Try Microsoft : AI-300 valid & accurate questions and answers

Hotspot Question
You create an Azure Machine Learning workspace.
You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

You need to complete the Python code to log the table.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Explanation:
Box 1: dump
Log a table, SDK v2 with MLflow
# Add a metric for each column prefixed by metric name. Similar to log_row row1 = {"table.col1": 5, "table.col2": 10}
# To be done for each row in the table
mlflow.log_metrics(row1)
# Using mlflow.log_artifact
import json
with open("table.json", 'w') as f:
json.dump(table, f)
mlflow.log_artifact("table.json")
Box 2: mlflow.log_artifact
Reference:
https://learn.microsoft.com/en-us/azure/machine-learning/reference-migrate-sdk-v1-mlflow-tracking

A company's platform engineers manage the resource settings and governance of Microsoft Foundry.
Developers must be able to create and update project assets but must not be able to change resource-level configurations.
You need to enforce least privilege access for the engineers and developers.
Which two actions should you perform? Each correct answer presents part of the solution.
Choose two.
NOTE: Each correct selection is worth one point.

• A. Assign a resource-level Azure AI Administrator role to the platform engineers.
• B. Disable Microsoft Entra ID authentication for the Microsoft Foundry resource.
• C. Assign the Azure AI Developer role to the developers.
• D. Share a single API key across all teams.

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A team schedules weekly retraining of a model using Azure ML pipelines. They also want retraining triggered automatically when production data significantly deviates from training data distribution, without duplicating pipeline logic. What should they implement?

• A. Azure Function to retrain model manually
• B. One pipeline triggered by schedule and data drift alerts
• C. Notebook-based retraining process
• D. Two independent pipelines with shared scripts

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Hotspot Question
A team is building a generative AI agent by using Retrieval-Augmented Generation (RAG) in Microsoft Foundry.
The team frequently updates prompt content. The team must be able to track changes across contributors while avoiding full application redeployments.
You need to enable rapid prompt iteration with traceability. Applications consuming the agent must be able to use updated prompts without requiring redeployment.
What should you configure for each requirement? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

A team is working in Microsoft Foundry to test and compare large language model (LLM) prompt variants in a development environment.
The team requires consistent inputs to evaluate prompt variants without relying on live user traffic.
You need to create a controlled evaluation of input data.
Which action should you perform first?

• A. Enable observability metrics.
• B. Configure content filters.
• C. Generate synthetic interaction data.
• D. Apply a blocklist.

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