The answer is the Token Counter tool in Azure OpenAI Studio. This tool is the correct choice because it allows developers to estimate the number of tokens a prompt will consume before making an API call, enabling accurate cost prediction for both input and expected output without incurring actual usage charges. On the Microsoft Azure AI Fundamentals AI-900 exam, this question tests your understanding of how to manage costs in Azure OpenAI Service by using built-in estimation features rather than relying on trial-and-error API calls. A common trap is confusing the Token Counter with the Content Filter or Deployments settings, but remember that only the Token Counter provides a pre-call estimate. For a quick memory tip, think of it as a "cost preview" button—just as you preview a document before printing, the Token Counter previews token usage before you commit to an API call.
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 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?
Why wrong: The Chat Completions API processes prompts and generates responses but does not provide an upfront token count estimation.
B
The Embeddings API
Why wrong: The Embeddings API converts text into vectors and consumes tokens, but it does not return a token estimate before the call.
C
The Token Counter tool in Azure OpenAI Studio
The Token Counter tool provides an accurate estimate of how many tokens a given prompt will use, allowing developers to predict costs before making an API call.
D
The Content Filter configuration
Why wrong: Content filters are designed to block harmful content and do not provide any token estimation functionality.
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.
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.
✗
The Chat Completions API
Why it's wrong here
The Chat Completions API processes prompts and generates responses but does not provide an upfront token count estimation.
✗
The Embeddings API
Why it's wrong here
The Embeddings API converts text into vectors and consumes tokens, but it does not return a token estimate before the call.
✓
The Token Counter tool in Azure OpenAI Studio
Why this is correct
The Token Counter tool provides an accurate estimate of how many tokens a given prompt will use, allowing developers to predict costs before making an API call.
Related concept
Read the scenario before looking for a memorised answer.
✗
The Content Filter configuration
Why it's wrong here
Content filters are designed to block harmful content and do not provide any token estimation functionality.
Common exam traps
Common exam trap: answer the scenario, not the keyword
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.
Detailed technical explanation
How to think about this question
The Token Counter tool uses the same tokenization algorithm (e.g., cl100k_base for GPT-4) as the underlying model to split text into tokens, including special tokens like <|im_start|> and <|im_end|> for chat formats. This ensures the estimate matches the actual token count the API will bill, which is critical because token limits (e.g., 4096 tokens for GPT-3.5-Turbo) can cause truncation or errors if exceeded. In real-world scenarios, developers use this tool to optimize prompt length and avoid costly overruns, especially when generating long technical reports.
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 startup's cloud architect reviews their monthly bill and notices costs are higher than expected for a long-running batch job. Switching from on-demand instances to Reserved Instances — or using Spot/Preemptible VMs — can reduce compute costs by up to 72 %. Questions like this test whether you understand the tradeoffs between commitment, flexibility, and cost across cloud pricing models.
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: 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.
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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