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.
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You have an Azure Machine Learning workspace named Workspace1. Workspace1 has a registered MLflow model named model1 with PyFunc flavor.
You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.
You have the following code:
You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.
Solution: Add the environment parameter.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: The solution does not meet the goal. MLflow (PyFunc) models support no-code deployment to managed online endpoints, meaning you do not need to explicitly specify an environment. For deployments without egress connectivity, additional considerations such as model packaging are required. Simply adding the environment parameter is neither required nor sufficient to ensure a successful deployment in this scenario.
Question 55
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 an Azure Machine Learning workspace named Workspace1. Workspace1 has a registered MLflow model named model1 with PyFunc flavor.
You plan to deploy model1 to an online endpoint named endpoint1 without egress connectivity by using Azure Machine Learning Python SDK v2.
You have the following code:
You need to add a parameter to the ManagedOnlineDeployment object to ensure the model deploys successfully.
Solution: Add the with_package parameter.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: When deploying an MLflow PyFunc model to an online endpoint without egress connectivity, Azure Machine Learning requires model packaging so that dependencies are bundled instead of downloaded at deployment time. In the Python SDK v2, this is enabled by setting the with_package parameter on the ManagedOnlineDeployment. Adding this parameter directly addresses the no-egress requirement and allows the deployment to succeed.
Question 56
You are a data scientist working for a hotel booking website company. You use the Azure Machine Learning service to train a model that identifies fraudulent transactions.
You must deploy the model as an Azure Machine Learning online endpoint by using the Azure Machine Learning Python SDK v2. The deployed model must return real-time predictions of fraud based on transaction data input.
You need to create the script that is specified as the scoring_script parameter for the CodeConfiguration class used to deploy the model.
What should the entry script do?
A. Register the model with appropriate tags and properties.
B. Create a Conda environment for the online endpoint compute and install the necessary Python packages.
C. Load the model and use it to predict labels from input data.
D. Start a node on the inference cluster where the model is deployed.
E. Specify the number of cores and the amount of memory required for the online endpoint compute.
Show Answer
Correct Answer: C
Explanation: In Azure Machine Learning online endpoints, the scoring_script (entry script) defines how inference is performed. Its responsibility is to load the trained model and implement init() and run() functions to process incoming request data and return real-time predictions. Model registration, environment setup, compute sizing, and cluster management are handled elsewhere in the deployment configuration, not in the scoring script.
Question 57
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 an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create a compute instance.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: A terminal session in Azure Machine Learning Studio already implies that a compute instance exists and is running. To add a new Jupyter kernel from that terminal, the prerequisite task is to configure or create a Python environment and register it as a kernel (for example, by installing ipykernel), not to create another compute instance. Therefore, creating a compute instance is not the required task before adding the new kernel.
Question 58
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 an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.8 – AzureML kernel.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: Deleting the default Python 3.8 – AzureML kernel is not a prerequisite for adding a new Jupyter kernel. To add a new kernel from a terminal session, you must first create or have an environment (for example, a Conda or virtual environment) and then register it as a Jupyter kernel. Therefore, the proposed solution does not meet the goal.
Question 59
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 an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Delete the Python 3.6 – AzureML kernel.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: B
Explanation: No. Deleting the Python 3.6 – AzureML kernel is not a required prerequisite to adding a new Jupyter kernel. To add a new kernel from a terminal session in Azure Machine Learning, you must create or activate an environment and install ipykernel for that environment, then register it as a kernel. Therefore, the proposed solution does not meet the goal.
Question 60
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 an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio.
You plan to add a new Jupyter kernel that will be accessible from the same terminal session.
You need to perform the task that must be completed before you can add the new kernel.
Solution: Create an environment.
Does the solution meet the goal?
A. Yes
B. No
Show Answer
Correct Answer: A
Explanation: In Azure Machine Learning, adding a new Jupyter kernel requires an Azure ML environment because kernels are created from environments. Before registering the kernel from the terminal session, the environment must exist, so creating an environment is a required prerequisite.
Question 61
You manage an Azure Machine Learning workspace. You have a folder that contains a CSV file. The folder is registered as a folder data asset.
You plan to use the folder data asset for data wrangling during interactive development.
You need to access and load the folder data asset into a Pandas data frame.
Which method should you use to achieve this goal?
A. mltable.from_parquet_files()
B. mltable.from_delimited_files()
C. mltable.from_data_lake()
D. mltable.load()
Show Answer
Correct Answer: D
Explanation: Because the data is already registered as an Azure ML data asset, you should load it by reference using mltable.load() (for example, with an azureml:<asset_name>:<version> URI). The from_delimited_files() method is used to create an MLTable from raw file paths, not to load an existing registered folder data asset.
Question 62
You have an Azure Machine Learning workspace.
You plan to tune a model hyperparameter when you train the model.
You need to define a search space that returns a normally distributed value.
Which parameter should you use?
A. QUniform
B. LogUniform
C. Uniform
D. LogNormal
Show Answer
Correct Answer: D
Explanation: Azure Machine Learning hyperparameter tuning supports several probability distributions. A normally distributed (Gaussian) search space is defined using **LogNormal** when the parameter values should follow a normal distribution in log space. The other options represent uniform or logarithmic uniform distributions, not normal distributions.
Question 63
You have an Azure Machine Learning workspace.
You plan to use the workspace to set up automated machine learning training for an image classification model.
You need to choose the primary metric to optimize the model training.
Which primary metric should you choose?
A. r2_score
B. mean_absolute_error
C. accuracy
D. root_mean_squared_log_error
Show Answer
Correct Answer: C
Explanation: For an image classification task in Azure Automated ML, the problem type is classification. The appropriate primary metric for optimizing classification models is accuracy, which measures the proportion of correctly classified images. The other options (r2_score, mean_absolute_error, root_mean_squared_log_error) are regression metrics and are not suitable for image classification.
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