The answer is the Whisper model. Whisper is the correct choice because it is the Azure OpenAI Service model specifically designed for speech-to-text transcription, handling multilingual audio recordings and supporting real-time conversion, unlike GPT-4 which is built for text generation. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your ability to distinguish between Azure AI services: Whisper is for audio processing, while GPT-4 handles language tasks. A common trap is confusing Whisper with a general-purpose language model, but remember that Whisper is optimized for converting spoken words into text across multiple languages. For a quick memory tip, think of Whisper as the model that “hears” and writes down what is said, making it the go-to for meeting transcription services.
AI-900 Practice Question: Describe features of generative AI workloads on Azure
This AI-900 practice question tests your understanding of describe features of generative ai 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 meeting transcription service needs to convert multilingual audio recordings into accurate text in real time. Which Azure OpenAI Service model is specifically designed for this task?
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Whisper
Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.
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.
✗
GPT-4
Why it's wrong here
GPT-4 is a large language model for text generation, not for speech-to-text transcription.
✗
DALL-E 2
Why it's wrong here
DALL-E 2 generates images from text descriptions, not audio transcription.
✓
Whisper
Why this is correct
Whisper is a model optimized for speech recognition and translation, capable of transcribing audio into text in multiple languages.
Related concept
Read the scenario before looking for a memorised answer.
✗
Codex
Why it's wrong here
Codex is a model specialized in generating code from natural language descriptions, not audio transcription.
Common exam traps
Common exam trap: answer the scenario, not the keyword
The trap here is that candidates may confuse GPT-4's general-purpose language capabilities with speech processing, assuming it can handle audio transcription, when in fact Whisper is the dedicated model for that task.
Detailed technical explanation
How to think about this question
Whisper is a transformer-based encoder-decoder model trained on a large dataset of multilingual audio, using a multitask objective that includes transcription, translation, and language identification. It processes audio as log-Mel spectrograms and outputs text tokens, enabling it to handle diverse accents, background noise, and multiple languages without requiring language-specific fine-tuning. In real-world scenarios, Whisper can be integrated with Azure Speech Services for streaming transcription, but it operates as a batch model in Azure OpenAI Service, meaning real-time use may require chunking audio into segments.
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.
Related glossary terms
Concepts from this question explained
These glossary pages explain the core terms tested in this AI-900 question in full detail.
Describe features of generative AI workloads on Azure — This question tests Describe features of generative AI workloads on Azure — Read the scenario before looking for a memorised answer..
What is the correct answer to this question?
The correct answer is: Whisper — Whisper is the Azure OpenAI Service model specifically designed for speech-to-text transcription, including multilingual audio recordings, and it supports real-time conversion. Unlike GPT-4, which is a large language model for text generation, Whisper is optimized for audio processing tasks such as transcription and translation. This makes it the correct choice for converting multilingual audio into accurate text in real time.
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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Question Discussion
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