Microsoft AI-901 Practice Exam Questions & Answers

Microsoft Foundry Agent Service documentation states that instructions define goals, constraints, and behavior for an agent. Therefore, instructions are used to guide how the generative AI model or agent should respond and behave.

Option A is correct because instructions can define constraints the model must follow.

Option B is correct because instructions can define the agent's role and behavior.

Options C, D, and E are incorrect because Azure region, model selection, and TPM allocation are configuration or deployment/resource settings, not purposes of prompt instructions.

For a vision-enabled model, Microsoft documentation shows that multimodal prompts can include both text and image content in the same user message content array. Azure OpenAI vision models accept multimodal image-and-text input and return natural language responses.

The correct request structure is a user message with a content array containing both items, for example:

{ 'role': 'user', 'content': [ {'type': 'input_text', 'text': 'What is in this image?'}, {'type': 'input_image', 'image_url': image_url} ] }

This avoids sending separate requests and is therefore the best choice for quickest response time.

A and C are incorrect because they send the text and image separately. B is incorrect because the image/question should be sent as a user message, while system messages are used for instructions and behavior.

Microsoft's Foundry Agent Service documentation states that tool_choice provides deterministic control over tool calling:

auto means the model decides whether to call tools. required means the model must call one or more tools. none means the model does not call tools.

Therefore:

A . auto = Incorrect, because the model may or may not call the Azure function. B . none = Incorrect, because this prevents tool/function calls. C . required = Correct, because it forces the agent to call a tool.

The Azure OpenAI function-calling documentation also confirms that tool_choice='auto' lets the model decide whether to call a function, while tool_choice='none' forces a user-facing response without a tool call.

Generating marketing email drafts from a short product description and target audience is a content generation task. This is a generative AI workload because the system creates new text based on the user's prompt.

B . computer vision is for interpreting images or video. C . text classification categorizes existing text, but does not draft new marketing emails. D . speech recognition converts spoken audio into text.

Therefore, the correct answer is A. generative AI.

The Microsoft responsible AI principle of inclusiveness means AI systems should be designed to empower and engage everyone, including people with different abilities, languages, and accessibility needs.

Therefore, designing the interface to support multiple languages and screen readers is an example of inclusiveness.

Why the other options are incorrect:

A . Provide explanations about how predictions are generated = Transparency C . Evaluate model outputs across demographic groups to reduce bias = Fairness D . Encrypt stored customer data and restrict access by using role-based controls = Privacy and security

To compare the costs of large language models in Microsoft Foundry portal, use the Model leaderboard.

Microsoft documentation states that the model leaderboard helps compare models across quality, safety, estimated cost, and throughput. It also supports trade-off charts and side-by-side model comparison for features, performance, and estimated cost.

Why the other options are incorrect:

A . Evaluator catalog is for selecting evaluators to measure model or application outputs, not comparing LLM costs. C . Compliance relates to governance and compliance, not model cost comparison. D . Tools provides Foundry tools, not benchmarked cost comparison across models.

The correct text analysis technique is Named Entity Recognition (NER).

Microsoft defines NER as a feature that identifies and categorizes entities in unstructured text, including people, places, and organizations.

Sentiment analysis detects positive, negative, or neutral opinion. Summarization creates shorter versions of text. Key phrase extraction identifies important phrases, but it does not specifically classify mentions as people, organizations, or locations.