Microsoft AI-300

Topic 2: Design and implement an MLOps infrastructure

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 train.py --training_data training_data

Does the solution meet the goal?

Topic 2: Design and implement an MLOps infrastructure

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 --trainingdata ${{inputs.training_data}}

Does the solution meet the goal?

Topic 2: Design and implement an MLOps infrastructure

HOTSPOT

You review the following Azure CLI command and the relevant Bicep excerpt.

(Non-relevant sections are omitted.)

You need to validate what the snippet will do before it is merged. For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

Topic 2: Design and implement an MLOps infrastructure

DRAG DROP

A team performs interactive experimentation during development. The team also runs scalable jobs for model training.

The team must minimize costs while ensuring compute resources scale when needed. Different workloads require different compute behaviors within the same workspace.

You need to configure compute targets that support each workload.

Which compute targets should you use? To answer, move the appropriate compute targets to the correct workload types. You may use each compute target 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.

Topic 2: Design and implement an MLOps infrastructure

DRAG DROP

You have an existing GitHub repository containing Azure Machine Learning project files.

You need to clone the repository to your Azure Machine Learning shared workspace file system.

Which four 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: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.