- A
A tool for counting the number of words and characters in a document
Why wrong: Word counting is basic document statistics — text analytics applies AI to extract meaningful insights like sentiment and key topics.
- B
AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text
Text analytics analyses text to surface sentiment, key phrases, entities, and language — turning unstructured text into actionable insights.
- C
A spreadsheet function for analysing numerical data in text cells
Why wrong: Spreadsheet functions are data tools — text analytics is an Azure AI service for natural language processing of textual content.
- D
Encrypting sensitive text data before storing it in the cloud
Why wrong: Encryption is a security feature — text analytics applies NLP to extract intelligence from unstructured text.
Quick Answer
The answer is that text analytics in Azure AI Language is an AI-powered service for extracting structured insights from unstructured text. This is correct because it leverages pre-built natural language processing models to automatically identify sentiment (positive, negative, or neutral), key phrases, named entities like people and organizations, and even detect the language of the input. On the Microsoft Azure AI Fundamentals AI-900 exam, this concept tests your understanding of how Azure AI Language turns raw text into actionable data without requiring custom machine learning training. A common trap is confusing text analytics with custom text classification or translation services—remember that text analytics focuses on pre-built extraction, not custom labeling. For a quick memory tip, think of the acronym S.K.E.L.: Sentiment, Key phrases, Entities, and Language detection—the four core outputs of this service.
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.
What is 'text analytics' in Azure AI Language?
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
AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text
Text analytics in Azure AI Language is an AI-powered service that extracts structured insights from unstructured text. It uses pre-built models to identify sentiment (positive/negative/neutral), key phrases, named entities (people, places, organizations), and language detection, enabling automated analysis of large volumes of text data.
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 tool for counting the number of words and characters in a document
Why it's wrong here
Word counting is basic document statistics — text analytics applies AI to extract meaningful insights like sentiment and key topics.
- ✓
AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text
Why this is correct
Text analytics analyses text to surface sentiment, key phrases, entities, and language — turning unstructured text into actionable insights.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
A spreadsheet function for analysing numerical data in text cells
Why it's wrong here
Spreadsheet functions are data tools — text analytics is an Azure AI service for natural language processing of textual content.
- ✗
Encrypting sensitive text data before storing it in the cloud
Why it's wrong here
Encryption is a security feature — text analytics applies NLP to extract intelligence from unstructured text.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse 'text analytics' with basic text processing (like word counting) or data protection, when the exam specifically tests understanding of AI-powered NLP features that extract meaning from unstructured text.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language uses transformer-based deep learning models (e.g., BERT variants) fine-tuned on large corpora to perform tasks like sentiment scoring (0 to 1 scale), entity linking to Wikipedia, and key phrase extraction via TF-IDF and neural ranking. A subtle behavior is that sentiment analysis can be affected by sarcasm or mixed sentiment, and the service provides both document-level and sentence-level scores for granularity. In a real-world scenario, a customer support team could use this to automatically categorize thousands of support tickets by sentiment and extract product names as entities to prioritize negative feedback.
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
Got this wrong? Here's your next step.
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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: AI-powered extraction of insights (sentiment, key phrases, entities) from unstructured text — Text analytics in Azure AI Language is an AI-powered service that extracts structured insights from unstructured text. It uses pre-built models to identify sentiment (positive/negative/neutral), key phrases, named entities (people, places, organizations), and language detection, enabling automated analysis of large volumes of text data.
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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