DumpTOP선택으로Microsoft AI-300시험을 패스하도록 도와드리겠습니다. 우선 우리DumpTOP 사이트에서Microsoft AI-300관련자료의 일부 문제와 답 등 샘플을 제공함으로 여러분은 무료로 다운받아 체험해보실 수 있습니다. 체험 후 우리의DumpTOP에 신뢰감을 느끼게 됩니다. DumpTOP에서 제공하는Microsoft AI-300덤프로 시험 준비하세요. 만약 시험에서 떨어진다면 덤프전액환불을 약속 드립니다.
| Section | Objectives |
|---|---|
| Topic 1: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - 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 |
| Topic 2: 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 |
| Topic 3: Implement machine learning model lifecycle and operations | - Deploy models to real-time and batch endpoints - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Retrain, update, and manage model versions in production |
| Topic 4: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Set up Azure Machine Learning workspace and compute targets - Implement security, governance, and compliance for MLOps - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 5: Optimize generative AI systems and model performance | - Optimize inference performance, caching, and throughput - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Tune prompts, system messages, and grounding strategies |
우리DumpTOP 에서 여러분은 아주 간단히Microsoft AI-300시험을 패스할 수 있습니다. 만약 처음Microsoft AI-300시험에 도전한다면 우리의Microsoft AI-300시험자료를 선택하여 다운받고 고부를 한다면 생가보다는 아주 쉽게Microsoft AI-300시험을 통과할 수 있으며 무엇보다도 시험시의 자신감 충만에 많은 도움이 됩니다. 다른 자료판매사이트도 많겠지만 저희는 저희 자료에 자신이 있습니다. 우리의 시험자료는 모두 하이퀼러티한 문제와 답으로 구성되었습니다, 그리고 우리는 업데트를 아주 중요시 생각하기에 어느 사이트보다 더 최신버전을 보실 수 잇을것입니다. 우리의Microsoft AI-300자료로 자신만만한 시험 준비하시기를 바랍니다. 우리를 선택함으로 자신의 시간을 아끼는 셈이라고 생각하시면 됩니다.Microsoft AI-300로 빠른시일내에 자격증 취득하시고MicrosoftIT업계중에 엘리트한 전문가되시기를 바랍니다.
질문 # 161
A real-time endpoint experiences sporadic latency spikes. Investigation reveals instances scale down to zero during inactivity. You need to reduce latency without significantly increasing cost.
What should you configure?
정답：C
질문 # 162
A team manages prompts used by a generative AI application built on Microsoft Foundry. Prompt updates are frequent, and prior changes have caused unexpected behavior in production.
The team requires that new prompt versions can be tested and reviewed without affecting production behavior.
You need to implement a source control practice that allows safe prompt experimentation.
What should you do?
정답：A
설명：
Creating a feature Git branch for each prompt change is an excellent and highly recommended action.
Prompts are code. Managing them through a standard software development lifecycle prevents production failures and allows for safe experimentation.
Isolation: Changes stay completely separate from the working production version.
Collaboration: Teams can review prompt tweaks together via Pull Requests (PRs).
Traceability: You can easily see who changed a prompt, when, and why.
Rollbacks: If a prompt performs poorly, you can instantly revert to a prior Git commit.
Reference:
https://mirascope.com/blog/prompt-management-system
질문 # 163
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?
정답：A
설명：
Correct:
* In Microsoft Foundry, turn on Tracing for the prompt flow of the project and execute test runs to produce trace data.
Incorrect:
* Create prompt variants and compare their outputs in the Evaluation experience.
* Use the prompt flow SDK to enable tracing for the flow before executing runs. Then run the flow to generate traceable results.
Note:
In Azure AI Foundry, you can capture and compare these metrics by enabling Tracing and using the Bulk Test feature. This allows you to systematically evaluate different prompt variants against a common dataset.
Steps to Evaluate and Compare Prompt Variants
*-> 1. Enable Tracing
Navigate to your Prompt Flow project.
Locate the Tracing toggle at the top of the flow authoring page.
Switch it to On.
This ensures every execution captures latency, token counts, and node-level inputs/outputs.
2. Create Prompt Variants
Within your flow, identify the LLM node you want to test.
Click Variants to create multiple versions of your prompt (e.g., Variant_0, Variant_1).
This allows you to test different instructions or few-shot examples side-by-side.
3. Run a Bulk Test (Evaluation)
4. Analyze the Results
Reference:
https://www.linkedin.com/pulse/streamlining-generative-ai-development-azure-foundry-tracing- taneja-mbwze
질문 # 164
You create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:
You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location should you use?
정답：B
질문 # 165
You create a binary classification model. You use the Fairlearn package to assess model fairness.
You must eliminate the need to retrain the model.
You need to implement the Fairlearn package.
Which algorithm should you use?
정답：D
질문 # 166
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