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| Section | Objectives |
|---|---|
| Topic 1: Knowledge Mining and Information Retrieval | - Indexing and semantic search - RAG (Retrieval Augmented Generation) patterns - Azure AI Search configuration |
| Topic 2: Develop Generative AI Applications and Agents | - AI agents architecture
|
| Topic 3: Plan and Manage Azure AI Solutions | - Azure AI resource provisioning and configuration - Responsible AI principles and governance - Model selection and lifecycle management |
| Topic 4: Implement Computer Vision Solutions | - Image classification and object detection - OCR and document intelligence |
| Topic 5: Implement Natural Language Processing Solutions | - Language understanding and intent recognition - Translation and multilingual support - Text analytics and summarization |
The AI-103 practice test software provides you the assistance to self-assess your progress. It helps you analyze the gradual improvement and notice the shortcomings while preparing Microsoft AI-103 through this format. The ability to customize your Microsoft AI-103 Practice Test time and the types of Microsoft AI-103 questions will turn your preparation into an easy affair.
NEW QUESTION # 100
You have a Microsoft Foundry project that contains a model deployment.
You have an application that calls the deployment by using the Azure OpenAI v1 API and DefaultAzureCredential.
The developers at your company receive HTTP 403 errors when they send inference requests, even after running az login.
You need to ensure that the developers can perform model inference. The solution must follow the principle of least privilege.
Which role-based access control (RBAC) role should you assign to the developers?
Answer: D
Explanation:
To resolve the HTTP 403 Forbidden errors when making inference calls via the Azure OpenAI v1 API and DefaultAzureCredential, users must be assigned the Cognitive Services OpenAI User built-in Azure RBAC role.
Why This Happens
Running az login successfully authenticates the user with Microsoft Entra ID, but it does not grant data-plane access permissions. By default, standard control-plane roles (like Reader or Foundry User) only allow users to view project metadata or manage settings, not send prompts to the model deployment endpoint itself.
Recommended Role Definition
Role Name: Cognitive Services OpenAI User
Permissions Granted: This role provides the absolute minimum privileges required to execute chat completions, embeddings, and general inference tasks (Microsoft.CognitiveServices/accounts/OpenAI/deployments/search/action and Microsoft.CognitiveServices/accounts/OpenAI/deployments/causalLanguageModeling/action). It does not allow users to deploy new models, view access keys, or alter configurations Scope Placement: Assign this role to the users (or a Microsoft Entra ID Group) at the Azure OpenAI resource level or the Resource Group level containing your Microsoft Foundry infrastructure.
Reference:
https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/managed-identity
NEW QUESTION # 101
You have a Microsoft Foundry project that contains a workflow for a customer support triage process.
You have an Ask a question node that stores user responses in a local variable named Var01.
You need to create the following Power Fx expressions:
* An if/else condition expression that ensures that Var01 contains a value
* A Send message expression that returns the stored user response in uppercase How should you configure the expressions? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
If/else condition expression: Not(IsBlank(Local.Var01))
Send message expression: {Upper(Local.Var01)}
The correct if/else condition is Not(IsBlank(Local.Var01)) because the workflow must continue only when the Ask a question node has captured a nonblank value. In Power Fx, IsBlank checks whether a value is blank, while Not reverses that Boolean result; therefore, Not(IsBlank(Local.Var01)) evaluates to true only when Var01 contains user input. IsEmpty(Local.Var01) is not the correct choice because IsEmpty checks whether a table contains records, not whether a text variable has a value. The Power Fx formula reference distinguishes these functions by defining IsBlank for blank values and IsEmpty for empty tables.
The Send message expression must be {Upper(Local.Var01)}. Microsoft Foundry workflow guidance uses the same pattern for an Ask a question node saved as Var01, followed by a Send message action that outputs
{Upper(Local.Var01)}. The Local. prefix is required because the response is stored in a local workflow variable, and Upper() converts the stored text response to uppercase. Reference topics: Microsoft Foundry workflows, Power Fx expressions, local variables, if/else branching, and Send message actions.
NEW QUESTION # 102
In Microsoft Foundry, you use the Chat playground with the GPT-35 Turbo model. You have a prompt that contains the following code.
You need the model to create an explanation of the code. The solution must minimize costs. What should you do?
