Quick Answer
The answer is that speaker names are missing because the speaker identification model has not been trained with voice samples. Azure Video Indexer’s speaker identification feature relies on a supervised learning process: it must first ingest labeled audio samples of known speakers to learn their unique vocal patterns. Without this custom training, the service defaults to generic labels like “Speaker #1” or “Speaker #2,” as it cannot map voices to actual identities. On the AI-102 exam, this question tests your understanding of the distinction between automatic diarization (which separates speakers but cannot name them) and custom speaker identification (which requires pre-trained voice samples). A common trap is assuming that speaker names appear automatically from the audio alone, but the key is that the model needs explicit enrollment. Memory tip: think “No samples, no names”—if you haven’t fed the model voice samples with names, it can’t call anyone by name.
This AI-102 practice question tests your understanding of implement image and video processing solutions. The scenario asks you to isolate a root cause — eliminate options that address a different problem before choosing. 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 news organization uses Azure Video Indexer to generate transcripts of live broadcasts. They notice that the speaker names are not appearing in the transcript. What is the most likely cause?
Clue words in this question
Noticing these words before you look at the options changes how you read each choice.
Clue: "most likely"
Why it matters: Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
The video resolution is too low for OCR.
Why wrong: OCR extracts text, not speaker names.
The speaker identification model has not been trained with voice samples.
Speaker names require custom voice identification.
The video format is not supported.
Why wrong: Format support does not affect speaker identification.
The language is not set correctly.
Why wrong: Language detection affects transcription, not speaker names.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
The speaker identification model has not been trained with voice samples.
Speaker names are missing because Azure Video Indexer's speaker identification feature requires pre-trained voice samples to match speakers to their identities. Without a custom voice model trained on known speakers' audio, the service can only label speakers as 'Speaker #1', 'Speaker #2', etc., but cannot assign actual names. This is a supervised learning process where the model must be trained with labeled voice samples before it can recognize and name speakers.
Key principle: Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
The video resolution is too low for OCR.
Why it's wrong here
OCR extracts text, not speaker names.
The speaker identification model has not been trained with voice samples.
Why this is correct
Speaker names require custom voice identification.
Clue confirmation
The clue word "most likely" in the question point toward this answer.
Related concept
Read the scenario before looking for a memorised answer.
The video format is not supported.
Why it's wrong here
Format support does not affect speaker identification.
The language is not set correctly.
Why it's wrong here
Language detection affects transcription, not speaker names.
Common exam traps
The trap here is that candidates may confuse speaker identification with automatic diarization or assume that speaker names are automatically extracted from the video metadata, when in fact Azure Video Indexer requires explicit training of a custom Person Model with voice samples to assign names.
Detailed technical explanation
Under the hood, Azure Video Indexer uses a combination of diarization (separating audio streams by speaker) and a custom voice identification model. Diarization can label speakers as 'Speaker 1', 'Speaker 2', etc., but to map those labels to actual names (e.g., 'John Smith'), you must upload voice samples for each speaker and train a Person Model via the API or portal. This is a supervised machine learning process where the model learns unique voice embeddings; without this training, the system has no reference to associate a voice with a name.
Key takeaway
Answer the scenario, not the keyword: identify the specific constraint before choosing the most familiar-sounding option.
Real-world example
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
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
Implement image and video processing solutions — This question tests Implement image and video processing solutions — Read the scenario before looking for a memorised answer..
The correct answer is: The speaker identification model has not been trained with voice samples. — Speaker names are missing because Azure Video Indexer's speaker identification feature requires pre-trained voice samples to match speakers to their identities. Without a custom voice model trained on known speakers' audio, the service can only label speakers as 'Speaker #1', 'Speaker #2', etc., but cannot assign actual names. This is a supervised learning process where the model must be trained with labeled voice samples before it can recognize and name speakers.
Identify which exam domain this question belongs to, review the core concept, then practise similar questions from the same domain.
Yes — watch for: "most likely". Probability qualifier — the question wants the most probable cause or outcome, not a guaranteed one. Eliminate low-probability options.
Read the scenario before looking for a memorised answer.
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Last reviewed: Jun 11, 2026
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