試験に合格することは、Microsoft試験問題と試験スキルの知識に基づいています。 AI-300トレーニングクイズには、目的を同時に達成できる豊富なコンテンツがあります。 レビューでは、高効率のOperationalizing Machine Learning and Generative AI Solutions実践教材が重要な役割を果たすことがわかっています。 弊社の専門家も最新のコンテンツを収集し、試験のトレンドがどこに向かっているのか、実際にOperationalizing Machine Learning and Generative AI Solutions試験したいものを調査しています。 シラバスと新しいトレンドを分析することで、AI-300練習エンジンは、参考のためにこの試験に完全に一致しています。 したがって、MogiExamこの機会に取り組んでください。私たちの練習資料はあなたを失望させません。
| Section | Objectives |
|---|---|
| Topic 1: Implement generative AI quality assurance and observability | - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Conduct red teaming, adversarial testing, and content filtering |
| Topic 2: Implement machine learning model lifecycle and operations | - Monitor model performance, data drift, and operational health - Deploy models to real-time and batch endpoints - Retrain, update, and manage model versions in production - Train, register, and version models using Azure Machine Learning |
| Topic 3: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows - Set up Azure Machine Learning workspace and compute targets |
| Topic 4: Design and implement a GenAIOps infrastructure | - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Manage API keys, rate limits, and responsible AI guardrails - Configure prompt orchestration, prompt flows, and agent frameworks - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
| Topic 5: Optimize generative AI systems and model performance | - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Tune prompts, system messages, and grounding strategies |
AI-300の試験問題は頻繁に更新され、十分な数のテストバンクを取得して、理論と実践の傾向を追跡できることが保証されます。つまり、AI-300トレーニング資料は多くの利点を高め、AI-300ガイド急流をよりよく理解するためです。 AI-300実践ガイドを購入して、私たちMicrosoftを信頼してください。それでも私たちを完全に信じられない場合は、AI-300学習質問の機能と機能の紹介をお読みください。
質問 # 107
Drag and Drop Question
You have several machine learning models registered in an Azure Machine Learning workspace.
You must use the Fairlearn dashboard to assess fairness in a selected model.
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.
正解：
解説：
Explanation:
Step 1: Select a metric to measured
You need to choose a baseline performance or fairness metric (such as accuracy rate, precision, or demographic parity) along which the dashboard evaluates the model's overall behavior and cross-group disparities.
Step 2: Select a model feature to evaluated
You must specify a sensitive feature (such as age, gender, or race) to split the data into different subgroups and evaluate potential disparities across them.
Step 3: Select a binary classification or regression model
The Fairlearn package is specifically designed to assess and mitigate unfairness in binary classification and regression tasks. It does not natively support clustering or multiclass models for group fairness assessments in this wizard.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/concept-fairness-ml?view=azureml-api-2
質問 # 108
A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?
正解：A
解説：
The team cannot rely on live user traffic for evaluation because it would make results non-reproducible and could expose users to untested prompts. Synthetic data generation within Microsoft Foundry allows the team to produce diverse, representative input examples that simulate real user queries without any live traffic risk, giving a stable, consistent test set evaluated under identical conditions. Option B (content filters) and Option C (blocklists) are safety controls applied at inference time, not evaluation inputs. Option D (observability metrics) is a monitoring capability for production systems. With synthetic data, the team can construct edge cases, varied phrasings, and domain-specific scenarios that would take months to accumulate organically from real users, directly enabling the controlled evaluation the question requires without any dependency on live traffic.
Microsoft Learn Reference Topic: Evaluate generative AI apps with synthetic data - Microsoft Foundry prompt evaluation
質問 # 109
A real-time endpoint is deployed in Azure Machine Learning to serve predictions to a web application.
Users report intermittent failures and unexpected responses when calling the endpoint.
You need to identify the appropriate troubleshooting action for each reported issue.
Which troubleshooting action should you perform for each issue? To answer, move the appropriate troubleshooting actions to the correct issues. You may use each troubleshooting action once, more than once, or not at all. You may need to move the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
For deployment failures when containers do not start, review deployment logs in Azure ML Studio or use the CLI get-logs command to surface dependency installation errors, missing files, or scoring script import failures. For authentication errors such as 401 or 403 responses, check that client applications are using valid authentication keys or bearer tokens, as mismatched or expired credentials cause auth failures. For performance degradation and slow responses, review compute instance scaling configuration, since a surge in traffic saturates a fixed number of replicas causing queuing and high latency without autoscaling. For internal server errors such as HTTP 500, these typically originate in the scoring script, so inspect the init and run functions for unhandled exceptions and verify the environment has all required packages installed.
Microsoft Learn Reference Topic: Troubleshoot online endpoint deployment failures - Azure Machine Learning
質問 # 110
Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.
After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear on the review screen.
You work in Microsoft Foundry with a prompt flow.
You must manually evaluate prompts and compare results across prompt variants.
You need to capture the inputs, outputs, token usage, and latencies for each flow run for the evaluation.
Solution: Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Does the solution meet the goal?
正解：A
解説：
Enabling tracing via the prompt flow SDK alone does not guarantee that all four required dimensions - inputs, outputs, token usage, and latencies - are captured and surfaced in the Microsoft Foundry portal ' s trace viewer in a way that allows manual comparison across runs. While the prompt flow SDK does support tracing configuration, the portal-based Tracing activation is the complete, supported path for this specific capture requirement. The correct and complete solution as confirmed by the next question in this series is to use the Tracing toggle directly in the Microsoft Foundry portal for the project, then execute test runs. This portal-side Tracing feature is purpose-built to instrument all four dimensions and store them in a queryable trace store accessible through the Foundry interface.
Microsoft Learn Reference Topic: Enable tracing for prompt flows in Microsoft Foundry portal - Capturing run telemetry
質問 # 111
A data science team trains a classification model that predicts loan approval outcomes.
Before registering the model, the team must ensure the following:
Predictions must not disproportionately impact protected groups.
Prediction errors can be evaluated across different data segments.
You need to assess whether the model meets Responsible AI expectations.
Which two approaches should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. Choose two .
正解：D、E
解説：
A loan approval model must be assessed for both transparency and demographic fairness before registration.
Option D (feature importance) addresses prediction transparency - using SHAP or Azure ML ' s Responsible AI dashboard, the team can see which features drive individual predictions, revealing whether proxy variables for protected attributes are influencing decisions inappropriately. Option E (error rates across demographic cohorts) directly measures whether the model makes more mistakes for specific groups - a core fairness metric required under Responsible AI principles. Option A (global error rates) masks disparities since you need cohort-level analysis. Option B (endpoint latency) is a performance engineering concern. Option C (inference schema compatibility) is a technical integration check. The Azure ML Responsible AI dashboard combines both error analysis and feature importance in a unified interface designed for exactly this type of pre-deployment fairness assessment.
Microsoft Learn Reference Topic: Responsible AI dashboard in Azure Machine Learning - Error analysis and model interpretability
質問 # 112
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AI-300参考書内容: https://www.mogiexam.com/AI-300-exam.html