AI-102 Practice Question: Implement natural language processing solutions
You need to use Azure AI Language to analyze customer feedback. Which THREE analysis types are available in the Text Analytics API?
⚠ Common exam trap
Azure often tests your ability to distinguish between Azure AI services, so the trap here is that candidates confuse the Text Analytics API with broader AI capabilities like image or speech processing, leading them to select options that belong to other services.
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
✓
Sentiment analysis
Sentiment analysis (C) is a core Text Analytics API feature that returns sentiment labels and confidence scores (positive, neutral, negative, mixed) for documents or sentences, making it valid for analyzing customer feedback. Entity recognition (D), specifically Named Entity Recognition (NER), is also available and identifies entities such as people, places, organizations, and dates in text. Key phrase extraction (E) is another supported Text Analytics capability that returns the main concepts or topics in a document, which is useful for summarizing customer feedback. Image captioning (A) belongs to Azure AI Vision, not Azure AI Language, and speech-to-text (B) is provided by Azure AI Speech, so neither is part of the Text Analytics API.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Image captioning
Why it's wrong here
Image captioning is a Computer Vision capability, not a Text Analytics analysis type, which processes textual input. It is tempting because feedback may include screenshots or photos, and image captioning would be correct when generating descriptions from visual content rather than analysing text.
- ✗
Speech-to-text
Why it's wrong here
Speech-to-text belongs to Azure AI Speech, not the Text Analytics API, which analyses written text only. It is tempting because customer feedback often arrives as call recordings, and speech transcription would be correct when converting audio input before subsequent language analysis.
- ✓
Sentiment analysis
Why this is correct
Sentiment analysis is a core Text Analytics capability, returning per-document and per-sentence scores between 0 and 1 for positive, neutral and negative tones. It satisfies the stem's requirement for available analysis types, alongside key phrase extraction and named entity recognition, which together form the three standard features.
- ✓
Entity recognition
Why this is correct
Entity recognition is a core Text Analytics capability, extracting named entities such as people, places, organisations and dates from unstructured feedback text. It satisfies the stem's requirement for an available analysis type, alongside sentiment analysis and key phrase extraction, forming the three valid options in the Text Analytics API.
- ✓
Key phrase extraction
Why this is correct
Key phrase extraction is a core Text Analytics capability, returning the main concepts in unstructured text so you can surface recurring themes across customer feedback. It satisfies the stem's requirement for an available analysis type, alongside sentiment analysis and named entity recognition, and is invoked through the Azure AI Language service.
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