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| Section | Weight | Objectives |
|---|---|---|
| Implement information extraction and knowledge mining | 10–15% | - Extract structured data from documents
|
| Implement generative AI and agentic solutions | 30–35% | - Design and implement intelligent agents
|
| Plan and manage Azure AI solutions | 25–30% | - Manage AI solution development lifecycle
|
| Implement text and speech analysis solutions | 10–15% | - Implement natural language processing
|
| Implement computer vision solutions | 10–15% | - Build multimodal solutions
|
Our AI-103 PDF format is user-friendly and accessible on any smart device, allowing applicants to study from anywhere at any time. We have included actual and updated Microsoft AI-103 Questions in this AI-103 Dumps PDF file. Our Developing AI Apps and Agents on Azure exam dumps PDF format is designed to help individuals acquire the knowledge necessary to succeed in the test.
NEW QUESTION # 31
You plan to build an app that will use the Azure AI DALL-E model.
You need to deploy the model.
Answer: C
Explanation:
Comprehensive and Detailed 150 to 250 words of Explanation From Azure AI Apps and Agents Developer/Course Guide/topics:
Use Microsoft Foundry and the Azure CLI . Microsoft Foundry provides the model discovery and deployment experience for supported Azure-hosted generative models, including image-generation models.
From Foundry, developers can select an available image-generation model, configure a deployment name and supported model version, and expose the model through an Azure endpoint. The Azure CLI can be used to automate the underlying resource and deployment configuration, making it appropriate for repeatable infrastructure and CI/CD workflows.
Azure Machine Learning Studio is not the primary deployment surface for Azure OpenAI DALL-E deployments. Microsoft Graph provides APIs for Microsoft 365 and identity-related workloads rather than deploying generative AI image models.
DALL-E is a text-to-image model, which means the eventual application submits a natural-language image- generation prompt and receives generated image content rather than performing classification or analysis of existing images.
The AI-103 Study Guide explicitly includes design and implementation of image-generation solutions , including generating images from prompts and reference media and selecting appropriate generation controls.
Microsoft Learn
NEW QUESTION # 32
You plan to configure an evaluation in Microsoft Foundry for a Retrieval Augmented Generation (RAG) chat app. You need to provide scores for groundedness, relevance, and harmful-content categories. Which two evaluation categories can you use? Each correct answer presents part of the solution.
Answer: D,E
Explanation:
Select AI quality (AI assisted) metrics for groundedness and relevance. Groundedness evaluates whether the generated answer is supported by the retrieved RAG context, while relevance evaluates whether the answer appropriately addresses the user's query. These evaluations use an AI judge model to assess meaning and contextual alignment rather than relying only on exact token matches. Microsoft Foundry categorizes groundedness and relevance as quality evaluators and requires a compatible judge-model deployment for AI- assisted evaluation.
Select risk and safety metrics to evaluate harmful-content categories. These evaluators detect and score content involving violence, sexual material, self-harm, and hate or unfairness. They provide the safety dimension required alongside the RAG quality measurements.
AI quality NLP metrics include mathematically calculated measures such as F1, BLEU, GLEU, ROUGE, and METEOR. These primarily compare token or n-gram overlap with reference answers and do not provide the requested groundedness and content-harm assessments. A fluency evaluator measures language readability, while a similarity evaluator compares a response with ground truth; neither is a complete evaluation category covering the requirements.
Study Guide alignment: evaluate generative AI solutions by using quality, groundedness, relevance, risk, and safety metrics .
NEW QUESTION # 33
You are building an app that will use Azure AI to monitor workspaces for safety. You need to recommend a service that meets the following requirements:
* Generates alerts when employees enter high-risk areas
* Monitors video feeds in real time
* Minimizes development effort
What should you recommend?
Answer: A
Explanation:
Azure Vision in Foundry Tools Spatial Analysis is designed to process real-time streaming video and analyze the presence, movement, and spatial relationships of people in physical environments. It provides predefined operations such as personcrossingpolygon, which can identify when a person enters or exits a configured zone. The resulting personZoneEnterExitEvent can be consumed by an application to trigger alerts when an employee enters an area designated as high risk.
