많은 사이트에서도 무료Microsoft AI-300덤프데모를 제공합니다.우리도 마찬가지입니다.여러분은 그러한Microsoft AI-300데모들을 보시고 다시 우리의 덤프와 비교하시면 ,우리의 덤프는 다른 사이트덤프와 차원이 다른 덤프임을 아시될것입니다, 우리Itcertkr에서 제공되는 덤프는 100%보장 도를 자랑하며,여러분은 시험패스로 인해 성공과 더 가까워 졌답니다
| Section | Objectives |
|---|---|
| Topic 1: Design and implement an MLOps infrastructure | - Manage environments, data stores, and model registries - Implement security, governance, and compliance for MLOps - Set up Azure Machine Learning workspace and compute targets - Configure source control, CI/CD pipelines, and automation for ML workflows |
| Topic 2: Implement machine learning model lifecycle and operations | - Retrain, update, and manage model versions in production - Monitor model performance, data drift, and operational health - Train, register, and version models using Azure Machine Learning - Deploy models to real-time and batch endpoints |
| Topic 3: Implement generative AI quality assurance and observability | - Monitor latency, token usage, cost, and error rates - Evaluate generative AI outputs for quality, safety, and grounding - Implement logging, tracing, and telemetry for GenAI applications - Conduct red teaming, adversarial testing, and content filtering |
| Topic 4: Optimize generative AI systems and model performance | - Tune prompts, system messages, and grounding strategies - Implement cost management and scaling strategies for GenAI workloads - Fine-tune and distill models for specific use cases - Optimize inference performance, caching, and throughput |
| Topic 5: Design and implement a GenAIOps infrastructure | - Set up Microsoft Foundry and Azure AI services for generative AI workloads - Manage API keys, rate limits, and responsible AI guardrails - Implement RAG (Retrieval-Augmented Generation) pipelines and vector search - Configure prompt orchestration, prompt flows, and agent frameworks |
Itcertkr의Microsoft인증AI-300자료는 제일 적중률 높고 전면적인 덤프임으로 여러분은 100%한번에 응시로 패스하실 수 있습니다. 그리고 우리는 덤프를 구매 시 일년무료 업뎃을 제공합니다. 여러분은 먼저 우리 Itcertkr사이트에서 제공되는Microsoft인증AI-300시험덤프의 일부분인 데모 즉 문제와 답을 다운받으셔서 체험해보실 수 잇습니다.
질문 # 38
Drag and Drop Question
You develop a flow for a Microsoft Foundry project.
You plan to use outputs generated by running the flow to determine the following information:
- the number of tokens used by each large language model (LLM) node of
the flow
- the accuracy of the model used by the flow
You need to examine the output that provides the required information.
Which output type should you examine? To answer, move the appropriate output types to the correct evaluations. You may use each output type 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:
Box 1: Traces
The appropriate output flow type to determine the number of tokens used by each Large Language Model (LLM) node is Traces.
While Metrics provide a macro-level, aggregated overview of total token usage across an entire application or resource, Traces capture detailed, node-by-node execution details.
When a prompt flow runs in Microsoft Foundry, Traces track:
The precise execution path of individual items
Inputs and outputs for each specific LLM node
Detailed telemetry records-such as input_tokens, output_tokens, and total_tokens-bound to that exact step Box 2: Metrics The most appropriate output flow type to determine the accuracy of the model is Metrics.
Metrics are quantitative measurements (such as accuracy, F1-score, precision, recall, or mean squared error) specifically calculated by evaluating model predictions against ground truth data.
Reference:
https://huggingface.co/docs/evaluate/a_quick_tour
질문 # 39
A team is deploying machine learning models to a production inference endpoint in Azure Machine Learning.
The team requires a safe way to validate a new model version without disrupting existing users.
You need to recommend a deployment strategy for controlled testing of a new model version.
What should you configure?
정답：B
설명：
A managed online endpoint can host multiple named deployments simultaneously, and you control what percentage of incoming traffic each deployment receives. With traffic splitting, you route a small percentage of live traffic to the new deployment while the majority continues to the proven existing model. You then monitor error rates, latency, and output quality for both deployments in real time under genuine production load. If the new model underperforms, you instantly route traffic back - no downtime, no user disruption. If it outperforms, you gradually increase its traffic share to 100%. Updating the registry version (option B) does not affect running deployments. A staging endpoint (option C) does not validate under real production load.
An evaluation script (option D) is a pre-deployment step. Traffic splitting is the blue/green and canary deployment pattern that Microsoft recommends for safe production rollouts.
Microsoft Learn Reference Topic: Perform safe rollout of new model deployments using traffic splitting - Azure Machine Learning
질문 # 40
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 # 41
Drag and Drop Question
A company is standardizing generative AI development across multiple teams.
Each team requires an isolated workspace. Governance and shared connections must be centrally managed.
You need to implement a Microsoft Foundry environment structure that supports centralized governance and team isolation.
Which type of configuration should you use for each requirement? To answer, move the appropriate configurations to the correct requirements. 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 # 42
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：B
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 # 43
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우리Itcertkr 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.
AI-300시험응시: https://www.itcertkr.com/AI-300_exam.html