Microsoft Designing and Implementing a Data Science Solution on Azure (DP-100)

You plan to run a Python script as an Azure Machine Learning experiment.

The script must read files from a hierarchy of folders. The files will be passed to the script as a dataset argument. You must specify an appropriate mode for the dataset argument.

Which two modes can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You create a mufti-class image classification deep learning model.

The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images. and retrain the model.

You need to use the Azure Machine Learning SDK to configure the schedule for the pipeline.

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.

You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python. You must add a statement that retrieves the names of the logs and outputs generated by the script.

You need to reference a Python class object from the SDK for the statement. Which class object should you use?

You create an Azure Machine Learning workspace named workspace1. You create a Python SDK v2 notebook to perform custom model training in workspace1.

You need to run the notebook from Azure Machine Learning Studio in workspace1.

What should you provision first?

You run a script as an experiment in Azure Machine Learning.

You have a Run object named run that references the experiment run. You must review the log files that were generated during the experiment run. You need to download the log files to a local folder for review.

Which two code segments can you run to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace. You create an experiment named experiment1 by using the Azure Machine Learning Python SDK v2 and MLflow.

You are reviewing the results of experiment1 by using the following code segment:

You have the following code. The code prepares an experiment to run a script:

The experiment must be run on local computer using the default environment. You need to add code to start the experiment and run the script.

Which code segment should you use?

You create an Azure Machine Learning workspace.

You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log string metrics.

You need to implement the method to log the string metrics.

Which method should you use?

You use the following code to define the steps for a pipeline:

from azureml.core import Workspace, Experiment, Run from azureml.pipeline.core import Pipeline

from azureml.pipeline.steps import PythonScriptStep ws = Workspace.from_config()

. . .

step1 = PythonScriptStep(name="step1", ...) step2 = PythonScriptsStep(name="step2", ...) pipeline_steps = [step1, step2]

You need to add code to run the steps.

Which two code segments can you use to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You create an Azure Machine Leaming workspace.

You are developing a Python SDK v2 notebook to perform custom model training in the workspace. The notebook code imports all required packages.

You need to complete the SDK v2 code to include a training script. environment. and compute information

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You create an Azure Databricks workspace and a linked Azure Machine Learning workspace. You have the following Python code segment in the Azure Machine Learning workspace:

import mlflow

import mlflow.azureml import azureml.mlflow import azureml.core

from azureml.core import Workspace

subscription_id = 'subscription_id' resourse_group = 'resource_group_name' workspace_name = 'workspace_name'

ws = Workspace.get(name=workspace_name, subscription_id=subscription_id, resource_group=resource_group)

experimentName = "/Users/{user_name}/{experiment_folder}/{experiment_name}"

mlflow.set_experiment(experimentName)

uri = ws.get_mlflow_tracking_uri() mlflow.set_tracking_uri(uri)

Instructions: For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2. You create a General Purpose v2 Azure storage account named mlstorage1. The storage account includes a publicly accessible container named mlcontainer1. The container stores 10 blobs with files in the CSV format.

You must develop Python SDK v2 code to create a data asset referencing all blobs in the container named mlcontainer1.

You need to complete the SDK v2 code.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You create and register a model in an Azure Machine Learning workspace.

You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step.

You need to create the compute target.

Which class should you use?

You are designing an Azure Machine Learning solution by using the python SDK via.

You must train and deploy the solution by using a compute target. The compute target must meet the following requirements:

• Enable the use of on-premises compute resources.

• Support autoscaling.

You need to configure a compute target for training and inference.

Which compute targets should you configure?

To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point

You previously deployed a model that was trained using a tabular dataset named training-dataset, which is based on a folder of CSV files.

Over time, you have collected the features and predicted labels generated by the model in a folder containing a CSV file for each month. You have created two tabular datasets based on the folder containing the inference data: one named predictions-dataset with a schema that matches the training data exactly, including the predicted label; and another named features-dataset with a schema containing all of the feature columns and a timestamp column based on the filename, which includes the day, month, and year.

