Question 880 of 1,020

Describe features of Natural Language Processing workloads on Azure →mediumMultiple ChoiceObjective-mapped

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.

What is 'speaker diarisation' in Azure AI Speech and when is it used?

A
Translating spoken audio into the dialect of the speaker's home region
Why wrong: Dialect translation is a language adaptation task — diarisation identifies which speaker said which part of the audio.
B
Identifying and labelling which speaker said which portions of a multi-speaker audio recording
Diarisation segments audio by speaker — 'Speaker 1: ..., Speaker 2: ...' — enabling attribution in meetings, calls, and conversations.
C
Detecting when a speaker is lying based on vocal stress patterns
Why wrong: Deception detection from voice is not a reliable or offered capability — diarisation purely identifies speaker identity segments.
D
Counting how many unique speakers have interacted with an AI voice assistant over time
Why wrong: Usage analytics is a reporting function — diarisation is a real-time audio processing task for attributing speech segments to speakers.

Full breakdown with real-world context →

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

✓

Identifying and labelling which speaker said which portions of a multi-speaker audio recording

Speaker diarization is an Azure AI Speech feature that segments an audio recording by speaker identity, labeling each segment with a unique speaker tag (e.g., Speaker 1, Speaker 2). It is used in scenarios like meeting transcription, call center analytics, or any multi-speaker audio where distinguishing who spoke when is required. This directly matches option B's description of identifying and labeling which speaker said which portions of a multi-speaker recording.

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.

✗
Translating spoken audio into the dialect of the speaker's home region
Why it's wrong here
Dialect translation is a language adaptation task — diarisation identifies which speaker said which part of the audio.
✓
Identifying and labelling which speaker said which portions of a multi-speaker audio recording
Why this is correct
Diarisation segments audio by speaker — 'Speaker 1: ..., Speaker 2: ...' — enabling attribution in meetings, calls, and conversations.
Related concept
Read the scenario before looking for a memorised answer.
✗
Detecting when a speaker is lying based on vocal stress patterns
Why it's wrong here
Deception detection from voice is not a reliable or offered capability — diarisation purely identifies speaker identity segments.
✗
Counting how many unique speakers have interacted with an AI voice assistant over time
Why it's wrong here
Usage analytics is a reporting function — diarisation is a real-time audio processing task for attributing speech segments to speakers.

Common exam traps

Common exam trap: answer the scenario, not the keyword

The trap here is that candidates confuse speaker diarization with speaker recognition (identifying a specific known person) or with counting speakers over time, but diarization is purely about segmenting and labeling unknown speakers within a single audio file, not identifying or tracking them across sessions.

Detailed technical explanation

How to think about this question

Under the hood, Azure AI Speech's speaker diarization uses a combination of voice activity detection (VAD) to find speech segments, followed by speaker embedding extraction (e.g., using i-vectors or x-vectors) to create a unique voiceprint for each speaker, then clustering these embeddings to assign segments to distinct speakers. A subtle behavior is that the service can handle overlapping speech by using time-frequency masking, but it may still mislabel short utterances or speakers with very similar vocal characteristics. In a real-world scenario, a call center recording with three agents and a customer can be diarized to automatically generate a transcript with speaker labels, enabling downstream analytics like agent talk-to-listen ratio.

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 company's IT admin needs to give a contractor read-only access to production logs without sharing account credentials. Using role-based access control (RBAC) and temporary scoped permissions — not a permanent shared password — is the correct pattern. Questions like this test whether you can apply least-privilege access across cloud identity services.

What to study next

Got this wrong? Here's your next step.

Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.

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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: Identifying and labelling which speaker said which portions of a multi-speaker audio recording — Speaker diarization is an Azure AI Speech feature that segments an audio recording by speaker identity, labeling each segment with a unique speaker tag (e.g., Speaker 1, Speaker 2). It is used in scenarios like meeting transcription, call center analytics, or any multi-speaker audio where distinguishing who spoke when is required. This directly matches option B's description of identifying and labeling which speaker said which portions of a multi-speaker recording.

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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