- A
Key phrase extraction and sentiment analysis
Key phrase extraction pulls out important talking points (complaints); sentiment analysis assigns a positive/negative/neutral score. Together they meet both needs.
- B
Entity recognition and text translation
Why wrong: Entity recognition finds names/locations, not complaints; text translation changes language, which is not needed for understanding complaints or sentiment.
- C
Language detection and summarization
Why wrong: Language detection identifies the language of the text; summarization provides a short version but does not extract key phrases or sentiment.
- D
PII detection and conversation analysis
Why wrong: PII detection finds sensitive data like phone numbers; conversation analysis is for understanding dialogue structure, not extracting complaints or sentiment.
Quick Answer
The correct combination is key phrase extraction and sentiment analysis. This is because the customer service team needs to identify common complaints from thousands of call transcripts, which requires pulling out recurring topics and main talking points—exactly what prebuilt key phrase extraction does—while also determining whether customer sentiment is positive, negative, or neutral, which is the job of sentiment analysis. On the AI-900 exam, this scenario tests your understanding of Azure AI Language’s prebuilt, no-code features; a common trap is confusing key phrase extraction with named entity recognition, but remember that key phrases capture general themes, not specific people or places. The search intent for prebuilt key phrase extraction and sentiment analysis for customer support transcripts is fully met here because both features work out-of-the-box without custom training. Memory tip: think “Phrases for problems, Sentiment for feelings.”
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 customer service team wants to analyze thousands of call transcripts to identify common complaints and understand whether customer sentiment is positive, negative, or neutral. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features 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 and sentiment analysis
Option A is correct because the customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.
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 pulls out important talking points (complaints); sentiment analysis assigns a positive/negative/neutral score. Together they meet both needs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Entity recognition and text translation
Why it's wrong here
Entity recognition finds names/locations, not complaints; text translation changes language, which is not needed for understanding complaints or sentiment.
- ✗
Language detection and summarization
Why it's wrong here
Language detection identifies the language of the text; summarization provides a short version but does not extract key phrases or sentiment.
- ✗
PII detection and conversation analysis
Why it's wrong here
PII detection finds sensitive data like phone numbers; conversation analysis is for understanding dialogue structure, not extracting complaints or sentiment.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse 'key phrase extraction' with 'entity recognition' or assume that 'conversation analysis' alone can extract complaints and sentiment, when in fact the correct combination requires two distinct prebuilt features that directly map to the two stated goals (identifying common complaints and understanding sentiment).
Trap categories for this question
Keyword trap
Language detection identifies the language of the text; summarization provides a short version but does not extract key phrases or sentiment.
Detailed technical explanation
How to think about this question
Under the hood, Azure AI Language's key phrase extraction uses a statistical model based on TF-IDF and graph-based ranking (similar to TextRank) to identify terms that are most representative of the document's content. Sentiment analysis uses a deep learning model trained on large corpora to assign a sentiment score between 0 (negative) and 1 (positive) at the document and sentence level, with an additional neutral label when confidence is low. In a real-world scenario, a call transcript might contain mixed sentiment (e.g., a customer frustrated about a billing issue but satisfied with the agent's help), and the prebuilt models handle this by scoring each sentence independently, allowing the team to pinpoint specific complaint segments.
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.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
- →
Describe features of Natural Language Processing workloads on Azure — study guide chapter
Learn the concepts, then practise the questions
- →
Describe features of Natural Language Processing workloads on Azure practice questions
Targeted practice on this topic area only
- →
All AI-900 questions
1,020 questions across all exam domains
- →
Microsoft Azure AI Fundamentals AI-900 study guide
Full concept coverage aligned to exam objectives
- →
AI-900 practice test guide
How to use practice tests most effectively before exam day
Related practice questions
Related AI-900 practice-question pages
Use these pages to review the topic behind this question. This is how one missed question becomes focused revision.
Describe Artificial Intelligence workloads and considerations practice questions
Practise AI-900 questions linked to Describe Artificial Intelligence workloads and considerations.
