- A
Text Analytics for Health
Why wrong: Incorrect. Text Analytics for Health is a specialized feature for extracting medical information from clinical and health-related text, not for general product review analysis.
- B
Key phrase extraction
Correct. Key phrase extraction automatically extracts the main concepts and important phrases from text, making it ideal for identifying frequently mentioned aspects in reviews.
- C
Entity linking
Why wrong: Incorrect. Entity linking identifies named entities in text and disambiguates them by linking to a knowledge base (like Wikipedia). It does not extract multi-word descriptive phrases like 'battery life'.
- D
Sentiment analysis
Why wrong: Incorrect. Sentiment analysis evaluates the emotional tone of text (positive, negative, neutral) but does not extract the specific topics or aspects being discussed.
AI-900 Practice Question: Describe features of Natural Language Processing workloads on Azure
This AI-900 practice question tests your understanding of describe features of natural language processing workloads on azure. 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.
A retail company wants to automatically analyze thousands of product reviews to identify the most frequently mentioned aspects, such as 'battery life', 'screen quality', and 'customer service'. They plan to use a prebuilt Azure AI Language feature without any custom training. Which feature should they use?
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 is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.
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.
- ✗
Text Analytics for Health
Why it's wrong here
Incorrect. Text Analytics for Health is a specialized feature for extracting medical information from clinical and health-related text, not for general product review analysis.
- ✓
Key phrase extraction
Why this is correct
Correct. Key phrase extraction automatically extracts the main concepts and important phrases from text, making it ideal for identifying frequently mentioned aspects in reviews.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity linking
Why it's wrong here
Incorrect. Entity linking identifies named entities in text and disambiguates them by linking to a knowledge base (like Wikipedia). It does not extract multi-word descriptive phrases like 'battery life'.
- ✗
Sentiment analysis
Why it's wrong here
Incorrect. Sentiment analysis evaluates the emotional tone of text (positive, negative, neutral) but does not extract the specific topics or aspects being discussed.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'key phrase extraction' with 'entity linking' or 'sentiment analysis', mistakenly thinking that identifying aspects requires linking to a knowledge base or analyzing sentiment, when in fact key phrase extraction is the direct and correct feature for surfacing frequently mentioned topics.
Trap categories for this question
Keyword trap
Incorrect. Entity linking identifies named entities in text and disambiguates them by linking to a knowledge base (like Wikipedia). It does not extract multi-word descriptive phrases like 'battery life'.
Detailed technical explanation
How to think about this question
Under the hood, key phrase extraction uses a statistical model based on term frequency-inverse document frequency (TF-IDF) and part-of-speech tagging to identify noun phrases that carry the most semantic weight. In a real-world scenario, this feature can process thousands of reviews in parallel via the Azure AI Language REST API, returning a ranked list of phrases that directly inform product improvement decisions.
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
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FAQ
Questions learners often ask
What does this AI-900 question test?
Describe features of Natural Language Processing workloads on Azure — This question tests Describe features of Natural Language Processing workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Key phrase extraction — Key phrase extraction is the correct choice because it is specifically designed to identify and extract the most important words or phrases from unstructured text, such as product reviews. This prebuilt Azure AI Language feature requires no custom training and directly surfaces frequently mentioned aspects like 'battery life' or 'screen quality' by analyzing term frequency and relevance.
What should I do if I get this AI-900 question wrong?
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
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: Jun 11, 2026
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.
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