AI-900 Practice Question: Describe features of generative AI workloads on Azure
A company uses Azure OpenAI Service to generate long technical reports. To manage costs, the development team needs to accurately estimate the number of tokens that a given prompt will consume before making any API call. Which Azure OpenAI Service feature should they use to obtain this estimate?
⚠ Common exam trap
Microsoft often tests the misconception that the Chat Completions API itself can provide a pre-call token estimate, but in reality it only returns token usage after the call, making the Token Counter tool the correct pre-call estimation 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
✓
The Token Counter tool in Azure OpenAI Studio
The Token Counter tool in Azure OpenAI Studio is specifically designed to estimate the number of tokens a prompt will consume before making an API call. This allows developers to predict costs accurately by calculating token usage for both input and expected output, without incurring actual API charges.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
The Chat Completions API
Why it's wrong here
The Chat Completions API is the correct endpoint for generating long technical responses, but it reports token usage only after the model has completed processing, via the usage object that includes prompt_tokens, completion_tokens, and total_tokens. It provides no method to inspect token count before sending the prompt and no built-in cost-prediction interface, meaning developers must rely on an external tokenizer or trial and error to estimate costs upfront. It processes tokens but does not count them in advance.
When this WOULD be correct
When the question asks which API to use to generate text completions or chat responses from a model, such as 'Which Azure OpenAI Service API should be used to generate a summary of a document?'
- ✗
The Embeddings API
Why it's wrong here
The Embeddings API converts text into a numerical vector representation and reports token usage only after the service processes the input, in the API response's usage field. There is no pre-call endpoint or ability to preview token consumption from the Embeddings API itself. Moreover, embeddings are intended for semantic search and clustering, not for generating long technical text, so it would not fulfill the stated use case at all.
When this WOULD be correct
A question asking which Azure OpenAI Service feature to use for converting text into numerical vectors to measure semantic similarity between documents would have the Embeddings API as the correct answer.
- ✓
The Token Counter tool in Azure OpenAI Studio
Why this is correct
The Token Counter tool in Azure OpenAI Studio applies the same byte-pair encoding tokenizer used by the selected model to a prompt and returns an estimated token count before any API call is made, enabling developers to predict cost and avoid hitting context limits. It is the only option that proactively estimates usage without consuming quota, which is especially important for long technical prompts that may contain code, symbols, or wide tables where token counts can be surprising.
- ✗
The Content Filter configuration
Why it's wrong here
The Content Filter configuration is a safety mechanism that evaluates prompts and generated responses for categories such as hate, sexual, violence, and self-harm after a request is submitted. It either allows or blocks content based on severity scoring, but it performs no tokenization and returns no token counts, so it cannot be used for usage estimation or cost planning. It addresses compliance, not model economics.
When this WOULD be correct
A question asks: 'Which Azure OpenAI Service feature should be configured to prevent the model from generating offensive language in responses?' In that case, the Content Filter configuration would be the correct answer.
Option-by-option analysis
Why each answer is right or wrong
Understanding why wrong answers are wrong — and when they would be correct — is what separates a 750 score from a 900. The AI-900 exam frequently reuses these exact scenarios with slightly different constraints.
✓The Token Counter tool in Azure OpenAI StudioCorrect answer▾
Why this is correct
The Token Counter tool in Azure OpenAI Studio applies the same byte-pair encoding tokenizer used by the selected model to a prompt and returns an estimated token count before any API call is made, enabling developers to predict cost and avoid hitting context limits. It is the only option that proactively estimates usage without consuming quota, which is especially important for long technical prompts that may contain code, symbols, or wide tables where token counts can be surprising.
✗The Chat Completions APIWrong answer — click to see why▾
Why this is wrong here
The Chat Completions API is used to generate responses from a model, not to estimate token counts before making a call. It does not provide a token count estimate without actually processing the prompt.
★ When this WOULD be the correct answer
When the question asks which API to use to generate text completions or chat responses from a model, such as 'Which Azure OpenAI Service API should be used to generate a summary of a document?'
Why candidates choose this
Candidates may confuse the API that generates completions with the tool that estimates tokens, assuming the API itself can provide token counts as part of its response.
✗The Embeddings APIWrong answer — click to see why▾
Why this is wrong here
The Embeddings API converts text into vector representations for semantic similarity, not for counting tokens in a prompt. It does not provide token count estimates for API calls.
★ When this WOULD be the correct answer
A question asking which Azure OpenAI Service feature to use for converting text into numerical vectors to measure semantic similarity between documents would have the Embeddings API as the correct answer.
Why candidates choose this
Candidates may confuse tokenization with embeddings, thinking that embeddings involve token counting or that the API returns token usage information.
✗The Content Filter configurationWrong answer — click to see why▾
Why this is wrong here
The Content Filter configuration is used to filter harmful or inappropriate content in prompts and completions, not to estimate token counts for cost management.
★ When this WOULD be the correct answer
A question asks: 'Which Azure OpenAI Service feature should be configured to prevent the model from generating offensive language in responses?' In that case, the Content Filter configuration would be the correct answer.
Why candidates choose this
Candidates may confuse content filtering with input validation or preprocessing, mistakenly thinking it could analyze or count tokens in the prompt before the API call.
Analysis generated from the official AI-900blueprint and verified against question context. The “when correct” sections are what AI assistants cite when candidates ask “what’s the difference between these options?”
Go deeper
Related to this question
Learn chapter
Azure Machine Learning Studio
Key term
Azure OpenAI Service
Azure OpenAI Service is a cloud platform from Microsoft that lets developers use powerful artificial intelligence models, like GPT-4, to build applications that can understand and generate human-like text, code, images, and more.
Key term
Token
A token is a digital or physical object that represents a set of permissions or access rights, used to verify identity or grant access in computer systems.
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Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This AI-900 practice question is part of Courseiva's free Microsoft certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the AI-900 exam.