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 legal firm needs to process thousands of contracts to automatically identify important terms such as dates, monetary amounts, names of parties, and legal citations. Which built-in feature of the Azure AI Language service is best suited for this task?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "best"
Why it matters: Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
Why wrong: Sentiment analysis determines the emotional polarity (positive, negative, neutral) of text, not specific entities like dates or monetary amounts.
B
B) Key Phrase Extraction
Why wrong: Key phrase extraction returns the most important points or topics in the text, but does not specifically extract structured fields like dates or money.
C
C) Entity Recognition
Correct. Entity recognition (NER) can identify and extract predefined entity types such as dates, monetary values, organizations, and more from text.
D
D) Language Detection
Why wrong: Language detection identifies the language of the text, not specific terms or entities within it.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
C) Entity Recognition
Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.
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) Sentiment Analysis
Why it's wrong here
Sentiment analysis determines the emotional polarity (positive, negative, neutral) of text, not specific entities like dates or monetary amounts.
✗
B) Key Phrase Extraction
Why it's wrong here
Key phrase extraction returns the most important points or topics in the text, but does not specifically extract structured fields like dates or money.
✓
C) Entity Recognition
Why this is correct
Correct. Entity recognition (NER) can identify and extract predefined entity types such as dates, monetary values, organizations, and more from text.
Clue confirmation
The clue word "best" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
✗
D) Language Detection
Why it's wrong here
Language detection identifies the language of the text, not specific terms or entities within it.
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 Recognition, assuming both extract 'important terms' — but Key Phrase Extraction lacks the predefined, structured categorization needed for specific data types like dates and monetary amounts.
Trap categories for this question
Keyword trap
Key phrase extraction returns the most important points or topics in the text, but does not specifically extract structured fields like dates or money.
Detailed technical explanation
How to think about this question
Azure's NER uses pre-trained machine learning models that can recognize up to 30+ entity categories, including specialized types like 'DateTime', 'Money', 'Person', 'Organization', and 'LegalCitation'. Under the hood, it leverages transformer-based models (e.g., BERT) fine-tuned on large annotated corpora to perform sequence labeling, where each token is assigned a BIO (Begin, Inside, Outside) tag for entity boundaries. In a real-world contract processing scenario, NER can be combined with custom entity extraction via the Custom NER feature to handle domain-specific terms like contract clauses or jurisdiction references.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
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: C) Entity Recognition — Entity Recognition (also called Named Entity Recognition, NER) is the correct choice because it is specifically designed to identify and categorize predefined entities such as dates, monetary amounts, person names, organizations, and legal citations from unstructured text. The Azure AI Language service's NER capability can automatically extract these important terms from thousands of contracts, making it the ideal built-in feature for this task.
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.
Are there clue words in this question I should notice?
Yes — watch for: "best". Signals that multiple options may be partially correct. Choose the option that most directly solves the exact problem described, not the one that sounds most complete.
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 →
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.
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.