You need to create a data drift monitor to identify any changing trends in the feature data since the model was trained. To accomplish this, you must define the required datasets for the data drift monitor.

Which datasets should you use to configure the data drift monitor? To answer, drag the appropriate datasets to the correct data drift monitor options. Each source may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace named workspace1 by using the Python SDK v2.

You must register datastores in workspace1 for Azure Blob and Azure Data Lake Gen2 storage to meet the following requirements:

• Data scientists accessing the datastore must have the same level of access.
• Access must be restricted to specified containers or folders.

You need to configure a security access method used to register the Azure Blob and Azure Data Lake Gen2 storage in workspace1.

Which security access method should you configure? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.:

You plan to run a Python script as an Azure Machine Learning experiment. The script contains the following code:

You plan to run a Python script as an Azure Machine Learning experiment. The script contains the following code:

You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source. You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.

Which code segment should you use?

You must specify a file dataset as an input to the script. The dataset consists of multiple large image files and must be streamed directly from its source. You need to write code to define a ScriptRunConfig object for the experiment and pass the ds dataset as an argument.

Which code segment should you use?

You manage an Azure Machine Learning workspace by using the Python SDK v2.

You must create an automated machine learning job to generate a classification model by using data files stored in Parquet format. You must configure an autoscaling compute

target and a data asset for the job.

You need to configure the resources for the job.

Which resource configuration should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You need to implement a scaling strategy for the local penalty detection data. Which normalization type should you use?

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 an Azure Machine Learning model.

You plan to deploy the model to an online endpoint.

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution: Create a managed online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.

Does the solution meet the goal?

You need to use the Python language to build a sampling strategy for the global penalty detection models. 

How should you complete the code segment? To answer, select the appropriate options in the answer area. 

NOTE: Each correct selection is worth one point.

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 an Azure Machine Learning model.

You plan to deploy the model to an online endpoint.

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution: Create a Kubernetes online endpoint and set the value of its auth_mode parameter to aml_token. Deploy the model to the online endpoint.

Does the solution meet the goal?

You need to define an evaluation strategy for the crowd sentiment models.

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 an Azure Machine Learning model.

You plan to deploy the model to an online endpoint.

You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model.

Solution: Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint.

Does the solution meet the goal?

You need to implement a feature engineering strategy for the crowd sentiment local models. What should you do?

You create a multi-class image classification deep learning model.

The model must be retrained monthly with the new image data fetched from a public web portal. You create an Azure Machine Learning pipeline to fetch new data, standardize the size of images, and retrain the model.

You need to use the Azure Machine Learning Python SDK v2 to configure the schedule for the pipeline. The schedule should be defined by using the frequency and interval properties, with frequency set to “month" and interval set to "1".

Which three classes should you instantiate 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 need to define a modeling strategy for ad response.

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.

You build a custom model you must log with MLflow. The custom model includes the following:

• The model is not natively supported by MLflow.

• The model cannot be serialized in Pickle format.

• The model source code is complex.

• The Python library for the model must be packaged with the model.

You need to create a custom model flavor to enable logging with MLflow.

What should you use?

You need to define an evaluation strategy for the crowd sentiment models.

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.

You manage an Azure Machine Learning workspace named workspace1.

You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the ComputeInstance class.

You need to add the instantiated compute instance to workspace1.

What should you use?

You need to implement a model development strategy to determine a user's tendency to respond to an ad. Which technique should you use?

You manage an Azure Machine Learning workspace. The workspace includes an Azure Machine Learning Kubernetes compute target configured as an Azure Kubernetes Service (AKS) cluster named AKS1. AKS1 is configured to enable the targeting of different nodes to train workloads.

You must run a command job on AKS1 by using the Azure ML Python SDK v2. The command job must select different types of compute nodes. The compute node types must be specified by using a command parameter.

