AI-300 무료 덤프문제 온라인 액세스

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 manage an Azure Machine Learning workspace. The Python script named script.py reads an argument named training_data. The training_data argument specifies the path to the training data in a file named dataset1.csv.
You plan to run the script.py Python script as a command job that trains a machine learning model.
You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.
Solution: python script.py dataset1.csv
Does the solution meet the goal?

• A.No
• B.Yes

DRAG DROP
A team deploys a classification model to production and scores incoming customer data daily.
After several weeks, business stakeholders report unexpected changes in prediction behavior, even though the endpoint remains healthy.
You need to determine whether data drift is occurring and if it is, identify the appropriate actions.
Which action should you perform for each observed signal? To answer, move the appropriate actions to the correct observed signals. You may use each action 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:

A team uses a hosted Git repository to store training code and pipeline definitions of a machine learning experiment.
The team must ensure that access to the repository is granted without requiring each developer to store personal access tokens on their machines.
Repository access must be secure and centrally managed to reduce credential spread.
You need to enable secure access between an Azure Machine Learning workspace and the repository.
What should you do?

• A.Share a repository deploy key across all developers on the team.
• B.Configure the workspace to use a managed identity for repository access.
• C.Require each developer to authenticate locally before every pipeline run.
• D.Generate a personal access token and store it in a pipeline variable.

You are preparing training data for a fine-tuning job in Microsoft Foundry.
Real production conversations cannot be used due to compliance requirements.
You need to generate synthetic interaction data that can be used for fine-tuning a generative model.
What should you do?

• A.Export model evaluation logs and use them directly as training data.
• B.Use a simulator to generate prompt-response interaction data that matches the target task.
• C.Run a simulator to produce telemetry logs and trace data from user interactions.
• D.Enable A/B testing and capture live user traffic for data generation.

Drag and Drop Question
You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1. You connect to compute1 by using a terminal window from workspace1.
You create a file named "requirements.txt" containing Python dependencies to include Jupyter.
You need to add a new Jupyter kernel to compute1.
Which four commands should you use? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

정답:

Explanation:
Step 1: conda create -n "python_env"
1. Create the environment
conda create -n python_env python=3.10 -y
Step 2: conda activate "python_env"
2. Activate it
conda activate python_env
Step 3: pip install -r "requirements.txt"
3. Install your dependencies (including ipykernel)
pip install -r requirements.txt
Step 4: ipython kernel install --user --name="python_env"
4. Register the environment as a Jupyter kernel
ipython kernel install --user --name="python_env"