Itexamdump에서 판매하고 있는 Microsoft AI-300인증시험자료는 시중에서 가장 최신버전으로서 시험적중율이 100%에 가깝습니다. Microsoft AI-300덤프자료를 항상 최신버전으로 보장해드리기 위해Microsoft AI-300시험문제가 변경되면 덤프자료를 업데이트하도록 최선을 다하고 있습니다. Itexamdump는 여러분이 자격증을 취득하는 길에서 없어서는 안되는 동반자로 되어드릴것을 약속해드립니다.
| Section | Weight | Objectives |
|---|---|---|
| Design and implement a GenAIOps infrastructure | 20–25% | - Set up Microsoft Foundry environment
|
| Optimize generative AI systems and model performance | 15–20% | - Optimize model selection and configuration
|
| Implement machine learning model lifecycle and operations | 25–30% | - Orchestrate model training and experimentation
|
| Implement generative AI quality assurance and observability | 10–15% | - Evaluate and test generative AI applications
|
| Design and implement an MLOps infrastructure | 15–20% | - Create and manage Machine Learning workspace resources and assets
|
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질문 # 41
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 # 42
A team develops and manages a conversational assistant by using Microsoft Foundry.
The team must be able to validate that the assistant does not produce hateful responses before the application is exposed to any users.
You need to evaluate the model output for hateful responses as part of a repeatable validation process.
Which evaluator should you configure first?
정답：A
설명：
The Content Safety evaluator in Microsoft Foundry is specifically designed to detect hate speech, violence, sexual content, and self-harm content in model outputs, making it the most direct evaluator for the requirement of preventing hateful responses. It uses Azure AI Content Safety service under the hood, which is trained to classify content across these harmful categories with high accuracy. Protected Material (option A) evaluates whether outputs contain copyrighted or licensed material - not hate speech. Groundedness (option B) measures factual accuracy against a source context - completely irrelevant to hate detection. Indirect Attacks (option C) evaluates whether the model was manipulated via prompt injection to produce harmful content - this is a robustness metric, not a direct output quality measure. Content Safety must be configured first because detecting hate is the primary safety concern explicitly identified in the requirement.
Microsoft Learn Reference Topic: Content safety evaluators in Microsoft Foundry - Detect harmful content in AI model outputs
질문 # 43
Hotspot Question
You manage a Microsoft Foundry project.
You are evaluating two RAG solutions.
When generating answers, the solutions display the following results:
- The first solution displays low completeness and low utilization.
- The second solution displays low completeness and high utilization.
You need to address the issues found during evaluation.
Which action should you perform first for each issue? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
정답：
설명：
질문 # 44
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：A
질문 # 45
A data science team completes multiple training runs within an experiment by using MLflow.
The team wants to store a selected model in Azure Machine Learning so that it can be versioned and deployed later.
The model must be versioned centrally for reuse across environments.
You need to version the trained model.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.
정답：B,D
설명：
To set up versioning for a trained model in an Azure Machine Learning (Azure ML) workspace using MLflow, you must capture the model artifacts during the training run and then register the model into the centralized registry.
[A]
1. Capture Model Artifacts
During each training run, use the MLflow SDK to log the model. This ensures that all necessary files (the model binary, environment dependencies, and the MLmodel metadata) are stored as run outputs in the workspace.
Manual Logging: Use a flavor-specific method like mlflow.sklearn.log_model(model,
"model_path") within an active run.
Automatic Logging: Call mlflow.autolog() before starting your training. This automatically captures metrics, parameters, and the model artifacts for supported frameworks.
Artifact Location: Once logged, artifacts are typically found in the outputs/ folder of the specific run, accessible via the Azure Machine Learning Studio.
[B]
2. Register the Model
After identifying the best-performing run, you register it to the Model Registry. This creates a named, versioned entity that can be accessed across different environments for deployment.
To set up versioning for an MLflow model in Azure Machine Learning (Azure ML) that is accessible across different environments, you should use a centralized Azure ML Registry. While a standard Azure ML Workspace acts as an MLflow server for individual experiments, an Azure ML Registry is the specifically designed feature for sharing models, environments, and components across multiple workspaces and environments within an Azure tenant.
3. Centralized Reuse
By registering the model in the workspace's registry, you establish a single source of truth. You can then load this specific version in any environment (e.g., staging or production) using its registry URI: models:/<model_name>/<version_or_alias>.
Incorrect:
[Not D]
Must use an Azure ML Registry (Central).
Reference:
https://mlflow.org/docs/latest/ml/model-registry/
질문 # 46
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