IT인증시험문제는 수시로 변경됩니다. 이 점을 해결하기 위해DumpTOP의Microsoft인증 AI-300덤프도 시험변경에 따라 업데이트하도록 최선을 다하고 있습니다.시험문제 변경에 초점을 맞추어 업데이트를 진행한후 업데이트된Microsoft인증 AI-300덤프를 1년간 무료로 업데이트서비스를 드립니다.
| Section | Objectives |
|---|---|
| Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases |
| Implement generative AI quality assurance and observability | - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Monitor latency, token usage, cost, and error rates - Conduct red teaming, adversarial testing, and content filtering |
| Implement machine learning model lifecycle and operations | - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Retrain, update, and manage model versions in production |
| Design and implement an MLOps infrastructure | - Set up Azure Machine Learning workspace and compute targets - 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 |
| Design and implement a GenAIOps infrastructure | - Configure prompt orchestration, prompt flows, and agent frameworks - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Set up Microsoft Foundry and Azure AI services for generative AI workloads |
DumpTOP는AI-300시험문제가 변경되면AI-300덤프업데이트를 시도합니다. 업데이트가능하면 바로 업데이트하여 업데이트된 최신버전을 무료로 제공해드리는데 시간은 1년동안입니다. AI-300시험을 패스하여 자격증을 취득하고 싶은 분들은DumpTOP제품을 추천해드립니다.온라인서비스를 찾아주시면 할인해드릴게요.
질문 # 103
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 # 104
Drag and Drop Question
An organization is adopting Microsoft Foundry to support multiple generative AI projects across different teams. Microsoft Foundry platform administrators require centralized governance.
Development teams need isolated environments for experimentation and deployment.
Shared policies must be enforced consistently while allowing teams to work independently.
You need to configure the Microsoft Foundry environment to meet the requirements.
Which Microsoft Foundry components should you configure? To answer, move the appropriate configurations to the correct responsibilities. You may use each configuration 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：
질문 # 105
You manage an Azure Machine learning workspace. You develop a machine learning model.
You must deploy the model to use a low-priority VM with a pricing discount.
You need to deploy the model.
Which compute target should you use?
정답：D
설명：
The best compute target for deploying a model using low-priority VMs (or their modern successor, Spot VMs) is an Azure Machine Learning compute cluster.
Best Compute Target: AML Compute Cluster
For low-priority/Spot pricing, you should use an Azure Machine Learning compute cluster configured with the LowPriority tier.
Primary Use Case: This target is specifically recommended for batch deployments. Batch inference is ideal for low-priority VMs because these jobs are asynchronous and can tolerate the interruptions (preemptions) inherent to discounted capacity.
Pricing Advantage: Low-priority VMs offer significant discounts-often up to 80% off standard rates-by utilizing unused Azure capacity.
Automatic Handling: When a node is preempted during a batch job, Azure Machine Learning automatically attempts to replace the lost capacity and re-queues failed tasks to the cluster.
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch
질문 # 106
A team deploys a model to a real-time endpoint in Azure Machine Learning. You deploy some updates to the endpoint.
The endpoint returns errors after the new deployment is released.
You need to restore the service as quickly as possible.
What should you do first?
정답：B
설명：
To restore the service as quickly as possible, you can roll back traffic to the previous deployment by updating the traffic allocation settings of your Azure Machine Learning online endpoint.
Azure Machine Learning managed online endpoints support multiple deployments under a single endpoint, allowing for blue-green deployment strategies where you can shift traffic between versions instantly.
Key Benefits of This Approach
Instant Recovery: Traffic shifting is a routing change and does not require redeploying the previous model's code or environment, making it the fastest recovery method.
No Downtime: Because the previous deployment remains "warm" (active but receiving no traffic), the switch happens without interrupting the service.
Isolation for Debugging: You can keep the failing deployment at 0% traffic to inspect its logs using az ml online-deployment get-logs without affecting end users How to Roll Back Traffic If your new deployment (e.g., "green") is returning errors, you can reallocate 100% of the traffic back to the known stable deployment (e.g., "blue") using the following methods:
* Azure CLI: Use the az ml online-endpoint update command to set the traffic percentage:
az ml online-endpoint update --name <your-endpoint-name> --traffic "blue=100 green=0"
* Azure Machine Learning Studio:
Navigate to Endpoints in the left menu.
Select your specific real-time endpoint.
Go to the Details or Live Traffic tab.
Adjust the traffic percentages so the previous deployment receives 100% and the failing deployment receives 0%.
Select Update or Save to apply the changes immediately
Reference:
https://learn.microsoft.com/en-us/answers/questions/1275110/azure-ml-v2-yaml-code-for-live- traffic-allocation
질문 # 107
-
A biomedical research company plans to enroll people in an experimental medical treatment trial.
You create and train a binary classification model to support selection and admission of patients to the trial.
The model includes the following features: Age, Gender, and Ethnicity.
The model returns different performance metrics for people from different ethnic groups.
You need to use Fairlearn to mitigate and minimize disparities for each category in the Ethnicity feature.
Which technique and constraint should you use? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
Explanation:
Technique: Grid search
Constraint: Demographic parity
The appropriate mitigation technique is GridSearch with a DemographicParity constraint. Fairlearn provides reduction-based mitigation algorithms that retrain a standard estimator using differently weighted training data while enforcing a specified fairness constraint. GridSearch is one of Fairlearn ' s reduction algorithms for binary classification and can search across candidate models representing different trade-offs between predictive performance and fairness.
The appropriate constraint is Demographic parity because the model determines whether individuals receive an opportunity-in this case, selection and admission to a medical trial . Microsoft categorizes decisions that extend or withhold opportunities or resources as allocation harms and specifically identifies demographic parity as a parity constraint intended to mitigate allocation disparities in binary classification.
Demographic parity seeks comparable rates of positive predictions across groups defined by the sensitive attribute. Here, Ethnicity is the sensitive feature, so the objective is to reduce disparities in selection rates between ethnic groups.
False-positive-rate parity has a different purpose: it specifically requires comparable false-positive rates across groups among cases whose true label is negative. The scenario does not identify unequal false-positive rates as the problem.
질문 # 108
......
다른 방식으로 같은 목적을 이룰 수 있다는 점 아세요? 여러분께서는 어떤 방식, 어느 길을 선택하시겠습니까? 많은 분들은Microsoft인증AI-300시험패스로 자기 일에서 생활에서 한층 업그레이드 되기를 바랍니다. 하지만 모두 다 알고계시는그대로Microsoft인증AI-300시험은 간단하게 패스할 수 있는 시험이 아닙니다. 많은 분들이Microsoft인증AI-300시험을 위하여 많은 시간과 정신력을 투자하고 있습니다. 하지만 성공하는 분들은 적습니다.
AI-300높은 통과율 인기덤프: https://www.dumptop.com/Microsoft/AI-300-dump.html