Free Microsoft AI-901 Practice Exam with Questions & Answers

Statement 1: Human-in-the-loop practices provide accountability for AI-generated decisions. = Yes

Microsoft responsible AI guidance recommends keeping a human in the loop, maintaining human oversight, and ensuring humans have a role in decisions based on model output.

Statement 2: Deploying an AI system to a production environment eliminates the need for ongoing monitoring. = No

Microsoft guidance states that after deployment, an AI-powered product or feature requires ongoing monitoring and improvement .

Statement 3: Disclosing the team that designed and deployed an AI system provides accountability for the system’s output. = Yes

Accountability in responsible AI means people and organizations remain responsible for AI systems and their effects. Identifying the people or team responsible for designing and deploying the system supports accountability and governance.

Statement 1: In the new Microsoft Foundry portal, you must fine-tune a model before you can deploy the model. = No

Fine-tuning is optional. Microsoft’s Foundry model deployment documentation describes deploying Foundry Models directly from the model catalog for inference. It does not require fine-tuning first.

Statement 2: In the new Microsoft Foundry portal, you can test a model from the model catalog only after you deploy the model. = No

Microsoft documentation states that some Foundry Tools are available to try via the model catalog without a project , and Foundry playgrounds are used for prototyping and validation before production. Therefore, the statement using “only after you deploy” is too restrictive.

Statement 3: In the new Microsoft Foundry portal, you can deploy a model from the model catalog only after retraining the model. = No

Retraining/fine-tuning is not required before deployment. Microsoft states that after you deploy a Foundry Model, you can interact with it in the Foundry Playground and use it from code, and the deployment workflow starts by selecting a model from the model catalog and choosing Deploy .

The Model catalog is used for comparing and deploying a wide range of models for generative AI development in Microsoft Foundry.

Microsoft documentation states that the model catalog in Foundry portal is the hub for discovering and using a wide range of models to build generative AI applications . It also includes many models across providers such as Azure OpenAI, Mistral, Meta, Cohere, NVIDIA, and Hugging Face.

The Microsoft Learn module for Foundry models also states that the model catalog is used to explore and filter models , compare models using benchmark metrics , and deploy a model to an endpoint .

Statement 1: Fairness can be achieved by focusing solely on improving the overall accuracy of an AI model. = No

Overall accuracy alone does not guarantee fairness. A model can have high overall accuracy while still producing biased or lower-quality outcomes for specific demographic groups. Fairness requires evaluating and reducing differences in model behavior across groups.

Statement 2: Evaluating AI system outputs to identify and reduce bias across demographic groups supports fairness. = Yes

This statement is correct. The Microsoft responsible AI principle of fairness means AI systems should treat people fairly and avoid unfair bias. Evaluating outputs across demographic groups helps identify and reduce bias.

Statement 3: Ensuring fairness for an AI system means that all users always receive the same output from the system. = No

Fairness does not mean identical outputs for all users. It means the system should avoid unfair bias and provide equitable treatment. Different users may receive different outputs when differences are relevant and appropriate.

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

The Microsoft responsible AI principle of transparency requires that AI systems be explainable to users .

Transparency means users should understand when they are interacting with AI, what the system can and cannot do, and how outputs or predictions are generated at an appropriate level.

The other options map to different responsible AI principles:

protect sensitive user data = Privacy and security

reduce bias in decisions = Fairness

require human oversight = Accountability

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.

In Microsoft Foundry Agent Service, tool_choice controls whether and how an agent uses tools during a run.

Statement 1: Yes

Setting tool_choice to auto lets the agent/model decide whether to call a tool. This statement is true.

Statement 2: No

Setting tool_choice to none does not mean the model decides whether to call a tool. It means the model will not call a tool. This statement is false.

Statement 3: Yes

Setting tool_choice to required means the agent must call one or more tools during the run. This statement is true.

Microsoft documentation describes the tool_choice values as follows: auto allows the model to decide whether to call tools, none prevents tool calls, and required forces one or more tool calls.

Azure Content Understanding uses transcription to convert audio content, such as voicemail recordings, into text. Microsoft’s Azure Content Understanding audio documentation states that transcription converts conversational audio into searchable and analyzable text-based transcripts.

Option A. Voice Live is not the feature used by Content Understanding to convert voicemail recordings into text.

Option B. key phrase extraction identifies important phrases after text is available; it is not the audio-to-text conversion step.

Option D. optical character recognition (OCR) is for extracting text from images or documents, not audio.

Therefore, the correct answer is C. transcription .

The completed code is:

poller = client.begin_analyze(

analyzer_id="invoice",

input_url=url

)

result = poller.result()

Azure Content Understanding analysis uses a long-running operation pattern. The Python SDK returns a poller from begin_analyze(), and Microsoft documentation states that the SDK poller handles polling automatically when you call .result() .

Therefore, to retrieve the analysis results after processing completes, the correct option is:

result

The other options are incorrect because status checks operation state, wait waits without returning the final analysis object, and get_results is not the method shown for retrieving the begin_analyze() result in this code pattern.