- A
Key phrase extraction and Sentiment analysis
Key phrase extraction identifies the main topics, and sentiment analysis determines the overall sentiment. This combination directly meets the teacher's requirements.
- B
Entity recognition and Language detection
Why wrong: Entity recognition identifies named entities (people, places, etc.) which is not the same as identifying main topics. Language detection only identifies the language of the text, which is not needed here.
- C
Text summarization and Key phrase extraction
Why wrong: Text summarization produces a condensed version of the essay, not the sentiment. The teacher also needs sentiment analysis, which is missing here.
- D
Sentiment analysis and Entity recognition
Entity recognition identifies specific entities, not the main topics. The teacher also needs key phrase extraction to identify topics, which is missing here.
Quick Answer
The correct answer is key phrase extraction and sentiment analysis. Key phrase extraction identifies the main topics discussed in each essay by automatically extracting salient terms and phrases from the text, while sentiment analysis evaluates the overall emotional tone—positive, negative, or neutral—expressed by the writer. Together, these two prebuilt Azure AI Language features directly address the teacher’s goal of performing both key phrase extraction and sentiment analysis for essays, allowing for a comprehensive understanding of both content and emotion. On the AI-900 exam, this question tests your ability to match specific Azure AI Language capabilities to real-world scenarios, often appearing as a scenario-based multiple-choice item. A common trap is confusing key phrase extraction with entity recognition—remember that key phrases are multi-word topics, not named entities. For a quick memory tip, think “Topics and Tone”: key phrases give you the topics, sentiment gives you the tone.
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 language teacher uses Azure AI Language to automatically analyze hundreds of student essays. The teacher wants to identify the main topics discussed in each essay and also understand the overall sentiment (positive, negative, or neutral) expressed. Which two prebuilt Azure AI Language features should the teacher use together to accomplish this goal?
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 and Sentiment analysis
Key phrase extraction identifies the main topics discussed in each essay by extracting salient terms and phrases, while sentiment analysis determines the overall sentiment (positive, negative, or neutral) expressed in the text. Together, these two prebuilt Azure AI Language features directly address the teacher's goal of analyzing both topics and sentiment.
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.
- ✓
Key phrase extraction and Sentiment analysis
Why this is correct
Key phrase extraction identifies the main topics, and sentiment analysis determines the overall sentiment. This combination directly meets the teacher's requirements.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity recognition and Language detection
Why it's wrong here
Entity recognition identifies named entities (people, places, etc.) which is not the same as identifying main topics. Language detection only identifies the language of the text, which is not needed here.
- ✗
Text summarization and Key phrase extraction
Why it's wrong here
Text summarization produces a condensed version of the essay, not the sentiment. The teacher also needs sentiment analysis, which is missing here.
- ✓
Sentiment analysis and Entity recognition
Why this is correct
Entity recognition identifies specific entities, not the main topics. The teacher also needs key phrase extraction to identify topics, which is missing here.
Related concept
Read the scenario before looking for a memorised answer.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse entity recognition with key phrase extraction, mistakenly thinking that identifying named entities (like 'Azure') is the same as extracting the main topics (like 'cloud computing benefits'), when entity recognition focuses on specific categories rather than thematic content.
Detailed technical explanation
How to think about this question
Key phrase extraction uses a statistical model that scores and ranks phrases based on their relevance to the document's content, often leveraging TF-IDF and graph-based ranking algorithms. Sentiment analysis assigns a sentiment score between 0 and 1 for positive and negative, with a neutral score near 0.5, and can also provide sentence-level granularity. In practice, combining these features allows a teacher to not only see that an essay is about 'climate change' (key phrase) but also that the sentiment is 'negative' (sentiment score 0.2).
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 and Sentiment analysis — Key phrase extraction identifies the main topics discussed in each essay by extracting salient terms and phrases, while sentiment analysis determines the overall sentiment (positive, negative, or neutral) expressed in the text. Together, these two prebuilt Azure AI Language features directly address the teacher's goal of analyzing both topics and sentiment.
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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