You create a multi-class image classification deep learning experiment by using the PyTorch framework. You plan to run the experiment on an Azure Compute cluster that has nodes with GPU's.
You need to define an Azure Machine Learning service pipeline to perform the monthly retraining of the image classification model. The pipeline must run with minimal cost and minimize the time required to train the model.
Which three pipeline steps should you run in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

You manage an Azure Machine Learning workspace named workspace 1 and a Data Science Virtual Machine (DSVM) named DSMV1.
You must run an experiment on DSMV1 by using a Jupyter notebook and Python SDK v2 code. You must store metrics and artifacts in workspace1. You start by creating Python SDK v2 code to import all required packages.
You need to implement the Python SDK v2 code to store metrics and artifacts in workspace1.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.

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 in the review screen.
You train and register a machine learning model.
You plan to deploy the model as a real-time web service. Applications must use key-based authentication to use the model.
You need to deploy the web service.
Solution:
Create an AciWebservice instance.
Set the value of the ssl_enabled property to True.
Deploy the model to the service.
Does the solution meet the goal?

You manage an Azure At Foundry project. You deploy an Azure OpenAI Service chat model. You configure the development environment with the necessary packages to build a Prompt flow in Visual Studio Code IDE.
You create a Python file called chat.py. You must configure the large language model with 0.2 as the temperature.
You need to develop the chat application.
Which two actions should you perform? Each correct answer presents part of the solution. Choose two.
NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace. You build automated machine learning training experiments for computer vision models.
You need to use a primary metric for model optimization and hyperparameter tuning for each model.
Which primary metrics should you use for the models? To answer, move the appropriate primary metrics to the correct computer vision models. You may use each primary metric 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.