You need to configure the command parameter.

Which parameter should you use?

You need to implement a new cost factor scenario for the ad response models as illustrated in the performance curve exhibit. Which technique should you use?

You create an Azure Machine learning workspace.

You must use the Azure Machine Learning Python SDK v2 to define the search space for discrete hyperparameters. The hyperparameters must consist of a list of predetermined, comma-separated integer values.

You need to import the class from the azure.ai.ml.sweep package used to create the list of values.

Which class should you import?

You manage an Azure Machine Learning workspace named workspace1 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.

Select and Place:

You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

Which three modules should you recommend be used in sequence? 

To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

You manage an Azure Machine Learning workspace named workspace1 with a compute instance named compute1.

You must remove a kernel named kernel1 from compute1. You connect to compute1 by using a terminal window from workspace1.

You need to enter a command in the terminal window to remove kernel.

Which command should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed. Which three Azure Machine Learning Studio modules should you use? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You use the Azure Machine Learning SDK v2 for Python and notebooks to train a model. You use Python code to create a compute target, an environment, and a training script.

You need to prepare information to submit a training job.

Which class should you use?

You need to identify the methods for dividing the data according to the testing requirements. Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

You train and register a model by using the Azure Machine Learning Python SDK v2 on a local workstation. Python 3.7 and Visual Studio Code are installed on the workstation.

When you try to deploy the model into production to a Kubernetes online endpoint, you experience an error in the scoring script that causes deployment to fail.

You need to debug the service on the local workstation before deploying the service to production.

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.

You need to configure the Edit Metadata module so that the structure of the datasets match.

Which configuration options should you select? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You monitor an Azure Machine Learning classification training experiment named train.classification on Azure Notebooks.

You must store a table named table as an artifact in Azure Machine Learning Studio during model training.

You need to collect and list the metrics by using MLflow.

How should you complete the code segment? To answer, select the appropriate option in the answer area.

NOTE: Each correct selection is worth one point.

You need to configure the Permutation Feature Importance module for the model training requirements. What should you do? To answer, select the appropriate options in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.

You use Azure Machine Learning to train a model.

You must use Bayesian sampling to tune hyperparameters.

You need to select a learning_rate parameter distribution.

Which two distributions can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You need to select a feature extraction method. Which method should you use?

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 odd a new Jupyter kernel that will be accessible from the some 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?

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 odd a new Jupyter kernel that will be accessible from the some 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?

You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.

How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area.

NOTE: Each correct selection is worth one point.

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 odd a new Jupyter kernel that will be accessible from the some 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?

You need to select a feature extraction method. Which method should you use?

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 use Azure Machine Learning Designer to load the following datasets into an experiment:

Dataset 1

Dataset2

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Execute Python Script component.

Does the solution meet the goal?

You need to implement an early stopping criteria policy for model training.

Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments 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.

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 use Azure Machine Learning Designer to load the following datasets into an experiment:

Dataset 1

Dataset2

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Join Data component.

Does the solution meet the goal?

You need to implement early stopping criteria as stated in the model training requirements.

Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order.

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

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 use Azure Machine Learning Designer to load the following datasets into an experiment:

Dataset 1

Dataset2

You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.

Solution: Use the Add Rows component.

Does the solution meet the goal?

You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-image classification deep learning model that uses a set of labeled bird photos collected by experts. You plan to use the model to develop a cross-platform mobile app that predicts the species of bird captured by app users.

You must test and deploy the trained model as a web service. The deployed model must meet the following requirements:

• An authenticated connection must not be required for testing.
• The deployed model must perform with low latency during inferencing.

The REST endpoints must be scalable and should have a capacity to handle large number of requests when multiple end users are using the mobile application.

You need to verify that the web service returns predictions in the expected JSON format when a valid REST request is submitted. Which compute resources should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace.

You must provide s for the behavior of the models with feature importance measures.

You need to configure a Responsible Al dashboard in Azure Machine Learning.

