- A
Converting a speaker's voice to a different language in real time
Why wrong: Real-time voice translation is speech translation — diarization identifies and separates different speakers in audio.
- B
Identifying and labeling different speakers in a multi-speaker audio recording
Speaker diarization segments audio by speaker — enabling transcripts that attribute each spoken segment to the correct speaker.
- C
Measuring the speaking speed (words per minute) of each speaker
Why wrong: Speaking rate measurement is analytics — diarization identifies who is speaking at each moment.
- D
Filtering out background speakers from a primary speaker's recording
Why wrong: Background noise/speaker suppression is audio enhancement — diarization labels all speakers for transcription attribution.
Quick Answer
The correct answer is identifying and labeling different speakers in a multi-speaker audio recording. Speaker diarization in Azure AI Speech partitions an audio stream into homogeneous segments based on who is speaking, answering the question “who spoke when?” by assigning unique labels like Speaker 1 or Speaker 2 to each segment. On the AI-900 exam, this concept tests your understanding of Azure AI Speech’s built-in capabilities for meeting transcription and call center analytics, often appearing as a scenario where you must distinguish speaker diarization from speech-to-text or translation features. A common trap is confusing it with simple transcription—remember that diarization adds the critical layer of speaker identity. Memory tip: think “diarization = dialogue + identification,” where the service separates overlapping voices into labeled turns.
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 the Azure AI Speech service's 'speaker diarization' feature?
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 labeling different speakers in a multi-speaker audio recording
Speaker diarization is the process of partitioning an audio stream into homogeneous segments according to the speaker identity. It answers the question 'who spoke when?' by assigning a unique label (e.g., Speaker 1, Speaker 2) to each segment in a multi-speaker recording. This is a built-in capability of Azure AI Speech, often used in meeting transcription and call center analytics.
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.
- ✗
Converting a speaker's voice to a different language in real time
Why it's wrong here
Real-time voice translation is speech translation — diarization identifies and separates different speakers in audio.
- ✓
Identifying and labeling different speakers in a multi-speaker audio recording
Why this is correct
Speaker diarization segments audio by speaker — enabling transcripts that attribute each spoken segment to the correct speaker.
Related concept
Read the scenario before looking for a memorised answer.
- ✗
Measuring the speaking speed (words per minute) of each speaker
Why it's wrong here
Speaking rate measurement is analytics — diarization identifies who is speaking at each moment.
- ✗
Filtering out background speakers from a primary speaker's recording
Why it's wrong here
Background noise/speaker suppression is audio enhancement — diarization labels all speakers for transcription attribution.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates confuse speaker diarization with speaker recognition or voice cloning, assuming it involves translating or modifying the speaker's voice, rather than simply labeling who is speaking when.
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) and speaker embedding clustering. The service first segments the audio into short frames, extracts i-vector or d-vector embeddings for each segment, and then clusters them using algorithms like agglomerative hierarchical clustering (AHC) to assign speaker labels. In real-world scenarios, such as transcribing a board meeting with five participants, diarization enables the output to show 'John: Let's review the Q3 results' rather than a single undifferentiated transcript.
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
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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 labeling different speakers in a multi-speaker audio recording — Speaker diarization is the process of partitioning an audio stream into homogeneous segments according to the speaker identity. It answers the question 'who spoke when?' by assigning a unique label (e.g., Speaker 1, Speaker 2) to each segment in a multi-speaker recording. This is a built-in capability of Azure AI Speech, often used in meeting transcription and call center analytics.
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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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. What is 'speaker diarisation' in Azure AI Speech and when is it used?
medium- A.Translating spoken audio into the dialect of the speaker's home region
- ✓ B.Identifying and labelling which speaker said which portions of a multi-speaker audio recording
- C.Detecting when a speaker is lying based on vocal stress patterns
- D.Counting how many unique speakers have interacted with an AI voice assistant over time
Why B: 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.
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Last reviewed: Jun 11, 2026
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