- A
A. Text Analytics (prebuilt)
Text Analytics provides out-of-the-box capabilities including key phrase extraction and sentiment analysis, perfectly meeting both needs without custom training.
- B
B. Custom Text Classification
Why wrong: Custom Text Classification requires a labeled dataset and custom training. Since the scenario specifies using a prebuilt feature without custom training, this is not appropriate.
- C
C. Conversational Language Understanding
Why wrong: Conversational Language Understanding is used to build intents and entities for chatbots, not for analyzing sentiment or extracting key phrases from existing text.
- D
D. Question Answering
Why wrong: Question Answering is designed to extract answers from provided documents (like FAQs) and is not for sentiment or key phrase extraction.
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. This is a configuration task: choose the command set that satisfies every stated requirement. Small differences — like 'secret' vs 'password' or 'transport input ssh' vs 'all' — change whether the answer is correct. 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 customer support team wants to analyze chat transcripts to automatically extract the most frequently mentioned product issues and also determine whether each chat represents a positive, neutral, or negative customer experience. Which prebuilt Azure AI Language 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
A. Text Analytics (prebuilt)
The Text Analytics (prebuilt) feature in Azure AI Language provides pre-built capabilities for key phrase extraction (to identify frequently mentioned product issues) and sentiment analysis (to classify chats as positive, neutral, or negative). This matches the customer support team's requirements exactly without needing custom training or complex configuration.
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.
- ✓
A. Text Analytics (prebuilt)
Why this is correct
Text Analytics provides out-of-the-box capabilities including key phrase extraction and sentiment analysis, perfectly meeting both needs without custom training.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
B. Custom Text Classification
Why it's wrong here
Custom Text Classification requires a labeled dataset and custom training. Since the scenario specifies using a prebuilt feature without custom training, this is not appropriate.
- ✗
C. Conversational Language Understanding
Why it's wrong here
Conversational Language Understanding is used to build intents and entities for chatbots, not for analyzing sentiment or extracting key phrases from existing text.
- ✗
D. Question Answering
Why it's wrong here
Question Answering is designed to extract answers from provided documents (like FAQs) and is not for sentiment or key phrase extraction.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates often confuse 'prebuilt Text Analytics' with 'Conversational Language Understanding' because both deal with text, but CLU is for intent/entity extraction in dialog flows, not for key phrase extraction or sentiment analysis on static transcripts.
Trap categories for this question
Keyword trap
Conversational Language Understanding is used to build intents and entities for chatbots, not for analyzing sentiment or extracting key phrases from existing text.
Scenario analysis trap
Custom Text Classification requires a labeled dataset and custom training. Since the scenario specifies using a prebuilt feature without custom training, this is not appropriate.
Detailed technical explanation
How to think about this question
Under the hood, Text Analytics uses pre-trained transformer-based models (e.g., BERT variants) fine-tuned on large corpora for tasks like key phrase extraction and sentiment scoring. The sentiment analysis returns a continuous score from 0 to 1 for positive, neutral, and negative classes, which can be thresholded to classify each chat. In a real-world scenario, you could batch-process thousands of transcripts via the REST API, extracting top key phrases and average sentiment scores to generate a dashboard of trending issues and customer satisfaction trends.
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: A. Text Analytics (prebuilt) — The Text Analytics (prebuilt) feature in Azure AI Language provides pre-built capabilities for key phrase extraction (to identify frequently mentioned product issues) and sentiment analysis (to classify chats as positive, neutral, or negative). This matches the customer support team's requirements exactly without needing custom training or complex configuration.
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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