You have an Azure Machine Learning workspace.
You plan to run a job to train a model as an MLflow model output.
You need to specify the output mode of the MLflow model.
Which three modes can you specify? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. rw_mount
B. ro_mount
C. upload
D. download
E. direct
Show Answer
Correct Answer: A, C, E
Explanation: When producing an MLflow model as a job output in Azure Machine Learning (v2), only output modes that support writing are valid. The supported output modes are:
- rw_mount: mounts the storage with read/write access so the job can write the MLflow artifacts.
- upload: uploads the output artifacts to the datastore after the job completes.
- direct: writes outputs directly to the target storage location.
The ro_mount mode is read-only and cannot be used for outputs, and download is not a valid output mode.
Question 65
You manage an Azure Machine Learning workspace.
You plan to import data from Azure Data Lake Storage Gen2.
You need to build a URI that represents the storage location.
Which protocol should you use?
A. https
B. adl
C. abfss
D. wasbs
Show Answer
Correct Answer: C
Explanation: Azure Machine Learning uses URI formats specific to each storage type. For Azure Data Lake Storage Gen2, the correct protocol is **abfss://** (Azure Blob File System Secure), which is required for secure access to ADLS Gen2 data sources.
Question 67
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 have the following Azure subscriptions and Azure Machine Learning service workspaces:
You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The solution does not meet the goal because the provided Python code does not correctly obtain a reference to the ml-project workspace. To retrieve a workspace, the MLClient must be properly instantiated with required parameters (such as subscription ID, resource group, and workspace name), and the get method must be called with the correct arguments. As written, the solution is incomplete or incorrectly structured, so it would fail to return the desired workspace reference.
Question 68
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 have the following Azure subscriptions and Azure Machine Learning service workspaces:
You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: In Azure ML SDK v2, MLClient.from_config() is a valid way to obtain a reference to a workspace when the config.json file contains the subscription ID, resource group, and workspace name for ml-project. The credential parameter is optional; if it is not provided, the SDK automatically uses DefaultAzureCredential (for example, Azure CLI login, managed identity, or environment-based credentials). Therefore, running this code successfully returns a reference to the ml-project workspace and meets the goal.
Question 70
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 have the following Azure subscriptions and Azure Machine Learning service workspaces:
You need to obtain a reference to the ml-project workspace.
Solution: Run the following Python code:
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The provided Python code does not correctly obtain a reference to the ml-project workspace because it omits required parameters (such as the subscription ID) when creating the MLClient. Without fully specifying the workspace context, the reference cannot be resolved.
Question 71
You have an Azure Machine Learning workspace named WS1.
You plan to use the Responsible AI dashboard to assess MLflow models that you will register in WS1.
You need to identify the library you should use to register the MLflow models.
Which library should you use?
A. PyTorch
B. mlpy
C. TensorFlow
D. scikit-learn
Show Answer
Correct Answer: D
Explanation: The Azure Responsible AI dashboard currently supports only MLflow models registered with the scikit-learn (sklearn) flavor in Azure Machine Learning. Other MLflow flavors such as PyTorch or TensorFlow are not supported for Responsible AI analysis.
Question 72
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 are creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.
You start by creating a linear regression model.
You need to evaluate the linear regression model.
Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The goal is to evaluate a linear regression model, which is a regression task. Appropriate metrics include regression metrics such as Relative Squared Error and Coefficient of Determination (R²). However, Accuracy, Precision, Recall, F1 score, and AUC are classification metrics and are not suitable for evaluating a regression model. Including inappropriate metrics means the solution does not correctly meet the goal.
Question 73
HOTSPOT
-
You create multiple machine learning models by using automated machine learning.
You need to configure a primary metric for each use case.
Which metrics should you configure? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Explanation: Regression tasks use r2_score to measure how well predicted values explain variance in a continuous target. Classification tasks like sentiment analysis commonly use accuracy to measure the proportion of correctly classified labels.
Question 74
You run Azure Machine Learning training experiments. The training scripts directory contains 100 files that includes a file named .amlignore. The directory also contains subdirectories named ./outputs and ./logs.
There are 20 files in the training scripts directory that must be excluded from the snapshot to the compute targets. You create a file named .gitignore in the root of the directory. You add the names of the 20 files to the .gitignore file. These 20 files continue to be copied to the compute targets.
You need to exclude the 20 files.
What should you do?
A. Copy the contents of the file named .gitignore to the file named .amlignore.
B. Move the file named .gitignore to the ./outputs directory.
C. Move the file named .gitignore to the ./logs directory.
D. Add the contents of the file named .amlignore to the file named .gitignore.
Show Answer
Correct Answer: A
Explanation: Azure Machine Learning uses .amlignore to determine which files are excluded from the snapshot sent to compute targets. When both .amlignore and .gitignore exist, .amlignore takes precedence and .gitignore is ignored. Since the 20 files are listed only in .gitignore, they continue to be copied. Copying those entries into .amlignore ensures the files are excluded.
Question 76
You manage an Azure Machine Learning workspace.
You must provide explanations for the behavior of the models with feature importance measures.
You need to configure a Responsible AI dashboard in Azure Machine Learning.
Which dashboard component should you configure?
A. Counterfactual what-if
B. Casual inference
C. Fairness assessment
D. Interpretability
Show Answer
Correct Answer: D
Explanation: Feature importance measures used to explain model behavior are provided by the Model Interpretability component of the Azure Machine Learning Responsible AI dashboard. This component shows global and local feature importance to help understand how inputs influence predictions.
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