Describe fundamental principles of machine learning on Azure practice questions
Practise AI-900 questions linked to Describe fundamental principles of machine learning on Azure.
Describe features of computer vision workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of computer vision workloads on Azure.
Describe features of Natural Language Processing workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of Natural Language Processing workloads on Azure.
Describe features of generative AI workloads on Azure practice questions
Practise AI-900 questions linked to Describe features of generative AI workloads on Azure.
AI-900 fundamentals practice questions
Practise AI-900 questions linked to AI-900 fundamentals.
AI-900 scenario practice questions
Practise AI-900 questions linked to AI-900 scenario.
AI-900 troubleshooting practice questions
Practise AI-900 questions linked to AI-900 troubleshooting.
Practice this exam
Start a free AI-900 practice session
Short sessions build daily habit. Longer sessions build exam-day stamina. Try a timed session to simulate real conditions.
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 — Option A is correct because the customer service team needs to identify common complaints (which requires extracting key phrases from the call transcripts) and understand sentiment polarity (positive, negative, or neutral). Azure AI Language's prebuilt key phrase extraction returns the main talking points and recurring terms, while sentiment analysis assigns a sentiment score and labels per sentence or document. Both features are available out-of-the-box without any custom training, directly meeting the stated requirements.
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
Courseiva creates original exam-style practice questions with explanations and wrong-answer analysis. It does not publish real exam questions, exam dumps, or protected exam content. Learn why practice questions differ from exam dumps →
Same concept, more angles
4 more ways this is tested on AI-900
These questions test the same concept from different angles. Work through them to make sure you can recognise it however the exam phrases it.
Variation 1. A customer support team wants to analyze chat logs to automatically identify the most common reasons for customer complaints and track how customer sentiment changes throughout a conversation. They plan to use prebuilt Azure AI Language features without any custom training. Which combination of features should they use?
medium- ✓ A.Key phrase extraction and sentiment analysis
- B.Entity recognition and language detection
- C.Text summarization and question answering
- D.Conversational language understanding and personal identification
Why A: Key phrase extraction identifies the most common reasons for complaints by pulling out important terms from the chat logs, while sentiment analysis tracks how customer sentiment changes throughout a conversation by assigning positive, negative, or neutral scores per utterance. Both are prebuilt Azure AI Language features that require no custom training, making them the correct combination for this scenario.
Variation 2. 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?
medium- ✓ A.A. Text Analytics (prebuilt)
- B.B. Custom Text Classification
- C.C. Conversational Language Understanding
- D.D. Question Answering
Why A: 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.
Variation 3. A customer support team wants to automatically analyze incoming emails to (1) determine the overall emotional tone (e.g., frustrated, satisfied) and (2) identify specific key phrases that indicate the reason for contact (e.g., 'return item', 'refund policy'). Which two Azure AI Language features should they use? (Choose two.)
medium- ✓ A.Sentiment analysis
- ✓ B.Key phrase extraction
- C.Entity recognition
- D.Language detection
Why A: Sentiment analysis is the correct choice because it evaluates text to determine the overall emotional tone, such as frustration or satisfaction, by assigning a sentiment score (positive, negative, neutral, or mixed) at the document and sentence level. This directly addresses the requirement to analyze the emotional tone of incoming emails.
Variation 4. A customer service team wants to analyze chat transcripts to understand customer sentiment and identify the most frequently discussed topics. Which two Azure AI Language features should they combine to achieve this?
medium- ✓ A.Sentiment analysis and key phrase extraction
- ✓ B.Language detection and entity extraction
- C.Text summarization and question answering
- D.Named entity recognition and translation
Why A: Sentiment analysis evaluates the emotional tone (positive, negative, neutral) of chat transcripts to understand customer sentiment, while key phrase extraction identifies the most frequently discussed topics by pulling out important terms and phrases. Combining these two features directly addresses the requirement to both gauge sentiment and surface recurring topics from the text.
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.
Question Discussion
Share a tip, memory trick, or ask about the reasoning behind this question. Do not post real exam questions, leaked content, braindumps, or copyrighted exam material. Comments are moderated and may be removed without notice.
Sign in to join the discussion.