DP-100 Designing and Implementing a Data Science Solution on Azurepopular - Practice Questions - Post 6
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3. HOTSPOT (Drag and Drop is not supported) You manage an Azure Machine Learning workspace named workspace1. You must register an Azure Blob storage datastore in workspace1 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 v2 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.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
4. 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?
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Option A: https
Option B: adl
Option C: abfss
Option D: wasbs
Answer(s): 3
Explanation: Not available
5. 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?
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Option A: mltable.from_parquet_files()
Option B: mltable.from_delimited_files()
Option C: mltable.from_data_lake()
Option D: mltable.load()
Answer(s): 2
Explanation: Not available
6. You manage an Azure Machine Learning workspace named proj1. You plan to use assets defined in proj1 to create a pipeline in the Machine Learning studio designer. You need to set the Registry name filter to display only the list of assets defined in proj1. What should you set the Registry name filter to?
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Option A: proj1
Option B: azureml-meta
Option C: azureml
Option D: workspace
Answer(s): 1
Explanation: Not available
7. DRAG DROP (Drag and Drop is not supported) You have an Azure Machine Learning workspace named WS1 and a GitHub account named account1 that hosts a private repository named repo1. You need to clone repo1 to make it available directly from WS1. The configuration must maximize the performance of the repo1 clone. Which four actions should you perform in sequence?
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
8. You manage an Azure Machine Learning workspace. You design a training job that is configured with a serverless compute. The serverless compute must have a specific instance type and count. You need to configure the serverless compute by using Azure Machine Learning Python SDK v2. What should you do?
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Option A: Specify the compute name by using the compute parameter of the command job.
Option B: Configure the tier parameter to Dedicated VM.
Option C: Initialize and specify the ResourceConfiguration class.
Option D: Initialize AmiCompute class with size and type specification.
Answer(s): 3
Explanation: Not available
9. HOTSPOT (Drag and Drop is not supported) You manage an Azure subscription that contains the following resources: You plan to implement a solution that will automatically trigger the retraining of the model implemented by MLPipeline1. The trigger must be invoked if data drift is detected in Dataset1. You need to select the components to invoke and run the solution. The solution must minimize coding implementation and maintenance efforts. Which components should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
10. 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.
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Option A: size
Option B: win_instances
Option C: type
Option D: name
Answer(s): 3,4
Explanation: Not available
11. You manage an Azure Machine Learning workspace named Workspace1. You plan to create a pipeline in the Azure Machine Learning Studio designer. The pipeline must include a custom component. You need to ensure the custom component can be used in the pipeline. What should you do first?
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Option A: Create a pipeline endpoint.
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15. HOTSPOT (Drag and Drop is not supported) You design a data processing strategy for a machine learning project. The data that must be processed includes unstructured flat files that must be processed in real time. The data transformation must be executed on a serverless compute and optimized for big data analytical workloads. You need to select the Azure services for the data science team. Which storage and data processing service should you use? To answer, select the appropriate option in the answer area. NOTE: Each correct selection is worth one point.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
16. DRAG DROP (Drag and Drop is not supported) You manage an Azure Machine Learning workspace named workspace1. You plan to create a registry named registry01 with the help of the following registry.yml (line numbers are used for reference only): You need to use Azure Machine Learning Python SDK v2 with Python 3.10 in a notebook to interact with 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.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
17. DRAG DROP (Drag and Drop is not supported) You have an Azure Machine Learning workspace. You plan to use the terminal to configure a compute instance to run a notebook. You need to add a new R kernel to the compute instance. In which order should you perform the actions? To answer, move all actions from the list of actions to the answer area and arrange them in the correct order.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
18. HOTSPOT (Drag and Drop is not supported) You manage an Azure Machine Learning workspace named Workspace1 and an Azure Blob Storage accessed by using the URL https://storage1.blob.core.windows.net/data1. You plan to create an Azure Blob datastore in Workspace1. The datastore must target the Blob Storage by using Azure Machine Learning Python SDK v2. Access authorization to the datastore must be limited to a specific amount of time. You need to select the parameters of the AzureBlobDatastore class that will point to the target datastore and authorize access to it. Which parameters should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
19. 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 manage an Azure Machine Learning workspace. The development environment for managing the workspace is configured to use Python SDK v2 in Azure Machine Learning Notebooks. A Synapse Spark Compute is currently attached and uses system-assigned identity. You need to use Python code to update the Synapse Spark Compute to use a user-assigned identity. Solution: Pass the UserAssignedIdentity class object to the SynapseSparkCompute class. Does the solution meet the goal?
