Quick Answer
The correct answer is to enable Key Phrase Extraction and Custom Entity Extraction. Key Phrase Extraction identifies the most important terms and main points in your customer support transcripts, such as “refund request” or “account issue,” providing a high-level summary of the conversation’s focus. Custom Entity Extraction goes further by allowing you to train a model to recognize domain-specific items from your product catalog, like exact product names or model numbers, making the analysis tailored and actionable. On the Microsoft Azure AI Engineer Associate AI-102 exam, this combination tests your understanding of how to pair a general, prebuilt feature with a custom, trained model to solve a real-world business problem—a common scenario in the “Analyze Text” section. A frequent trap is choosing Sentiment Analysis instead of Custom Entity Extraction, but remember that sentiment detects emotion, not specific catalog items. Memory tip: think “Key for the gist, Custom for the list.”
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. 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 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?
PII Detection
Why wrong: Not relevant to product catalog entities.
Key Phrase Extraction
Extracts key topics and terms from text.
Summarization
Why wrong: Not required for entity extraction.
Custom Entity Extraction
Enables extraction of product-specific entities.
Answer choices
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: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
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
Read the scenario before looking for a memorised answer.
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
Read the scenario before looking for a memorised answer.
Common exam traps
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
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.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
Plan and manage an Azure AI solution — This question tests Plan and manage an Azure AI solution — Read the scenario before looking for a memorised answer..
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.
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Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 30, 2026
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