Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 71:

  You have an Azure Machine Learning workspace.

  You plan to implement automated hyperparameter tuning for model training in the workspace.

  You need to select the sweep job parameter sampling method that randomizes the selection of hyperparameters from the search space while allowing reproducibility.

  Which sampling method should you use?

  A. Bayesian
  B. Random
  C. Sobol
  D. Grid
  D. Grid

• Question 72:

  DRAG DROP

  You manage an Azure Machine Learning workspace that has an Azure Machine Learning datastore.

  Data must be loaded from the following sources:

  1. a credential-less Azure Blob Storage

  2. an Azure Data Lake Storage (ADLS) Gen 2 which is not a credential-less datastore

  You need to define the authentication mechanisms to access data in the Azure Machine Learning datastore.

  Which data access mechanism should you use? To answer, move the appropriate data access mechanisms to the correct storage types. You may use each data access mechanism 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.

  Select and Place:

  

  

• Question 73:

  You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.

  You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.

  You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

  What should you do?

  A. Do not specify an environment in the run configuration for the experiment. Run the experiment by using the default environment.
  B. Create a virtual machine (VM) with the required Python configuration and attach the VM as a compute target. Use this compute target for all experiment runs.
  C. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.
  D. Create a config.yaml file defining the conda packages that are required and save the file in the experiment folder.
  E. Always run the experiment with an Estimator by using the default packages.
  C. Create and register an Environment that includes the required packages. Use this Environment for all experiment runs.

  ---

  Explanation

  If you have an existing Conda environment on your local computer, then you can use the service to create an environment object. By using this strategy, you can reuse your local interactive environment on remote runs.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-environments

• Question 74:

  HOTSPOT

  You have an Azure Machine Learning workspace and a data source file ./data/cc_data.csv in local storage.

  You plan to use Azure Machine Learning Python SDK v2 to store the content of the cc_data.csv file in a data asset named cc_data_asset in the workspace.

  You write code to connect to the workspace and import all required libraries.

  You need to complete the remaining code to ensure that it results in the cc_data_asset that contains the data from cc_data.csv.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 75:

  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 new experiment in Azure Machine Learning Studio.

  One class has a much smaller number of observations than the other classes in the training set.

  You need to select an appropriate data sampling strategy to compensate for the class imbalance.

  Solution: You use the Principal Components Analysis (PCA) sampling mode.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.

  Note: SMOTE is used to increase the number of underepresented cases in a dataset used for machine learning. SMOTE is a better way of increasing the number of rare cases than simply duplicating existing cases.

  Incorrect Answers:

  The Principal Component Analysis module in Azure Machine Learning Studio (classic) is used to reduce the dimensionality of your training data. The module analyzes your data and creates a reduced feature set that captures all the information contained in the dataset, but in a smaller number of features.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/smote

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/principal-component-

  analysis

• Question 76:

  You have an Azure Machine Learning workspace. You build a deep learning model.

  You need to publish a GPU-enabled model as a web service.

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

  NOTE: Each correct selection is worth one point.

  A. Azure Kubernetes Service (AKS)
  B. Azure Container Instances (ACI)
  C. Local web service
  D. Azure Machine Learning compute clusters
  A. Azure Kubernetes Service (AKS)
  B. Azure Container Instances (ACI)

• Question 77:

  HOTSPOT

  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.

  

  

  ---

  Explanation:

  Box 1: log_metrics

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

  mlflow.log_metric() logs a single key-value metric. The value must always be a number. MLflow remembers the history of values for each metric. Use mlflow.log_metrics() to log multiple metrics at once.

  Box 2: log_artifact

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

  mlflow.log_artifact() logs a local file or directory as an artifact, optionally taking an artifact_path to place it in within the run's artifact URI. Run artifacts can be organized into directories, so you can place the artifact in a directory this way.

  Logging files

  In general, files in MLflow are called artifacts. You can log artifacts in multiple ways in Mlflow:

  - Log a trivial file already existing mlflow.log_artifact("path/to/file.pkl")

  Files are always logged in the root of the run. If artifact_path is provided, then the file is logged in a folder as indicated in that parameter.

  - Log all the artifacts in an existing folder mlflow.log_artifacts("path/to/folder")

  Folder structure is copied to the run, but the root folder indicated is not included.

  Incorrect:

  * log_image

  Logging images MLflow supports two ways of logging images. Both of them persists the given image as an artifact inside of the run.

  - Log numpy metrics or PIL image objects mlflow.log_image(img, "figure.png") img should be an instance of numpy.ndarray or PIL.Image.Image. figure.png is the name of the artifact that will be generated inside of the run. It doesn't have to be an existing file.