Answer: C
Explanation:
Add the natural-language comment // what does function F do? after the code. The comment gives GPT-35 Turbo a direct and unambiguous instruction to analyze and explain function F. Because it uses syntax that naturally belongs beside source code, the model can distinguish the program being analyzed from the requested task and generate an explanatory completion. Microsoft's prompt-engineering guidance recommends clearly stating the required outcome and using cues that direct the model toward the desired response.
This approach retains the existing GPT-35 Turbo deployment and adds only a small number of prompt tokens, satisfying the cost-minimization requirement. Changing to GPT-4-32k would be unnecessary because the requirement is a straightforward code-explanation task and does not indicate that a substantially larger context window is required.
Adding function F(explanation) resembles a new function declaration or invocation rather than an instruction to explain existing code. The model could interpret it as source code that must be completed. Setting temperature to 1 changes output randomness but does not tell the model to explain the function; it can also make the answer less consistent.
Study Guide alignment: Use prompt-engineering techniques, construct clear model instructions, tune generation behavior, and select an appropriate model based on quality and cost requirements.
NEW QUESTION # 103
You plan to deploy a containerized version of an Azure Al Language service that will be used for sentiment analysis.
You configure https: //contoso.cognitiveservices.azure, cow as the endpoint URI for the service.
You need to run the container on an Azure virtual machine by using Docker.
How should you complete the command? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
First selection:
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment
Second selection:
https://contoso.cognitiveservices.azure.com
Completed command:
docker run --rm -it -p 5000:5000 --memory 8g --cpus 1 \
mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment \
Eula=accept \
Billing=https://contoso.cognitiveservices.azure.com \
ApiKey=xxx
The image mcr.microsoft.com/azure-cognitive-services/textanalytics/sentiment is the official Microsoft Container Registry image for Azure AI Language sentiment analysis. The Billing argument must reference the endpoint of the provisioned Azure AI Language resource associated with the supplied API key. It is not the address used by applications to invoke the locally running container.
Azure
AI containers require three licensing and metering parameters: Eula=accept, a valid Billing endpoint, and ApiKey. Without all three values, the container does not start. The command maps host port 5000 to container port 5000, allocates 8 GB of memory and one CPU, creates an interactive terminal, and automatically removes the stopped container. Applications subsequently invoke the locally hosted service through an address such as http://
< vm-address > :5000. The Azure endpoint remains responsible for metering and authentication; analyzed customer text is processed by the container rather than sent to the billing endpoint.
Study Guide alignment: Implement text analysis solutions - perform sentiment analysis and opinion mining, deploy Azure Language containers, and configure container billing parameters.
NEW QUESTION # 104
You have an Azure subscription that has the following configurations:
* Subscription ID: 8d3591aa-96b8-4737-ad09-00f9bled35ad
* Tenant ID: 3edfe572-cb54-3ced-ael2-c5cl77f39al2
You plan to create a resource that will perform sentiment analysis and optical character recognition (OCR).
You need to use an HTTP request to create the resource in the subscription. The solution must use a single key and endpoint How should you complete the request? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Azure Resource Manager resource paths are scoped through the subscription , not the Microsoft Entra tenant.
Therefore, the URL must contain:
subscriptions/8d3591aa-96b8-4737-ad09-00f9b1ed35ad
The tenant ID identifies the Microsoft Entra directory but does not replace the subscription portion of an ARM resource path.
The appropriate resource provider is Microsoft.CognitiveServices . Azure AI service accounts, including multi-service Azure AI resources, are provisioned under the Microsoft.CognitiveServices/accounts resource type.
A multi-service resource is required because the solution needs both Language sentiment analysis and Vision OCR while exposing a consolidated Azure resource with common key/endpoint management. Microsoft.
ApiManagement manages API gateways, Microsoft.ContainerService manages Kubernetes services, and Microsoft.KeyVault manages keys and secrets rather than provisioning Azure AI capabilities.
A typical ARM resource path therefore contains:
/subscriptions/ < subscription-id > /resourceGroups/ < rg > /providers/Microsoft.CognitiveServices/accounts/
< name >
This aligns with the AI-103 requirement to design Azure infrastructure for AI apps and choose appropriate Foundry/Azure AI services . Microsoft Learn
NEW QUESTION # 105
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