AI-901 Practice Questions

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.

A system prompt, also called a system message, is used to guide how a generative AI model behaves, including its tone, role, response style, and output format. Microsoft states that system messages help steer an Azure OpenAI chat model toward the desired behavior, tone, and output format.

Statement 1: System prompts can be used to authorize users. = No

Authorization is handled by application identity, access control, authentication, and security configuration. A system prompt can guide model behavior, but it is not an authorization mechanism.

Statement 2: A system prompt is used to reduce tokens per minute (TPM). = No

TPM is a quota or throughput setting for model deployments. A system prompt does not reduce or configure TPM.

Statement 3: A system prompt guides the behavior of a generative AI model. = Yes

This is correct. Microsoft documentation says system messages help steer the model’s behavior, tone, and output format.

The correct answer is API endpoint . After a generative AI model is deployed in Microsoft Foundry, the deployment exposes an inference endpoint through which external applications can submit requests and receive generated responses. Microsoft Foundry documentation describes deployed models as being accessible programmatically through an endpoint and associated credentials. Applications invoke the relevant REST API or SDK against this endpoint and specify the deployed model as required by the API.

An embedding vector is a numerical representation of semantic information used for similarity search, retrieval, and grounding; it is not the network interface through which a deployed model is invoked. A training dataset contains data used to train or fine-tune a model and is not called when performing inference. A URL parameter can be part of an API request, but it does not represent the deployed model ' s callable interface.

This directly aligns with the AI-901 objective to deploy a model and interact with it in the Foundry portal and to create lightweight applications that invoke deployed generative AI models through supported APIs and SDKs.

Azure Text Analytics client library

Comprehensive and Detailed Explanation with all Azure AI documents : =

The completed sentence is:

To develop an application that analyzes text by using Azure Language in Foundry Tools, use the Azure Text Analytics client library package.

Azure AI Language text analysis features, such as sentiment analysis, named entity recognition, key phrase extraction, language detection, and text summarization, are accessed in code by using the Azure Text Analytics client library .

The other options are incorrect:

Azure AI Projects client library is used for Foundry project and agent-related workflows, not specifically Azure Language text analysis.

Azure OpenAI client library is used to call OpenAI model deployments, not Azure Language text analytics APIs.

Azure Speech SDK is used for speech-to-text and text-to-speech workloads, not text analysis.

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 correct parameter is analyzer_id . The Azure AI Content Understanding Python SDK uses the ContentUnderstandingClient.begin_analyze() method to submit content for processing. Microsoft defines the method signature with analyzer_id as the required identifier that specifies which analyzer should process the supplied input.

Therefore, the code should be completed as:

poller = client.begin_analyze(analyzer_id= " invoiceAnalyzer " , ...)

A custom analyzer encapsulates the configuration that determines how Content Understanding interprets the source content and which fields or structured information it extracts. Because the analyzer has already been created with the name invoiceAnalyzer , that value is supplied through analyzer_id when the analysis operation begins.

extraction_type is not the parameter used to select an analyzer. model_name would refer conceptually to an underlying AI model, but Content Understanding analysis is invoked through an analyzer abstraction rather than by specifying a model directly. Likewise, schema_id does not identify the analyzer to execute.

Microsoft ' s custom-analyzer guidance explicitly demonstrates client.begin_analyze(analyzer_id=analyzer_id, inputs=[...]) , confirming the required SDK pattern