- A
The language detection model misidentified the language
Why wrong: Language detection is not the cause; the issue is with key phrase extraction.
- B
You need to set a confidence threshold to exclude negative phrases
Why wrong: Confidence thresholds affect which phrases are returned, but they do not handle negation.
- C
Key phrase extraction does not consider negation
Key phrase extraction extracts noun phrases without considering negation modifiers.
- D
The service is not trained on your specific domain
Why wrong: The prebuilt model is general-purpose but negation handling is not domain-specific.
Question 965 of 993
Implement knowledge mining and information extraction solutions →easyMultiple ChoiceObjective-mapped
AI-102 Practice Question: Implement knowledge mining and information extraction solutions
This AI-102 practice question tests your understanding of implement knowledge mining and information extraction 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.
You are using Azure AI Language Service to extract key phrases from customer reviews. You notice that for reviews containing the word 'not good', the service sometimes extracts 'good' as a key phrase. What is the most likely reason?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue:
"most likely"Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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
✓
Key phrase extraction does not consider negation
Key phrase extraction in Azure AI Language Service uses a statistical model that identifies significant terms based on frequency and context, but it does not inherently understand negation. When the phrase 'not good' appears, the model may still extract 'good' as a key phrase because it recognizes 'good' as a high-value term, ignoring the negation. This is a known limitation of the feature, as it focuses on noun phrases and important terms rather than sentiment or negated constructs.
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.
- ✗
The language detection model misidentified the language
Why it's wrong here
Language detection is not the cause; the issue is with key phrase extraction.
- ✗
You need to set a confidence threshold to exclude negative phrases
Why it's wrong here
Confidence thresholds affect which phrases are returned, but they do not handle negation.
- ✓
Key phrase extraction does not consider negation
Why this is correct
Key phrase extraction extracts noun phrases without considering negation modifiers.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
The service is not trained on your specific domain
Why it's wrong here
The prebuilt model is general-purpose but negation handling is not domain-specific.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often assume Azure AI Language Service handles negation across all features, but key phrase extraction explicitly does not consider negation, unlike sentiment analysis which does.
Trap categories for this question
Keyword trap
Language detection is not the cause; the issue is with key phrase extraction.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language Service's key phrase extraction relies on a named entity recognition (NER) and phrase chunking pipeline that identifies noun phrases and important terms using part-of-speech tagging and statistical co-occurrence. Negation is not modeled in this pipeline; instead, it is treated as a separate linguistic construct typically handled by sentiment analysis or custom models. In real-world scenarios, this means that for customer reviews like 'The product is not good', the service might output 'product' and 'good' as key phrases, requiring post-processing or custom text normalization to handle negations.
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.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Implement knowledge mining and information extraction solutions — This question tests Implement knowledge mining and information extraction solutions — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Key phrase extraction does not consider negation — Key phrase extraction in Azure AI Language Service uses a statistical model that identifies significant terms based on frequency and context, but it does not inherently understand negation. When the phrase 'not good' appears, the model may still extract 'good' as a key phrase because it recognizes 'good' as a high-value term, ignoring the negation. This is a known limitation of the feature, as it focuses on noun phrases and important terms rather than sentiment or negated constructs.
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.
Are there clue words in this question I should notice?
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
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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