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Option A: Yes
Option B: No
Answer(s): 1
Explanation: Not available
20. DRAG DROP (Drag and Drop is not supported) You have an Azure Machine Learning workspace named WS1. You plan to use WS1 to train two models named model1 and model2. For model1, you plan to use automated machine learning. For model2, you plan to use Azure Machine Learning designer. You need to determine the compute targets you should use to train each model. Your solution must ensure the following: • The compute target for model1 supports auto-shutdown/auto-start based on a schedule. • The compute target for model2 supports the use of low-priority Azure Virtual Machines. Which compute targets should you use? To answer, move the appropriate compute targets to the correct model. 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.
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation:
21. You manage an Azure Machine Learning Workspace named Workspase1 and an Azure Files share named Share1. You plan to create an Azure Files datastore in Workspace1 to target Share1. You need to configure permanent access to Share1 from the Azure Files datastore. Which authorization method should you use?
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Option A: Secondary access key
Option B: Anonymous access
Option C: Account SAS key
Option D: Service SAS key
Answer(s): 3
Explanation: Not available
22. You are analyzing a dataset containing historical data from a local taxi company. You are developing a regression model. You must predict the fare of a taxi trip. You need to select performance metrics to correctly evaluate the regression model. Which two metrics can you use? Each correct answer presents a complete solution? NOTE: Each correct selection is worth one point.
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Option A: a Root Mean Square Error value that is low
Option B: an R-Squared value close to 0
Option C: an F1 score that is low
Option D: an R-Squared value close to 1
Answer(s): 1,4
Explanation: RMSE and R2 are both metrics for regression models. A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction. D: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect. Incorrect Answers: C, E: F-score is used for classification models, not for regression models.
23. 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 using Azure Machine Learning to run an experiment that trains a classification model. You want to use Hyperdrive to find parameters that optimize the AUC metric for the model. You configure a HyperDriveConfig for the experiment by running the following code: You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted. You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric. Solution: Run the following code: Does the solution meet the goal?
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27. You write five Python scripts that must be processed in the order specified in Exhibit A `" which allows the same modules to run in parallel, but will wait for modules with dependencies. You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names. You need to create the pipeline shown. Assume all relevant imports have been done. Which Python code segment should you use?
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Image for Option D: 
Answer(s): 1
Explanation: The steps parameter is an array of steps. To build pipelines that have multiple steps, place the steps in order in this array.
28. You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored. You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator. The script includes the following code to read data from the csv_files folder: You have the following script. You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore. Which code should you use to configure the estimator?
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Answer(s): 2
Explanation: Besides passing the dataset through the input parameters in the estimator, you can also pass the dataset through script_params and get the data path (mounting point) in your training script via arguments. This way, you can keep your training script independent of azureml-sdk. In other words, you will be able use the same training script for local debugging and remote training on any cloud platform. Example: from azureml.train.sklearn import SKLearn script_params = { # mount the dataset on the remote compute and pass the mounted path as an argument to the training script '--data-folder': mnist_ds.as_named_input('mnist').as_mount(), '--regularization': 0.5 } est = SKLearn(source_directory=script_folder, script_params=script_params, compute_target=compute_target, environment_definition=env, entry_script='train_mnist.py') # Run the experiment run = experiment.submit(est) run.wait_for_completion(show_output=True) Incorrect Answers: A: Pandas DataFrame not used.
29. DRAG DROP (Drag and Drop is not supported) 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. Select and Place:
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Option A: See Explanation section for answer.
Answer(s): 1
Explanation: Step 1: Configure a DataTransferStep() to fetch new image dataג€¦ Step 2: Configure a PythonScriptStep() to run image_resize.y on the cpu-compute compute target. Step 3: Configure the EstimatorStep() to run training script on the gpu_compute computer target. The PyTorch estimator provides a simple way of launching a PyTorch training job on a compute target.
30. 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. An IT department creates the following Azure resource groups and resources: The IT department creates an Azure Kubernetes Service (AKS)-based inference compute target named aks-cluster in the Azure Machine Learning workspace. You have a Microsoft Surface Book computer with a GPU. Python 3.6 and Visual Studio Code are installed. You need to run a script that trains a deep neural network (DNN) model and logs the loss and accuracy metrics. Solution: Attach the mlvm virtual machine as a compute target in the Azure Machine Learning workspace. Install the Azure ML SDK on the Surface Book and run Python code to connect to the workspace. Run the training script as an experiment on the mlvm remote compute resource. Does the solution meet the goal?
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Option A: Yes
Option B: No
Answer(s): 1
Explanation: Use the VM as a compute target. Note: A compute target is a designated compute resource/environment where you run your training script or host your service deployment. This location may be your local machine or a cloud-based compute resource.
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