AI-102 Practice Question: Implement natural language processing solutions
A company uses Azure AI Language to analyze customer reviews. They need to detect sentiment, extract key phrases, and identify named entities. Which THREE capabilities should they combine?
⚠ Common exam trap
Microsoft often tests the distinction between core NLP capabilities (sentiment, NER, key phrase extraction) and advanced features like summarization or language detection, so candidates may mistakenly include abstractive summarization because it sounds like it could help analyze reviews, but it is not one of the three required capabilities.
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
✓
Named entity recognition (NER)
Sentiment analysis (E) is correct because it returns sentiment labels and confidence scores (positive, negative, neutral, mixed) for the review text, directly satisfying the requirement to detect sentiment. Key phrase extraction (D) is correct because it identifies the main talking points in unstructured text, which fulfills the requirement to extract key phrases. Named entity recognition (A) is correct because it detects and categorizes entities such as people, places, organizations, and dates, satisfying the requirement to identify named entities. Abstractive summarization (B) is not needed because it generates new condensed text rather than performing the three requested analyses, and language detection (C) is not required because the scenario does not ask to identify the review language.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
Named entity recognition (NER)
Why this is correct
Named entity recognition identifies and categorises entities such as people, places, organisations and dates within review text, directly satisfying the requirement to identify named entities. It is one of the three Azure AI Language capabilities the solution must combine alongside sentiment analysis and key phrase extraction.
- ✗
Abstractive summarization
Why it's wrong here
Abstractive summarization generates new condensed prose, whereas the scenario requires sentiment scores, key phrases and entity extraction. It is tempting because summarising reviews sounds related to analysing them, and it would be correct if the requirement were producing a short narrative digest of each review rather than structured outputs.
- ✗
Language detection
Why it's wrong here
Language detection identifies the text's language, not sentiment, key phrases or entities, so it adds nothing to the three required capabilities. It is tempting because multilingual reviews often need language identification first, and it would be correct if the pipeline had to route non-English text to the appropriate sentiment model.
- ✓
Key phrase extraction
Why this is correct
Key phrase extraction surfaces the main talking points in each review, satisfying the requirement to extract key phrases. Combined with sentiment analysis and named entity recognition, it forms the three-capability set Azure AI Language provides for this review analysis scenario.
- ✓
Sentiment analysis
Why this is correct
Sentiment analysis returns a positive, neutral, or negative score with confidence for each review, satisfying the requirement to detect sentiment. Combined with key phrase extraction and named entity recognition, it forms the three-capability pipeline the scenario demands for analysing customer reviews.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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