- A
PII Detection
Why wrong: Not relevant to product catalog entities.
- B
Key Phrase Extraction
Extracts key topics and terms from text.
- C
Summarization
Why wrong: Not required for entity extraction.
- D
Custom Entity Extraction
Enables extraction of product-specific entities.
Question 570 of 993
AI-102 Key Phrase Extraction Practice Question
This AI-102 practice question tests your understanding of plan and manage an azure ai solution. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. A key principle to apply: key Phrase Extraction. 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 designing an Azure AI solution that uses Azure AI Language to analyze customer support transcripts. The solution must identify key phrases, detect sentiment, and extract custom entities specific to your product catalog. Which two Azure AI Language features should you enable?
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
Key Phrase Extraction (Option B) is correct because it identifies the main points and important terms in customer support transcripts, such as 'refund request' or 'account issue,' which directly supports analyzing the content. Custom Entity Extraction (Option D) is correct because it allows you to define and extract domain-specific entities from your product catalog, such as product names or model numbers, using a trained custom entity extraction model. Together, these two features enable both general insight extraction and tailored, product-specific data extraction from the transcripts.
Key principle: Key Phrase Extraction
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
PII Detection
Why it's wrong here
Not relevant to product catalog entities.
- ✓
Key Phrase Extraction
Why this is correct
Extracts key topics and terms from text.
Related concept
Key Phrase Extraction
- ✗
Summarization
Why it's wrong here
Not required for entity extraction.
- ✓
Custom Entity Extraction
Why this is correct
Enables extraction of product-specific entities.
Related concept
Key Phrase Extraction
Common exam traps
Common exam trap: answer the scenario, not the keyword
Microsoft often tests the distinction between pre-built features (like Key Phrase Extraction) and custom features (like Custom Entity Extraction), and the trap here is that candidates may incorrectly choose Summarization or PII Detection because they sound relevant to 'analyzing transcripts,' but they do not fulfill the specific requirements of key phrase identification and custom entity extraction.
Detailed technical explanation
How to think about this question
Key Phrase Extraction uses a pre-trained natural language processing model to return a list of strings that represent the main talking points, leveraging TF-IDF and graph-based ranking algorithms. Custom Entity Extraction requires you to provide labeled training data and uses a custom Named Entity Recognition (NER) model trained via Azure AI Language's custom text classification pipeline, which can be deployed as a dedicated endpoint. In a real-world scenario, you might combine these to first extract general topics with Key Phrase Extraction and then run Custom Entity Extraction to pull out specific product SKUs or issue types that are unique to your catalog.
KKey Concepts to Remember
- Key Phrase Extraction
- Custom Entity Extraction
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
Key Phrase Extraction
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. Key Phrase Extraction 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.
Review key Phrase Extraction, then practise related AI-102 questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this AI-102 question test?
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Key Phrase Extraction.
What is the correct answer to this question?
The correct answer is: Key Phrase Extraction — Key Phrase Extraction (Option B) is correct because it identifies the main points and important terms in customer support transcripts, such as 'refund request' or 'account issue,' which directly supports analyzing the content. Custom Entity Extraction (Option D) is correct because it allows you to define and extract domain-specific entities from your product catalog, such as product names or model numbers, using a trained custom entity extraction model. Together, these two features enable both general insight extraction and tailored, product-specific data extraction from the transcripts.
What should I do if I get this AI-102 question wrong?
Review key Phrase Extraction, then practise related AI-102 questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Key Phrase Extraction
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Last reviewed: Jun 30, 2026
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