Which dashboard component should you configure?

You create a deep learning model for image recognition on Azure Machine Learning service using GPU-based training. You must deploy the model to a context that allows for real-time GPU-based inferencing.

You need to configure compute resources for model inferencing. Which compute type should you use?

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.

You create a batch inference pipeline by using the Azure ML SDK. You run the pipeline by using the following code:

from azureml.pipeline.core import Pipeline from azureml.core.experiment import Experiment

pipeline = Pipeline(workspace=ws, steps=[parallelrun_step]) pipeline_run = Experiment(ws, 'batch_pipeline').submit(pipeline)

You need to monitor the progress of the pipeline execution.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You manage an Azure Machine Learning workspace.

You must create and configure a compute cluster for a training job by using Python SDK v2.

You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.

Which two properties should you define? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You train and register a model in your Azure Machine Learning workspace.

You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

You need to create the inferencing script for the ParallelRunStep pipeline step.

Which two functions should you include? Each correct answer presents part of the solution.

NOTE: Each correct selection is worth one point.

You have an Azure Machine Learning workspace named WSI.

You plan to use the Responsible AI dashboard to assess MLflow models that you will register in WSI.

You need to identify the library you should use to register the MLflow models.

Which library should you use?

You deploy a model as an Azure Machine Learning real-time web service using the following code.

The deployment fails.

You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed. Which code segment should you run?

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?

You deploy a model in Azure Container Instance.

You must use the Azure Machine Learning SDK to call the model API.

You need to invoke the deployed model using native SDK classes and methods.

How should you complete the command? To answer, select the appropriate options in the answer areas.

NOTE: Each correct selection is worth one point.

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?

You create a multi-class image classification deep learning model. You train the model by using PyTorch version 1.2.

You need to ensure that the correct version of PyTorch can be identified for the inferencing environment when the model is deployed. What should you do?

You manage an Azure Machine Learning workspace named workspace1.

You must register an Azure Blob storage datastore in workspace' by using an access key. You develop Python SDK v2 code to import all modules required to register the datastore.

You need to complete the Python SDK v? code to define the datastore.

How should you complete the code? To answer. select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You train a machine learning model.

You must deploy the model as a real-time inference service for testing. The service requires low CPU utilization and less than 48 MB of RAM. The compute target for the deployed service must initialize automatically while minimizing cost and administrative overhead.

Which compute target should you use?

You plan to implement an Azure Machine Learning solution.

You have the following requirements:

• Run a Jupyter notebook to interactively train a machine learning model.

• Deploy assets and workflows for machine learning proof of concept by using scripting rather than custom programming.

You need to select a development technique for each requirement.

Which development technique should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You register a model that you plan to use in a batch inference pipeline.

The batch inference pipeline must use a ParallelRunStep step to process files in a file dataset. The script has the ParallelRunStep step runs must process six input files each time the inferencing function is called.

You need to configure the pipeline.

Which configuration setting should you specify in the ParallelRunConfig object for the PrallelRunStep step?

You create an Azure Machine Learning workspace.

You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a list of numeral metrics.

You need to implement a method to log a list of numeral metrics.

Which method should you use?

You deploy a real-time inference service for a trained model.

The deployed model supports a business-critical application, and it is important to be able to monitor the data submitted to the web service and the predictions the data generates.

You need to implement a monitoring solution for the deployed model using minimal administrative effort. What should you do?

You create an Azure Machine Learning workspace and an Azure Synapse Analytics workspace with a Spark pool. The workspaces are contained within the same Azure subscription.

You must manage the Synapse Spark pool from the Azure Machine Learning workspace.

You need to attach the Synapse Spark pool in Azure Machine Learning by using the Python SDK v2.

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.

You use Azure Machine Learning to train and register a model.

You must deploy the model into production as a real-time web service to an inference cluster named service-compute that the IT department has created in the Azure Machine Learning workspace.