Spatial Analysis minimizes development effort because it supplies pretrained people-detection and zone- monitoring capabilities. Developers configure camera streams, polygonal zones, and event parameters instead of collecting images, labeling objects, and training a custom model.
Image Analysis primarily evaluates individual images and does not maintain movement or zone state across a live stream. Azure AI Video Indexer extracts searchable insights from video or live-stream content, but it is not the specialized option for detecting physical zone-entry events. Custom Vision object detection would require training data, labeling, model training, deployment, and additional application logic to track movement.
The Study Guide explicitly associates Spatial Analysis with detecting the presence and movement of people in video under Implement computer vision solutions # Analyze videos .
NEW QUESTION # 34
Note: This section contains one or more sets of questions with the same scenario and problem. Each question presents a unique solution to the problem. You must determine whether the solution meets the stated goals. More than one solution in the set might solve the problem. It is also possible that none of the solutions in the set solve the problem.
After you answer a question in this section, you will NOT be able to return. As a result, these questions do not appear on the Review Screen.
You have a Microsoft Foundry project that contains an agent. The agent generates summaries from retrieved policy documents.
Users report that some responses omit required regulatory clauses, even when the clauses are present in the retrieved content.
You need to improve response completeness.
Solution: You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Does this meet the goal?
Answer: A
Explanation:
Correct:
* You add a reflection pass that regenerates the response if the required clauses are missing.
This is Self-Correction Strategy: A reflection pass allows an agent to evaluate its own initial output against specified constraints (e.g., checking for the presence of mandatory regulatory clauses). If the required text is missing, the agent triggers a programmatic self-correction or regeneration loop to include them before final delivery.
Incorrect:
* You increase the value of the max_tokens parameter.
Increasing the max_tokens parameter prevents the response from being cut off mid-sentence due to length constraints. However, it does not force the model's logic to explicitly include missing information that it chose to leave out earlier in the text.
* You increase the value of the temperature parameter.
Raising the temperature parameter increases randomness and creativity. For rigid compliance tasks like summarizing regulatory documents, higher temperature actually increases the risk of hallucination and omission.
* You run an evaluation flow that scores responses for completeness and blocks responses that fall below a defined threshold.
Evaluation Flow Block: Running an evaluation flow to score completeness and blocking bad responses identifies and stops low-quality outputs, but it does not fix or actively improve the response completeness. It simply filters failures out of the system.
Reference:
https://pub.towardsai.net/reflection-with-llm-how-to-make-ai-review-its-own-work-2db122fca1d8
NEW QUESTION # 35
You have an invoice-processing application named App1 that uses Azure Content Understanding in Foundry Tools. You are building a new Content Understanding pipeline named Pipeline1 that must meet the following requirements:
* Compare an invoice to its related purchase order.
* Validate the invoice against static vendor contract documents.
* Return a single structured output that includes discrepancy findings.
You need to configure Pipeline1 and expose the pipeline as a single analyzer endpoint. What should you configure?
Answer: B
Explanation:
Configure a multi-file task in pro mode . At analysis time, provide the invoice and its related purchase order as the input documents. During analyzer creation, add the static vendor contracts as reference data . Pro mode can reason across multiple input documents and use reference documents as contextual knowledge for validation, enrichment, and discrepancy detection. Microsoft specifically documents the scenario of supplying an invoice and purchase order as inputs while using contract files as reference data to identify inconsistencies.
Define a field schema containing the required invoice information and generated discrepancy findings.
Building the task creates an analyzer ID that applications invoke through one analyzer API endpoint, producing a unified structured result governed by that schema.
Standard mode is optimized for straightforward processing of individual files. It does not support cross-file analysis, reference-dataset integration, or the multi-step reasoning required to compare invoices, purchase orders, and contractual conditions. A "multi-file task in standard mode" is therefore not a supported configuration. Confidence scores do not address document comparison and, notably, are not available in pro mode.
Study Guide alignment: configure Content Understanding analyzers, design structured schemas, process multiple documents, integrate reference data, and implement document validation workflows .
NEW QUESTION # 36
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