Pass4Test의 Microsoft인증 AI-300시험덤프자료는 IT인사들의 많은 찬양을 받아왔습니다.이는Pass4Test의 Microsoft인증 AI-300덤프가 신뢰성을 다시 한번 인증해주는것입니다. Microsoft인증 AI-300시험덤프의 인기는 이 시험과목이 얼마나 중요한지를 증명해줍니다. Pass4Test의 Microsoft인증 AI-300덤프로 이 중요한 IT인증시험을 준비하시면 우수한 성적으로 시험을 통과하여 인정받는 IT전문가로 될것입니다.
| Section | Objectives |
|---|---|
| Operationalizing machine learning solutions | - ML lifecycle management
|
| Plan and design AI solutions using Azure AI services | - Requirements gathering and solution architecture
|
| Design and implement generative AI solutions | - Large language model integration
|
| Implement secure and scalable AI systems | - Security and governance
|
많은 분들이Microsoft AI-300시험을 패스하려고 하는데 시험대비방법을 찾지 못하고 계십니다. Microsoft AI-300덤프를 구매하려면 먼저Microsoft AI-300샘플문제를 다운받아 덤프품질을 검증후 주문하시면 믿음이 생길것입니다. Microsoft AI-300시험대비덤프는 IT업계에 오랜 시간동안 종사한 전문가들의 노하우로 연구해낸 최고의 자료입니다.
질문 # 83
A team manages prompts that are used by a generative AI application built on Microsoft Foundry. Multiple developers contribute prompt updates, and changes must be reviewed and tracked over time.
The team requires that:
Prompt changes are reviewed before being applied to the version in production.
Previous prompt versions can be restored if issues occur.
Prompt updates follow the same governance practices as the application code.
You need to implement a controlled process for managing and updating prompts in production.
How should you manage prompt updates to meet the requirements? To answer, move the appropriate actions to the correct requirements. You may use each 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.
정답：
설명：
Explanation:
All three requirements point to Git as the underlying mechanism, but each uses a different Git capability. For reviewing changes before production: a branch-based workflow where prompt changes are made on feature branches and merged to the main branch only after pull request approval enforces the review gate. For restoring previous versions: Git ' s commit history and tag system provide a precise, immutable record of every prompt state, and a git revert or checkout to a specific commit SHA restores any prior version instantly.
For governance parity with application code: by storing prompts in the same Git repository as application code, all the same CI/CD, branch protection, code review, and audit trail policies apply automatically. The alternatives such as Blob Storage or embedded configuration files lack native review workflows, branch protection, and full audit history.
Microsoft Learn Reference Topic: Prompt management and versioning with Git integration in Microsoft Foundry
질문 # 84
Hotspot Question
You monitor an Azure Machine Learning classification training experiment named train_classification on Azure Notebooks.
You must store a table named table as an artifact in Azure Machine Learning Studio during model training.
You need to collect and list the metrics by using MLflow.
How should you complete the code segment? To answer, select the appropriate option in the answer area.
NOTE: Each correct selection is worth one point.
정답：
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Explanation:
Box 1: log_metrics
Log Metrics dictionary
The dictionary row1 contains key-value pairs representing metrics. The mlflow.log_metrics() function is used to log multiple metrics simultaneously.
Box 2: log_artifact
Save Table Artifact
The code writes a JSON file locally and needs to upload it to the experiment run. The mlflow.log_artifact() function logs a local file or directory as an artifact in Azure Machine Learning Studio.
Box 3: mlflow_run.info.run_id
Retrieve Run ID
To fetch the finalized run data using the MlflowClient, you need to pass the unique run ID string.
This identifier is accessed via the active run object using mlflow_run.info.run_id. Note that you also need to use the instantiated client object variable instead of the class name MlflowClient to call the method correctly.
Reference:
https://levelup.gitconnected.com/mlops-mastering-mlflow-unlocking-efficient-model-management-and-experiment-tracking-d9d0e71cc697
질문 # 85
You have an Azure Machine Learning workspace.
You have the following code:
You plan to rely on serverless compute to train a model by using Azure Machine Learning Python SDK v2.
The serverless compute must use a designated number of nodes of a specific virtual machine type.
You need to modify the code to run the training job according to the plan.
How should you modify the command object? To answer, select the appropriate oations in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
질문 # 86
You develop a Prompt flow in Microsoft Foundry project.
You plan to use variants and invoke a custom API in the flow.
You need to add tools to the flow that will implement the planned functionality. Your solution must minimize development efforts.
Which tools should you use? To answer, move the appropriate tools to the correct functionalities. You may use each tool 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.
정답：
설명：
Explanation:
Implement variants: LLM tool
Invoke a custom API: Python tool
For implementing variants , use the LLM tool . Microsoft documents that Prompt flow variants are supported specifically on LLM tool nodes. A variant represents an alternative configuration of the same node, such as different prompt text, temperature values, deployment settings, or other LLM parameters. This allows multiple prompt/model configurations to be tested without duplicating the entire flow, which directly minimizes development effort.
For invoking a custom API , use the Python tool . The Python tool executes custom Python logic within a Prompt flow node and can integrate with external or third-party services. Microsoft explicitly describes Prompt flow tools as supporting integration with third-party APIs and Python packages, and the Python tool can also consume a custom connection when authentication credentials are required. This makes it the appropriate choice for calling an API that is not covered by a built-in Prompt flow tool.
The Embedding tool is designed to generate vector embeddings for text and is therefore unrelated to either requirement. It does not provide variant functionality and is not the general-purpose mechanism for invoking a custom API.
Study Guide Reference: Design and implement a GenAIOps infrastructure - Prompt flow tools, LLM variants, Python nodes, custom integrations, and flow experimentation.
질문 # 87
You use Azure Machine Learning to tram a model.
You must use Baylean sampling to Tune hyperparaters.
You need to select a learning_rate parameter distribution.
Which two distributions can you use? Each correct answer presents a complete solution.
NOTE Each correct selection is worth one point.
정답：C,D
질문 # 88
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Pass4Test의 Microsoft AI-300덤프로Microsoft AI-300시험준비를 하면 시험패스는 간단한 일이라는걸 알게 될것입니다. Microsoft AI-300덤프는 최근Microsoft AI-300시험의 기출문제모음으로 되어있기에 적중율이 높습니다.시험에서 떨어지면 덤프비용 전액 환불해드리기에 우려없이 덤프를 주문하셔도 됩니다.
AI-300최고품질 시험대비자료: https://www.pass4test.net/AI-300.html