最近の数年間で、IT領域の継続的な発展と成長に従って、AI-300認証試験はもうMicrosoft試験のマイルストーンになりました。MicrosoftのAI-300「Operationalizing Machine Learning and Generative AI Solutions」の認証試験はあなたがIT分野のプロフェッショナルになることにヘルプを差し上げます。MicrosoftのAI-300の試験問題を提供するウェブが何百ありますが、なぜ受験生は殆どTopexamを選んだのですか。それはTopexamにはIT領域のエリートたちが組み立てられた団体があります。その団体はMicrosoftのAI-300の認証試験の最新の資料に専攻して、あなたが気楽にMicrosoftのAI-300の認証試験に合格するためにがんばっています。Topexamは初めにMicrosoftのAI-300の認証試験を受けるあなたが一回で成功することを保証します。Topexamはいつまでもあなたのそばにいて、あなたと一緒に苦楽を共にするのです。
| Section | Weight | Objectives |
|---|---|---|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Design and implement an MLOps infrastructure | 15–20% | - Implement infrastructure as code for Machine Learning
|
| Implement machine learning model lifecycle and operations | 25–30% | - Register, version, and package models
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement generative AI quality assurance and observability | 10–15% | - Monitor generative AI systems
|
そんなに多くの人はMicrosoft AI-300試験に合格できるのに興味がわきますか。人に引けをとりたくないあなたはMicrosoft AI-300資格認定を取得したいですか。ここで、彼らはAI-300試験にうまく合格できる秘訣は我々社の提供する質高いMicrosoft AI-300問題集を利用したことだと教えます。弊社のMicrosoft AI-300問題集を通して復習してから、真実的に自分の能力の向上を感じ、AI-300資格認定を受け取ります。
質問 # 34
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.
An organization provisions Azure Machine Learning workspaces for development, test, and production environments.
Each environment must be deployed consistently and updated through source control. The deployment process must be automated, repeatable, and auditable.
You need to deploy Azure Machine Learning resources in a consistent and controlled manner.
Solution: Clone an existing Azure Machine Learning workspace to create additional environments.
Does the solution meet the goal?
正解：A
解説：
Correct:
* Define Azure Machine Learning resources in a Bicep template and deploy them within a GitHub Action.
This action is best because it fulfills all of your operational requirements:
Consistency & Controlled Manner:
Infrastructure as Code (IaC) via Bicep ensures that dev, test, and prod environments are configured identically, eliminating configuration drift.
Source Control: Storing the Bicep template in a Git repository satisfies the requirement that each environment must be updated through source control.
Automated & Repeatable: Orchestrating the deployment using GitHub Actions fully automates the workflow, allowing it to be executed reliably every time a change is merged.
Auditable: Git commit histories combined with GitHub deployment logs provide a comprehensive, compliant audit trail of exactly who modified the infrastructure and when.
Incorrect:
* Clone an existing Azure Machine Learning workspace to create additional environments.
* Create Azure Machine Learning workspaces manually in the Azure portal for each environment.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-github-actions-machine-learning
質問 # 35
You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters.
In previous model training and tuning runs, many models showed similar performance.
You need to select an early termination policy that meets the following requirements:
* Accounts for the performance of all previous runs when evaluating the current run.
* Avoids comparing the current run with only the best performing run to date.
Which two early termination policies should you use? Each correct answer presents part of the solution.
正解：A、B
解説：
Median stopping and Truncation selection both evaluate a run relative to the broader population of training runs rather than comparing it only against the single best run.
The Median Stopping Policy calculates running averages of the primary metric across all runs . At an evaluation interval, a run can be terminated when its best primary-metric performance is worse than the median of the running averages across the population. Microsoft explicitly defines this policy as being based on running averages of the primary metric of all runs.
The Truncation Selection Policy periodically ranks runs by their primary metric and terminates a configured percentage of the lowest-performing runs . For example, a 20-percent truncation policy terminates runs falling within the lowest 20 percent at the applicable evaluation interval. It therefore compares each candidate with the population of comparable runs rather than only the current leader.
Bandit is specifically unsuitable because it uses an allowable slack relative to the best-performing run , which directly violates the requirement. The Default behavior applies no early termination policy and allows runs to execute to completion.
Study Guide Reference: Implement machine learning model lifecycle and operations - automated hyperparameter tuning, early termination policies, Median Stopping Policy, Truncation Selection Policy, and efficient experiment execution.
質問 # 36
You use an Azure Machine Learning workspace.
You must monitor cost at the endpoint and deployment level.
You have a trained model that must be deployed as an online endpoint. Users must authenticate by using Microsoft Entra ID.
What should you do?
正解：D
質問 # 37
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 manage a Retrieval-Augmented Generation (RAG) application built on Microsoft Foundry.
The application retrieves documents from an indexed knowledge base and generates answers for internal users.
Recent feedback indicates that answers are fluent but sometimes include information that is not supported by the documents that were retrieved.
You need to evaluate whether a proposed change improves RAG answer quality by using supported and measurable techniques.
Solution: Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Does the solution meet the goal?
正解：A
解説：
Correct:
* Review user feedback comments collected after deployment to determine whether answers appear more accurate.
Relying solely on user feedback comments is not the proper immediate action for evaluating this specific issue but it is still the best choice.
Evaluate whether your proposed change improves the Retrieval-Augmented Generation (RAG) system by reducing hallucinations (unsupported information), you need to measure faithfulness and context relevance using automated, quantifiable metrics.
The proper course of action is to implement an LLM-as-a-Judge framework using an open-source evaluation library like Ragas or TruLens.
Recommended Evaluation Plan
Establish a baseline: Run your current RAG pipeline through a test dataset of 50-100 representative user queries.
Capture the outputs: Save the user query, the exact retrieved document snippets, and the generated response for every test.
Apply the change: Deploy your proposed modification (e.g., altered prompt, different temperature, or re-ranking algorithm).Run the evaluation: Pass the test dataset through the updated pipeline to generate a new set of responses.
Compare the metrics: Use the framework to score both sets of data and mathematically verify if the change reduced unsupported claims.
Incorrect:
* Compare embedding vector dimensions used by the retrieval pipeline before and after the change.
Comparing embedding vector dimensions is not a valid or effective method for measuring RAG answer quality. Vector dimensions (e.g., 1536 or 3072) are static architectural properties of your embedding model. They do not reflect factual accuracy, semantic grounding, or the rate of hallucinations in your text generation.
* Measure token throughput and average response latency before and after applying the proposed change.
Measuring token throughput and latency is not the correct action to solve this specific problem.
Reference:
https://www.getmaxim.ai/articles/how-to-evaluate-your-rag-system/
質問 # 38
You train and publish a machine teaming model.
You need to run a pipeline that retrains the model based on a trigger from an external system.
What should you configure?
正解：C
質問 # 39
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