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 law firm receives hundreds of legal documents daily. They need to automatically extract key entities like names of parties, dates, jurisdictions, and also classify each document as 'contract', 'pleading', or 'memo'. Which combination of Azure AI Language features should they use?
Why wrong: Key phrase extraction identifies important phrases but does not classify documents into custom categories.
B
Entity recognition and custom text classification
Entity recognition extracts specific entities like party names and dates; custom text classifies documents into the required types.
C
Sentiment analysis and language detection
Why wrong: Sentiment analysis detects emotion, language detection identifies the language; neither extracts entities nor classifies by document type.
D
Summarization and conversation analysis
Why wrong: Summarization produces a summary, conversation analysis handles dialogue; both are unrelated to entity extraction or document classification.
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Entity recognition and custom text classification
Option B is correct because the law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.
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.
✗
Entity recognition and key phrase extraction
Why it's wrong here
Key phrase extraction identifies important phrases but does not classify documents into custom categories.
✓
Entity recognition and custom text classification
Why this is correct
Entity recognition extracts specific entities like party names and dates; custom text classifies documents into the required types.
Related concept
Read the scenario before looking for a memorised answer.
✗
Sentiment analysis and language detection
Why it's wrong here
Sentiment analysis detects emotion, language detection identifies the language; neither extracts entities nor classifies by document type.
✗
Summarization and conversation analysis
Why it's wrong here
Summarization produces a summary, conversation analysis handles dialogue; both are unrelated to entity extraction or document classification.
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 key phrases can replace entities, but key phrases are unstructured and not mapped to predefined categories like dates or jurisdictions.
Trap categories for this question
Keyword trap
Key phrase extraction identifies important phrases but does not classify documents into custom categories.
Detailed technical explanation
How to think about this question
Azure AI Language's entity recognition uses a pre-trained model that can extract named entities like Person, Date, and Organization, but for custom entities (e.g., 'jurisdiction'), you would need to use the custom entity recognition feature, which is part of the custom text classification workflow. Custom text classification trains a model on labeled examples using a multi-class or multi-label approach, enabling the system to assign documents to user-defined categories like 'contract' or 'pleading' with high accuracy. In practice, a law firm might also use optical character recognition (OCR) to digitize scanned documents before feeding them into these NLP pipelines.
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: Entity recognition and custom text classification — Option B is correct because the law firm needs both entity extraction (to identify parties, dates, jurisdictions) and document classification (contract, pleading, memo). Azure AI Language's prebuilt entity recognition handles the entity extraction, while custom text classification allows the firm to train a model to classify documents into their specific categories. This combination directly addresses both requirements without unnecessary features.
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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