  - Log matlotlib plot or image file mlflow.log_figure(fig, "figure.png")

  figure.png is the name of the artifact that will be generated inside of the run. It doesn't have to be an existing file.

  * log_metric

  For example, the following code snippet demonstrates configuring the experiment, and then logging during a job:

  import mlflow

  # Set the experiment mlflow.set_experiment("mlflow-experiment")

  # Start the run mlflow_run = mlflow.start_run()

  # Log metrics or other information mlflow.log_metric('mymetric', 1)

  # End run

  mlflow.end_run() Box 3: mlflow_run.info.run_id

  View metrics and artifacts in your workspace

  The metrics and artifacts from MLflow logging are tracked in your workspace. To view them anytime, navigate to your workspace and find the experiment by name in your workspace in Azure Machine Learning studio. Or run the below code.

  Retrieve run metric using MLflow get_run().

  from mlflow.tracking import MlflowClient

  # Use MlFlow to retrieve the run that was just completed client = MlflowClient() run_id = mlflow_run.info.run_id finished_mlflow_run = MlflowClient().get_run(run_id) metrics = finished_mlflow_run.data.metrics tags = finished_mlflow_run.data.tags params = finished_mlflow_run.data.params

  print(metrics,tags,params)

  https://mlflow.org/docs/latest/tracking.html

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-log-view-metrics

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-use-mlflow

• Question 78:

  You manage an Azure Machine Learning workspace. You develop a machine learning model.

  You must deploy the model to use a low-priority VM with a pricing discount.

  You need to deploy the model.

  Which compute target should you use?

  A. Azure Kubernetes Service (AKS)
  B. Azure Machine Learning compute clusters
  C. Azure Container Instances (ACI)
  D. Local deployment
  B. Azure Machine Learning compute clusters

  ---

  Explanation

  How batch deployment works with low priority VMs

  Azure Machine Learning Batch Deployments provides several capabilities that make it easy to consume and benefit from low priority VMs:

  * Batch deployment jobs consume low priority VMs by running on Azure Machine Learning compute clusters created with low priority VMs. Once a deployment is associated with a low priority VMs' cluster, all the jobs produced by such deployment will use low priority VMs. Per-job configuration is not possible.

  * Batch deployment jobs automatically seek the target number of VMs in the available compute cluster based on the number of tasks to submit. If VMs are preempted or unavailable, batch deployment jobs attempt to replace the lost capacity by queuing the failed tasks to the cluster.

  * Etc.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-use-low-priority-batch

• Question 79:

  DRAG DROP

  You are managing an Azure Machine Learning workspace.

  You must tune a hyperparameter for a neural network model. The learning rate must be a continuous hyperparameter between 0.001 and 0.1. The batch size can be 32, 64, or 128.

  You need to select the appropriate search space for each parameter.

  Which search space should you use? To answer, move the appropriate search spaces to the correct hyperparameters. You may use each search space option 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.

  Select and Place:

  

  

• Question 80:

  You create an Azure Machine Learning workspace.

  You must create a custom role named DataScientist that meets the following requirements:

  1. Role members must not be able to delete the workspace.

  2. Role members must not be able to create, update, or delete compute resource in the workspace.

  3. Role members must not be able to add new users to the workspace.

  You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

  The custom role must enforce the restrictions specified by the IT Operations team.

  Which JSON code segment should you use?

  

  

  A. Option A
  B. Option B
  C. Option C
  D. Option D
  A. Option A

  ---

  Explanation

  The following custom role can do everything in the workspace except for the following actions:

  1. It can't create or update a compute resource.

  2. It can't delete a compute resource.

  3. It can't add, delete, or alter role assignments.

  4. It can't delete the workspace.

  To create a custom role, first construct a role definition JSON file that specifies the permission and scope for the role. The following example defines a custom role named "Data Scientist Custom" scoped at a specific workspace level:

  data_scientist_custom_role.json :

  {

  "Name": "Data Scientist Custom",

  "IsCustom": true,

  "Description": "Can run experiment but can't create or delete compute.", "Actions": ["*"],

  "NotActions": [

  "Microsoft.MachineLearningServices/workspaces/*/delete", "Microsoft.MachineLearningServices/workspaces/write", "Microsoft.MachineLearningServices/workspaces/computes/*/write", "Microsoft.MachineLearningServices/workspaces/computes/*/delete", "Microsoft.Authorization/*/write" ],

  "AssignableScopes": [

  "/subscriptions/<subscription_id>/resourceGroups/<resource_group_name>/providers/ Microsoft.MachineLearningServices/workspaces/<workspace_name>" ] }

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-assign-roles