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| Section | Weight | Objectives |
|---|---|---|
| Implement agentic solutions | 20-25% | - Build AI agents
|
| Implement computer vision solutions | 10-15% | - Analyze visual content
|
| Plan and manage Azure AI solutions | 25-30% | - Manage AI solution lifecycle
|
| Implement text analysis and information extraction solutions | 10-15% | - Analyze and extract information
|
| Implement generative AI solutions | 25-30% | - Develop generative AI applications
|
At Pass4sureCert, we are proud to offer you actual AI-103 exam questions in our Microsoft AI-103 practice exam material. This actual study material has been checked and approved by leading professionals in the field. A team of over 90,000 experts and professionals have collaborated to design the Developing AI Apps and Agents on Azure (AI-103) exam material, ensuring that you receive both theoretical knowledge and practical insights to excel in the Developing AI Apps and Agents on Azure exam.
NEW QUESTION # 108
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: D
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 # 109
A team wants to deploy a model from the Foundry catalogue with the fastest possible setup, pay only for the tokens they consume, and avoid provisioning any virtual machine (VM) quota. Which deployment option fits?
Answer: C
Explanation:
Serverless API deployment runs on Microsoft-managed infrastructure, bills per input and output token, and needs no VM quota, so it is the fastest path that matches token-based billing. It is the standard choice when you want pay-per-use economics without managing compute.
NEW QUESTION # 110
You are creating an agent workflow in a Microsoft Foundry project to support natural voice interactions.
The agent must receive continuous audio input, convert the input into text for reasoning, and then return spoken responses to a user. The workflow must meet the following requirements:
. Support turn-taking dynamics, where the agent begins to generate the speech output before the user finishes speaking.
. Operate with low latency to maintain a conversational experience.
You need to enable both speech to text and text to speech in a real-time agent interaction.
What should you do?
Answer: A
Explanation:
The correct answer is D. Use real-time speech to text for incoming audio and text to speech for agent responses . The workflow requires continuous audio input, low-latency transcription for reasoning, and spoken output back to the user. Azure Speech in Foundry Tools real-time speech to text is designed for immediate transcription from streaming audio, which satisfies the incoming-audio side of the interaction. Text to speech provides the outbound spoken response path after the agent generates its answer.
This pattern aligns with Microsoft's real-time voice-agent architecture. The Voice Live API overview explains that low-latency speech-to-speech systems integrate speech recognition, generative reasoning, and text-to-speech functionality to create natural voice experiences. It also identifies contact centers as a key scenario and highlights low perceived latency for end users. Embeddings do not decode audio into conversational speech. Batch transcription introduces file-oriented delay and is not suitable for turn-taking.
Speech translation is only appropriate when translating between languages and does not provide the required reasoning-plus-spoken-response loop. Reference topics: Azure Speech in Foundry Tools, real-time speech to text, text to speech, voice agents, low-latency interaction, and conversational turn-taking.
NEW QUESTION # 111
You have a Microsoft Foundry project that contains an agent. The agent uses two tools to perform the following actions:
* Use Azure AI Search to retrieve answers from a private product documentation index.
* Use the web search tool to retrieve public information on the internet.
You need to ensure that for a specific run, the agent deterministically retrieves information only from the internet. To what should you set tool_choice?
Answer: D
Explanation:
Set tool_choice to the named Grounding with Bing Search tool:
{ " type " : " bing_grounding " }
A named tool choice directs the model to invoke a specific tool rather than allowing it to select from all tools attached to the agent. Microsoft's SDK specification describes AgentsNamedToolChoice as the mechanism used to force the model to call a particular tool and lists bing_grounding and azure_ai_search as supported named tool types.
Grounding with Bing Search retrieves current public information from the internet and supplies the results to the model for response generation. Selecting bing_grounding therefore directs the run to the public web source rather than the private Azure AI Search index.
Setting tool_choice to " required " only establishes that a tool must be invoked; when multiple tools are available, it does not identify which one must be selected. " auto " delegates the decision entirely to the model. Selecting { " type " : " azure_ai_search " } would force retrieval from the private product- documentation index, which is the opposite of the requirement.
Study Guide alignment: configure agent tools, control tool selection, integrate web grounding, and connect agents to enterprise knowledge sources .
NEW QUESTION # 112
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: D
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 # 113
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