity.
Why wrong: This may remove valid content and reduce coverage.
- C
Add alternate phrases to existing QnA pairs for common user queries.
This improves matching without retraining.
- D
Enable active learning to let the bot suggest new questions based on user queries.
Why wrong: Active learning requires user feedback over time; it does not immediately improve relevance.
Question 458 of 993
AI-102 Practice Question: Implement natural language processing solutions
This AI-102 practice question tests your understanding of implement natural language processing 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.
A company deploys a custom question answering project in Azure AI Language. Users report that the bot sometimes returns irrelevant answers. The knowledge base contains hundreds of QnA pairs. You need to improve answer relevance without retraining the model. 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
✓
Add alternate phrases to existing QnA pairs for common user queries.
Option C is correct because adding alternate phrases to existing QnA pairs directly improves the bot's ability to match user queries to the correct answer without retraining. In Azure AI Language's custom question answering, the model uses a ranker that compares the user's input against the questions and alternate phrases in the knowledge base. By providing more varied phrasings for common queries, you increase the likelihood of a high-confidence match, thereby reducing irrelevant answers.
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.
- ✗
Increase the confidence score threshold in the project settings.
Why it's wrong here
This may cause many valid queries to be unanswered.
- ✗
Reduce the number of QnA pairs to decrease ambiguity.
Why it's wrong here
This may remove valid content and reduce coverage.
- ✓
Add alternate phrases to existing QnA pairs for common user queries.
Why this is correct
This improves matching without retraining.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Enable active learning to let the bot suggest new questions based on user queries.
Why it's wrong here
Active learning requires user feedback over time; it does not immediately improve relevance.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'active learning' (a feature for suggesting new questions) with a direct method to improve current answer relevance, when in fact it is a long-term knowledge base enhancement tool that does not immediately affect matching accuracy.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's custom question answering uses a transformer-based ranker that computes similarity scores between the user query and each QnA pair's questions and alternate phrases. Alternate phrases are stored as additional text embeddings in the same index, so adding them expands the semantic coverage without retraining the underlying model. In a real-world scenario, a retail bot with hundreds of product FAQs can see a 20-30% improvement in first-match accuracy by adding just 3-5 alternate phrases per common query.
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: Add alternate phrases to existing QnA pairs for common user queries. — Option C is correct because adding alternate phrases to existing QnA pairs directly improves the bot's ability to match user queries to the correct answer without retraining. In Azure AI Language's custom question answering, the model uses a ranker that compares the user's input against the questions and alternate phrases in the knowledge base. By providing more varied phrasings for common queries, you increase the likelihood of a high-confidence match, thereby reducing irrelevant answers.
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: Jul 4, 2026
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