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| Section | Weight | Objectives |
|---|---|---|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Implement information extraction and knowledge mining | 10–15% | - Build knowledge bases and search solutions
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
| Implement text and speech analysis solutions | 10–15% | - Implement speech capabilities
|
>> Certified AI-103 Questions <<
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NEW QUESTION # 107
s a Microsoft Foundry resource. You need to build an app that will suggest product names from a given product description. Which Foundry model should you use?
Answer: A
Explanation:
Use GPT-4 because suggesting product names from a textual description is a natural-language generation task. The application can supply the product's characteristics, intended audience, tone, and naming constraints in a prompt, and GPT-4 can generate candidate names that reflect those requirements. GPT models interpret natural-language instructions and generate corresponding textual completions, making them appropriate for ideation, summarization, content drafting, and conversational applications.
The other model categories serve different modalities. DALL-E is an image-generation model that creates visual content from natural-language prompts; it is not intended primarily to produce product-name lists.
Whisper is a speech-recognition model used to transcribe audio into text. Embedding models convert text into numerical vectors for similarity search, clustering, classification, and retrieval scenarios; they do not generate human-readable product names. Microsoft's Foundry model documentation distinguishes chat and generative models from image-generation, audio, and embedding model families.
The app should therefore deploy GPT-4, construct a prompt containing the product description and naming criteria, and consume the generated textual response through the model inference endpoint.
Study Guide alignment: Select and deploy appropriate generative models, integrate deployed models into applications, and implement prompt-driven content-generation workflows.
NEW QUESTION # 108
You use Azure Custom Vision to build a classifier. After training is complete, which two metrics are available for review?
Answer: A,E
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Azure Custom Vision exposes precision and recall as primary metrics for evaluating trained model iterations.
Precision measures how many positive predictions are correct:
Precision = TP / (TP + FP)
A high precision value means that when the model predicts a class, it generates relatively few false positives.
Recall measures how many actual positive examples the model successfully identifies:
Recall = TP / (TP + FN)
A high recall value means that relatively few relevant examples are missed.
These metrics must be considered together. Increasing a prediction threshold can make the classifier more conservative, potentially improving precision while decreasing recall. Lowering the threshold often increases recall at the cost of additional false positives.
F-score can be calculated from precision and recall in general machine-learning practice, but it is not one of the two Custom Vision review metrics requested by the choices. Area under the curve and weighted accuracy likewise are not the standard selections represented in this question.
The AI-103 Study Guide requires understanding of computer-vision model implementation and operational evaluation when creating object/component-identification solutions. Microsoft Learn
NEW QUESTION # 109
You train an Azure Custom Vision object detection model to identify a company ' s products by using the Retail domain.
You plan to deploy the model as part of a mobile app for Android phones.
You need to prepare the model for deployment.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
Answer:
Explanation:
Explanation:
* Change the model domain to a compact domain.
* Retrain the model.
* Export the model.
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
A Custom Vision model can be packaged for local mobile inference only when it is trained by using an exportable compact domain .
The current Retail-domain model is intended for its hosted Custom Vision scenario and cannot simply be copied into an Android application. Therefore, first change the model/project domain to the appropriate compact object-detection domain, such as General (compact) where applicable.
Second, retrain the model . The existing Retail iteration was trained using a different underlying model
/domain configuration. Retraining creates a new compact iteration that is optimized for export and edge inference.
Third, export the compact model using an Android-compatible format supported by Custom Vision and the app ' s inference runtime. Depending on the available project/export options, this can involve formats such as TensorFlow/TensorFlow Lite or ONNX.
Testing is useful before production deployment but does not replace any of the three steps required to transform the hosted Retail model into an exportable mobile model.
The correct lifecycle is therefore:
change domain # retrain # export
Study Guide alignment: implement object/component detection and prepare vision solutions for appropriate local or mobile execution environments .
NEW QUESTION # 110
You have a Microsoft Foundry project that contains a customer support agent built by using the Foundry Agent Service.
The agent uploads user-provided screenshots to Azure Storage through a ticketing tool and receives a blob URL for additional reasoning.
You need to use image moderation during agent runs and prevent harmful content from being returned during runs. Azure Al Content Safety must access the images by using the blob URL. The solution must follow the principle of least privilege.
What should you configure for Content Safety? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
Guardrails: Select User input, Output, Tool response, and Tool call and set Action to Block.
Storage access: A system-assigned managed identity that is assigned the Storage Blob Data Contributor role The guardrail must be applied to User input, Output, Tool response, and Tool call with the action set to Block . Microsoft Foundry guardrails support four intervention points: user input, tool call, tool response, and output. This scenario includes user-provided screenshots, a ticketing tool that uploads images and returns blob URLs, and final agent responses. Applying blocking controls at all four points ensures harmful image-related content is inspected throughout the agent run and prevented from continuing or being returned to the user.
Microsoft's guardrails guidance also states that tool call and tool response controls are specifically required when harmful content can pass through agent tools.
For storage, configure the Azure AI Content Safety resource with a system-assigned managed identity and grant it Storage Blob Data Contributor on the storage account or container. The Content Safety image moderation quickstart states that images can be supplied by blob storage URL and that the Content Safety resource must be given storage access by enabling its system-assigned managed identity and assigning Storage Blob Data Contributor or Owner; Contributor is the least-privileged valid option shown. Reference topics: Foundry guardrails, agent intervention points, image moderation, managed identity, and Azure Storage RBAC.
NEW QUESTION # 111
You have a Microsoft Foundry project that contains a customer support application.
You create two evaluations named Run1 and Run2 that have the following configurations:
* Both include risk and safety metrics
* Both include the protected material evaluation
* Run1 includes harmful content metrics that use a medium seventy threshold
* Run2 includes harmful content metrics that use a high severity threshold You run both evaluations against a dataset named D61 and receive the following results:
* Content harm defect rate of Run1: 12%
* Content harm defect late of Run2.4%
* Protected material evaluation o ' Run1: 6%
* Protected material evaluation of Run2: 6%
You start a fine-tuning job by using 081. The job fails during automatic RAI checks for multiple content harm types.
You discover that the content filtering configuration is set to high severity.
for each of the following statements, select Yes if the statement is true. Otherwise select No.
NOTE: Each correct selection Is worth one point.
Answer:
Explanation:
Explanation:
The first statement is No because automatic Responsible AI checks applied to a fine-tuning dataset are separate from the runtime content-filter threshold. Changing the runtime filter from high to low does not correct unsafe or disallowed training examples that cause the fine-tuning safety evaluation to fail.
The second statement is Yes . A medium severity threshold evaluates or flags a broader set of content than a high-only threshold. Consequently, Run1 can legitimately report a larger defect rate-12%-while Run2 reports only 4%.
The third statement is also Yes . Protected-material evaluation is a separate evaluation dimension from the standard harmful-content severity categories. Because both runs use the same dataset and protected-material evaluator, an identical 6% result is consistent even though the harmful-content threshold changes.
The key distinction is between runtime harmful-content thresholds , protected-material detection , and automatic RAI checks for training/fine-tuning data . They are related responsible-AI controls but operate independently.
The AI-103 Study Guide covers safety filters, risk detection, safety evaluations, and responsible-AI instrumentation as separate operational controls. Microsoft Learn
NEW QUESTION # 112
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