Question 1mediummultiple choice
Full question →mediummultiple choiceObjective-mapped
A news agency receives thousands of articles daily from wire services. They want to automatically identify and tag mentions of people, organizations, and locations within each article to improve search and categorization. The agency has no labeled training data. Which built-in Azure AI Language feature should they use?
Answer choices
Why each option matters
Good practice is not just finding the correct option. The wrong answers often show the exact trap the exam wants you to fall into.
A
Distractor review
Sentiment Analysis
Sentiment Analysis determines the overall emotional tone of the text, such as positive, negative, or neutral. It does not extract named entities.
B
Distractor review
Key Phrase Extraction
Key Phrase Extraction returns a list of important words or phrases from the text but does not classify them into categories like person, organization, or location.
C
Best answer
Named Entity Recognition (NER)
NER automatically identifies and categorizes named entities such as persons, organizations, and locations. It requires no training data and is directly suitable for this task.
D
Distractor review
Language Detection
Language Detection identifies the natural language in which the text is written (e.g., English, French). It does not extract entities.
Common exam trap
Common exam trap: answer the scenario, not the keyword
Many certification questions include familiar terms but test a specific constraint. Read the exact wording before choosing an answer that is generally true but wrong for this case.
Technical deep dive
How to think about this question
This question should be treated as a scenario, not a definition check. Identify the problem, the constraint and the best action. Then compare each option against those facts.
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.
- Use explanations to understand the rule behind the answer.
TExam Day Tips
- Underline the problem statement mentally.
- 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.
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.
More questions from this exam
Keep practising from the same exam bank, or move into a focused topic page if this question exposed a weak area.
Question 1
A developer wants to build a virtual assistant that can understand user intents such as 'Book a flight' or 'Check weather' and extract relevant entities like destination and date. The developer has a small set of labeled example utterances. Which Azure AI Language feature should the developer use?
Question 2
A developer is building a customer support chatbot using Azure OpenAI. The chatbot should never reveal its system instructions or internal configuration. The developer wants to add a rule at the beginning of the conversation to prevent prompt injection attacks. Which technique should they use?
Question 3
A developer is using Azure OpenAI Service to generate product descriptions from technical specifications. The generated descriptions sometimes include plausible-sounding but incorrect details (hallucinations). The developer wants to ensure the model's responses are strictly based on the provided product data and does not add any external or invented information. Which approach should the developer use?
Question 4
A developer is using Azure OpenAI with GPT-4 to build a chatbot that answers legal questions based on a company's internal policy documents. The developer wants the model's responses to be maximally deterministic and factual, avoiding any creative or speculative language. Which parameter should the developer set to the lowest possible value in the API call?
Question 5
A developer is using Azure OpenAI to generate creative product descriptions. The outputs are often repetitive and lack variety. The developer wants to increase the diversity of the generated text while still keeping it coherent. Which parameter should the developer increase?
Question 6
A developer is using Azure OpenAI Service to generate product descriptions. They want the output to be highly focused and deterministic, with less randomness. Which parameter should they decrease?
FAQ
Questions learners often ask
What does this AI-900 question test?
Read the scenario before looking for a memorised answer.
What is the correct answer to this question?
The correct answer is: Named Entity Recognition (NER) — Named Entity Recognition (NER) is a pre-built feature that can identify and categorize entities such as persons, organizations, and locations from text without requiring any custom training. Sentiment Analysis evaluates overall sentiment (positive/negative/neutral). Key Phrase Extraction extracts important phrases but does not classify them into specific entity types. Language Detection identifies the language of the text.
What should I do if I get this AI-900 question wrong?
Then try more questions from the same exam bank and focus on understanding why the wrong options are tempting.
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