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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Topic 2: Implement agentic solutions | 20-25% | - Manage agent operations
|
| Topic 3: Plan and manage Azure AI solutions | 25-30% | - Plan Azure AI resources
|
| Topic 4: Implement generative AI solutions | 25-30% | - Optimize and evaluate models
|
| Topic 5: Implement computer vision solutions | 10-15% | - Analyze visual content
|
In today's world, the AI-103 certification exam has become increasingly popular, providing professionals with the opportunity to upskill and stay competitive in the tech industry. At TestSimulate, we understand the importance of obtaining the Microsoft AI-103 Certification in the Microsoft sector, where technological advancements constantly evolving.
NEW QUESTION # 90
You are creating an image-editing workflow in a Microsoft Foundry project.
The workflow must meet the following requirements:
* Ensure that background objects can be removed by applying a mask-based inpainting edit.
* Preserve the original lighting and style of the edited images.
* Use the built-in image editing controls, NOT a custom model.
You need to ensure that image edits apply exclusively inside the masked area.
How should you configure the workflow?
Answer: C
Explanation:
The correct configuration is D. Enable mask_inpainting and supply both the input image and a mask indicating which part of the image to modify . The requirement is not to generate a new image, but to edit a specific region of an existing image while preserving the surrounding lighting, composition, and style. Azure OpenAI image editing in Microsoft Foundry supports modifying existing images by submitting an input image plus a prompt. For masked edits, the mask explicitly defines the part of the image the model is allowed to change; Microsoft states that the mask parameter defines the area to edit and must match the input image dimensions.
text_to_image would create a new image from a prompt and cannot guarantee preservation of the original image. image_variation generates related variants rather than targeted removals. image_to_image with high strength can regenerate broader areas and may alter unrelated visual details. Mask-based inpainting is the built-in editing control that limits modification to the selected region. Reference topics: Azure OpenAI image editing, mask inpainting, image edit API, input image, mask parameter, and computer vision image generation workflows.
NEW QUESTION # 91
You use Sentiment Analysis results from customer surveys to calculate bonuses for customer-service staff.
What should you do to meet Microsoft responsible AI principles?
Answer: D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Add a human review and approval step before an AI-derived result affects an employee ' s financial outcome.
Sentiment analysis is probabilistic. It can be affected by ambiguity, sarcasm, domain-specific phrasing, language variation, and contextual information not present in the survey text. Using the model ' s output as the sole determinant of compensation could therefore produce unfair or insufficiently accountable results.
Human review provides an oversight mechanism in which the AI signal is treated as evidence rather than an unquestionable final decision. The reviewer can consider model confidence, additional performance information, relevant employment policies, and exceptional circumstances.
Including low-confidence predictions would increase uncertainty rather than mitigate it. Using information that should have been deleted creates data-governance and privacy concerns. Publishing raw customer surveys could expose customer information and does not improve responsible decision-making.
The principle is especially important for decisions with meaningful financial, employment, safety, or legal consequences.
The AI-103 Study Guide covers oversight modes, constraints, approval workflows, responsible AI instrumentation, and safety/governance controls , all of which support maintaining appropriate human accountability. Microsoft Learn
NEW QUESTION # 92
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 # 93
You have a Python application named App1 that integrates with a Microsoft Foundry project named Project1.
You need to ensure that App1 meets the following requirements:
* Authenticates by using a Microsoft Entra managed identity
* Sends prompts to a dYouloyed model by using the Azure OpenAI Responses API How should you complete the Python code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
credential = DefaultAzureCredential
response = openai_client.responses.create
The correct authentication class is DefaultAzureCredential because the requirement is Microsoft Entra-based authentication, and this credential supports managed identity in hosted Azure environments. The Microsoft Foundry quickstart for Python shows the project client being created with AIProjectClient (endpoint=PROJECT_ENDPOINT, credential=DefaultAzureCredential()), which enables the Foundry SDK to authenticate without using API keys or embedded secrets. The same guidance shows creating an OpenAI- compatible client from the project by calling project.get_openai_client().
The correct Responses API method is create because the application must send a new prompt to the deployed model and receive generated output. Microsoft's Foundry quickstart demonstrates the exact pattern: response
= openai.responses.create(model= " gpt-5-mini " , input= " ... " ), followed by reading response.output_text.
The retrieve operation is used to fetch an existing response, not submit a new inference request, and compact is not the correct method for generating a model response. AzureKeyCredential would violate the Microsoft Entra managed identity requirement, while ClientSecretCredential uses an application secret rather than managed identity. Reference topics: Microsoft Foundry SDK, AIProjectClient, Microsoft Entra authentication, DefaultAzureCredential, and Azure OpenAI Responses API.
NEW QUESTION # 94
You need Pipeline1 to fail if:
* responses include claims not supported by retrieved source content;
* retrieved source content does not align with the labeled expected context.
Which two built-in evaluators should you use?
Answer: A,D
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Groundedness to detect claims in the generated answer that are not supported by the retrieved source context. Groundedness is fundamental to RAG evaluation because it determines whether the model stays faithful to the evidence supplied to it rather than introducing unsupported information.
Use the Retrieval evaluator to measure whether the search/retrieval stage returns content that aligns with the expected grounding information in the labeled dataset. A failure at this stage means the generation model might receive incomplete or irrelevant evidence even if it behaves correctly.
These two evaluators therefore inspect distinct parts of the RAG pipeline:
* Retrieval evaluates the evidence selected by the retrieval system.
* Groundedness evaluates whether the generated answer is supported by that evidence.
Response Completeness evaluates whether an answer sufficiently covers expected information. Fluency measures linguistic quality, and Coherence assesses organization and logical readability. None directly covers the two specified failure modes.
This combination is appropriate for a CI/CD quality gate because retrieval regressions and unsupported generation can be detected separately before production deployment.
The AI-103 Study Guide explicitly covers evaluating models and apps for fabrications, relevance, quality, and safety , together with monitoring grounding quality. Microsoft Learn
NEW QUESTION # 95
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