Client applications consuming the deployed web service must be authenticated based on their Azure Active Directory service principal. You need to write a script that uses the Azure Machine Learning SDK to deploy the model. The necessary modules have been imported. How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You create an Azure Machine Learning workspace.

You plan to write an Azure Machine Learning SDK for Python v2 script that logs an image for an experiment. The logged image must be available from the images tab in Azure Machine Learning Studio.

You need to complete the script.

Which code segments should you use? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs.

The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed.

You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort. What should you advise the IT Operations team to do?

You manage an Azure Machine Learning workspace.

You must log multiple metrics by using MLflow.

You need to maximize logging performance.

What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You use Azure Machine Learning designer to create a real-time service endpoint. You have a single Azure Machine Learning service compute resource. You train the model and prepare the real-time pipeline for deployment.

You need to publish the inference pipeline as a web service. Which compute type should you use?

You manage an Azure Machine Learning workspace.

You need to define an environment from a Docker image by using the Azure Machine Learning Python SDK v2.

Which parameter should you use?

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 perform hyperparameter tuning with Azure Machine Learning.

You create the following Python code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

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 AksWebservice instance.

Set the value of the auth_enabled property to True. Deploy the model to the service.

Does the solution meet the goal?

You are running a training experiment on remote compute in Azure Machine Learning (ML) by using Azure ML SDK v2 for Python.

The experiment is configured to use a conda environment that includes all required packages.

You must track metrics generated in the experiment.

You need to complete the script for the experiment.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

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 AksWebservice instance.

Set the value of the auth_enabled property to False.

Set the value of the token_auth_enabled property to True. Deploy the model to the service.

Does the solution meet the goal?

You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features with few distinct values.

You need to control whether automated machine learning automatically imputes missing values and encode categorical features as part of the training task.

Which enum of the automl package should you use?

You use the following Python code in a notebook to deploy a model as a web service:

from azureml.core.webservice import AciWebservice from azureml.core.model import InferenceConfig

inference_config = InferenceConfig(runtime='python', source_directory='model_files', entry_script='score.py', conda_file='env.yml')

deployment_config = AciWebservice.deploy_configuration(cpu_cores=1, memory_gb=1) service = Model.deploy(ws, 'my-service', [model], inference_config, deployment_config) service.wait_for_deployment(True)

The deployment fails.

You need to use the Python SDK in the notebook to determine the events that occurred during service deployment an initialization. Which code segment should you use?

You create an Azure Machine Learning managed compute resource. The compute resource is configured as follows:

• Minimum nodes: 2

• Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

• Minimum nodes: 0

• Maximum nodes: 8

You need to reconfigure the compute resource.

Which three methods can you use? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You use the Azure Machine Learning Python SDK to define a pipeline that consists of multiple steps.

When you run the pipeline, you observe that some steps do not run. The cached output from a previous run is used instead.

You need to ensure that every step in the pipeline is run, even if the parameters and contents of the source directory have not changed since the previous run. What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

NOTE: Each correct selection is worth one point.

You are using hyperparameter tuning in Azure Machine Learning Python SDK v2 to train a model.

You configure the hyperparameter tuning experiment by running the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No.

NOTE: Each correct selection is worth one point.

You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service.

You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment.

You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update. What should you do?

You create an Azure Machine Learning workspace.

You must use the Python SDK v2 to implement an experiment from a Jupyter notebook in the workspace. The experiment must log a table in the following format:

table = {
"col1" : [1, 2, 3],
"col2" : [4, 5, 6]
)

You need to complete the Python code to log the table.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You use Azure Machine Learning designer to create a training pipeline for a regression model.

You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values. What should you do?

You create a workspace to include a compute instance by using Azure Machine Learning Studio. You are developing a Python SDK v2 notebook in the workspace.

You need to use Intellisense in the notebook.

What should you do?

You retrain an existing model.

You need to register the new version of a model while keeping the current version of the model in the registry. What should you do?