- A
PII detection and custom named entity recognition (NER)
PII detection automatically identifies personal information for redaction. Custom NER can be trained on annotated documents to extract specific legal entities like case numbers and judge names. This combination meets both needs.
- B
Prebuilt entity recognition and key phrase extraction
Why wrong: Prebuilt entity recognition extracts common entities (e.g., people, places) but may not capture specialized legal entities like case numbers. Key phrase extraction identifies important phrases but does not provide structured entity extraction suitable for redaction or custom entity types.
- C
Sentiment analysis and language detection
Why wrong: Sentiment analysis determines sentiment (positive/negative) and language detection identifies the language of the text. Neither helps with redaction or extraction of specific legal entities.
- D
PII detection only
Why wrong: PII detection covers personal information (names, addresses, SSNs) for redaction, but it does not extract legal-specific entities like case numbers or judge names. The firm also requires those custom extractions.
Quick Answer
The correct combination is PII detection and custom named entity recognition (NER). This is because the legal firm requires two distinct capabilities: prebuilt PII detection to automatically identify and redact common sensitive data like names, addresses, and social security numbers, and custom NER to extract domain-specific legal entities such as case numbers, judge names, and statute references from their small set of manually annotated documents. On the AI-900 exam, this question tests your understanding of how Azure AI Language offers both out-of-the-box and trainable text analytics features—a common trap is assuming prebuilt NER alone can handle legal jargon, but it cannot recognize custom entities without training. Remember the memory tip: “PII for privacy, custom NER for specialty”—prebuilt handles the standard sensitive fields, while custom NER learns your unique vocabulary from annotated examples.
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 automatically process thousands of court documents. The system must identify and redact sensitive personal information such as names, addresses, and social security numbers. Additionally, it must extract legal-specific entities like case numbers, judge names, and statute references. The firm has a small set of manually annotated documents with these legal entities. Which combination of Azure AI Language 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
PII detection and custom named entity recognition (NER)
Option A is correct because the firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.
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.
- ✓
PII detection and custom named entity recognition (NER)
Why this is correct
PII detection automatically identifies personal information for redaction. Custom NER can be trained on annotated documents to extract specific legal entities like case numbers and judge names. This combination meets both needs.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Prebuilt entity recognition and key phrase extraction
Why it's wrong here
Prebuilt entity recognition extracts common entities (e.g., people, places) but may not capture specialized legal entities like case numbers. Key phrase extraction identifies important phrases but does not provide structured entity extraction suitable for redaction or custom entity types.
- ✗
Sentiment analysis and language detection
Why it's wrong here
Sentiment analysis determines sentiment (positive/negative) and language detection identifies the language of the text. Neither helps with redaction or extraction of specific legal entities.
- ✗
PII detection only
Why it's wrong here
PII detection covers personal information (names, addresses, SSNs) for redaction, but it does not extract legal-specific entities like case numbers or judge names. The firm also requires those custom extractions.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may assume prebuilt entity recognition covers all entity types, but it lacks domain-specific entities, or they may think PII detection alone is sufficient, ignoring the need for custom extraction of legal terms.
Trap categories for this question
Keyword trap
Prebuilt entity recognition extracts common entities (e.g., people, places) but may not capture specialized legal entities like case numbers. Key phrase extraction identifies important phrases but does not provide structured entity extraction suitable for redaction or custom entity types.
Detailed technical explanation
How to think about this question
Azure AI Language's custom NER uses a transformer-based model that can be fine-tuned with as few as 50 annotated documents to recognize domain-specific entities, while the prebuilt PII detection leverages a separate model trained on global patterns for names, addresses, and SSNs. The two features can be chained in a pipeline: first run PII detection to flag and redact sensitive data, then apply custom NER to extract legal entities from the cleaned text. This approach avoids the need for a single monolithic model and allows each task to use the most appropriate prebuilt or custom capability.
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
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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: PII detection and custom named entity recognition (NER) — Option A is correct because the firm needs both PII detection to redact sensitive personal information and custom NER to extract legal-specific entities like case numbers and judge names from a small set of annotated documents. Azure AI Language provides a prebuilt PII detection feature for common sensitive data and a custom NER capability that can be trained on the firm's annotated documents to recognize domain-specific entities.
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
1 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 law firm needs to automatically detect and redact sensitive information such as names, addresses, and social security numbers from legal documents. Which Azure AI Language feature can detect these entities without custom training?
medium- A.Sentiment Analysis
- B.Key Phrase Extraction
- ✓ C.PII Detection
- D.Language Detection
Why C: PII Detection is the correct Azure AI Language feature because it is specifically designed to identify and redact sensitive personal information such as names, addresses, and social security numbers from text without requiring any custom training. This pre-built capability uses machine learning models to detect categories of personally identifiable information (PII) out of the box, making it ideal for compliance scenarios like legal document processing.
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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