The correct combination is Named Entity Recognition and Key Phrase Extraction. Named Entity Recognition, or NER, directly addresses the need to extract entities and key phrases from contracts by identifying predefined categories like person names for parties, dates, and monetary amounts. Key Phrase Extraction then complements this by surfacing the main topics in each document, which allows the system to detect the presence of specific clauses such as non-compete or termination rights without requiring custom training. On the AI-900 exam, this scenario tests your understanding of how Azure AI Language pre-built features solve real-world text analytics tasks, often appearing as a scenario-based question where you must choose the right service pair. A common trap is selecting Entity Linking or Sentiment Analysis, which handle different problems like disambiguation or opinion mining. For a quick memory tip, think of NER as the “who, what, when, how much” tool and Key Phrase Extraction as the “what’s it about” tool—together they cover both the structured details and the thematic content of any contract.
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 analyze thousands of contracts to extract key information such as party names, dates, and monetary amounts. They also need to identify if certain clauses (e.g., non-compete, termination rights) are present. Which combination of Azure AI Language features should they use?
Named Entity Recognition and Key Phrase Extraction
NER extracts structured entities (names, dates, amounts) and Key Phrase Extraction surfaces important phrases that can represent clauses.
B
Entity Linking and Language Detection
Why wrong: Entity Linking connects entities to a knowledge base; Language Detection identifies language—neither extracts clauses or monetary amounts effectively.
C
Sentiment Analysis and Key Phrase Extraction
Why wrong: Sentiment Analysis determines positive/negative tone, not useful for extracting dates or amounts.
D
Text Analytics for Health and Entity Recognition
Why wrong: Text Analytics for Health is specialized for healthcare entities, not suitable for legal contracts.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Named Entity Recognition and Key Phrase Extraction
Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.
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.
✓
Named Entity Recognition and Key Phrase Extraction
Why this is correct
NER extracts structured entities (names, dates, amounts) and Key Phrase Extraction surfaces important phrases that can represent clauses.
Related concept
Read the scenario before looking for a memorised answer.
✗
Entity Linking and Language Detection
Why it's wrong here
Entity Linking connects entities to a knowledge base; Language Detection identifies language—neither extracts clauses or monetary amounts effectively.
✗
Sentiment Analysis and Key Phrase Extraction
Why it's wrong here
Sentiment Analysis determines positive/negative tone, not useful for extracting dates or amounts.
✗
Text Analytics for Health and Entity Recognition
Why it's wrong here
Text Analytics for Health is specialized for healthcare entities, not suitable for legal contracts.
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 Linking or Sentiment Analysis, mistakenly thinking that identifying clauses requires understanding sentiment or linking to external knowledge, rather than recognizing that Key Phrase Extraction directly surfaces the key topics and clauses present in the text.
Detailed technical explanation
How to think about this question
Under the hood, Azure's NER uses a pre-trained transformer model (e.g., BERT-based) fine-tuned on a large corpus to recognize entities like Person, Date, and Money with high precision. Key Phrase Extraction relies on a statistical model that identifies words and phrases with high term frequency-inverse document frequency (TF-IDF) scores relative to the document, effectively highlighting the most salient topics. In a real-world scenario, a legal firm could use NER to extract all dates from contracts and then use Key Phrase Extraction to flag documents containing phrases like 'non-compete agreement' or 'termination upon breach' for further review.
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: Named Entity Recognition and Key Phrase Extraction — Named Entity Recognition (NER) extracts predefined entities such as person names, dates, and monetary amounts from text, which directly addresses the need to identify party names, dates, and monetary amounts in contracts. Key Phrase Extraction identifies the main points or topics in a document, making it suitable for detecting the presence of specific clauses like non-compete or termination rights by surfacing the key phrases that represent those clauses.
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.
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Question Discussion
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