Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 451:

  You have an Azure Machine Learning workspace named WS1.

  You plan to use Azure Machine Learning SDK v2 to register a model as an asset in WS1 from an artifact generated by an MLflow run. The artifact resides in a named output of a job used for the model training.

  You need to identify the syntax of the path to reference the model when you register it.

  Which syntax should you use?

  A. t//model/
  B. azureml://registries
  C. mlflow-model/
  D. azureml://jobs/
  D. azureml://jobs/

• Question 452:

  You use the Azure Machine learning SDK foe Python to create a pipeline that includes the following step:

  The output of the step run must be cached and reused on subsequent runs when the source.directory value has not changed.

  You need to define the step.

  What should you include in the step definition?

  A. allow.reuse
  B. hash_path
  C. data-as_input(name-..)
  D. version
  A. allow.reuse

• Question 453:

  HOTSPOT

  You create an Azure Machine Learning workspace. You use the Azure Machine Learning Python SDK v2 to create a compute cluster.

  The compute cluster must run a training script. Costs associated with running the training script must be minimized.

  You need to complete the Python script to create the compute cluster.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: AmlCompute

  Create compute cluster, Python SDK v2

  Example:

  from azure.ai.ml.entities import AmlCompute cpu_cluster_name = "cpucluster" cluster_basic = AmlCompute( name=cpu_cluster_name, type="amlcompute", size="STANDARD_DS3_v2", max_instances=4, # Minimum running nodes when there is no job running min_instances=0 ) ml_client.begin_create_or_update(cluster_basic)

  Box 2: min_instances=0

  Incorrect:

  * tier= "LowPriority"

  * min_instances=1 Reference:

  https://learn.microsoft.com/en-us/azure/machine-learning/migrate-to-v2-resource-compute

  https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-train-model

• Question 454:

  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?

  A. Specify the compute name by using the compute parameter of the command job
  B. Configure the tier parameter to Dedicated VM.
  C. Initialize and specify the ResourceConfiguration class
  D. Initialize AmICompute class with size and type specification.
  C. Initialize and specify the ResourceConfiguration class

• Question 455:

  DRAG DROP

  You are analyzing a raw dataset that requires cleaning.

  You must perform transformations and manipulations by using Azure Machine Learning Studio.

  You need to identify the correct modules to perform the transformations.

  Which modules should you choose? To answer, drag the appropriate modules to the correct scenarios. Each module 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.

  Select and Place:

  

  

  ---

  Box 1: Clean Missing Data

  Box 2: SMOTE

  Use the SMOTE module in Azure Machine Learning Studio 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.

  Box 3: Convert to Indicator Values

  Use the Convert to Indicator Values module in Azure Machine Learning Studio. The purpose of this module is to convert columns that contain categorical values into a series of binary indicator columns that can more easily be used as features in a machine learning model.

  Box 4: Remove Duplicate Rows

  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/convert-to-indicator-values

• Question 456:

  You are developing a machine learning model.

  You must inference the machine learning model for testing.

  You need to use a minimal cost compute target

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

  NOTE: Each correct selection is worth one point

  A. Local web service
  B. Remote VM
  C. Azure Databricks
  D. Azure Machine Learning Kubernetes
  E. Azure Container Instances
  A. Local web service
  E. Azure Container Instances

• Question 457:

  You are solving a classification task.

  The dataset is imbalanced.

  You need to select an Azure Machine Learning Studio module to improve the classification accuracy.

  Which module should you use?

  A. Permutation Feature Importance
  B. Filter Based Feature Selection
  C. Fisher Linear Discriminant Analysis
  D. Synthetic Minority Oversampling Technique (SMOTE)
  D. Synthetic Minority Oversampling Technique (SMOTE)

  ---

  Explanation

  Use the SMOTE module in Azure Machine Learning Studio (classic) 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.

  You connect the SMOTE module to a dataset that is imbalanced. There are many reasons why a dataset might be imbalanced: the category you are targeting might be very rare in the population, or the data might simply be difficult to collect.

  Typically, you use SMOTE when the class you want to analyze is under- represented.

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

• Question 458:

  HOTSPOT

  You use Azure Machine Learning to implement hyperparameter tuning for an Azure ML Python SDK v2-based model training.

  Training runs must terminate when the primary metric is lowered by 25 percent or more compared to the best performing run.

  You need to configure an early termination policy to terminate training jobs.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 459:

  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
  A. Yes

• Question 460:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You need to use the Azure Machine Learning SDK for Python to create and register the Azure Data Lake Storage Generation 2 datastore for the workspace.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: register_azure_data_lake_gen

  For an Azure Data Lake Storage Generation 2 (ADLS Gen 2) datastore, use register_azure_data_lake_gen2() to register a credential datastore connected to an Azure DataLake Gen 2 storage with service principal permissions.

  Box 2: filesystem='test'

  Example code:

  adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(workspace=ws, datastore_name=adlsgen2_datastore_name, account_name=account_name, # ADLS Gen2 account name filesystem='test', # ADLS Gen2 filesystem tenant_id=tenant_id, # tenant id of service principal client_id=client_id, # client id of service principal client_secret=client_secret) # the secret of service principal

  Note: The following code creates and registers the adlsgen2_datastore_name datastore to the ws workspace. This datastore accesses the file system test in the account_name storage account, by using the provided service principal credentials. Review the storage access & permissions section for guidance on virtual network scenarios, and where to find required authentication credentials.

  adlsgen2_datastore_name = 'adlsgen2datastore' subscription_id=os.getenv("ADL_SUBSCRIPTION", "<my_subscription_id>") # subscription id of ADLS account resource_group=os.getenv("ADL_RESOURCE_GROUP", "<my_resource_group>") # resource group of ADLS account

  account_name=os.getenv("ADLSGEN2_ACCOUNTNAME", "<my_account_name>") # ADLS Gen2 account name tenant_id=os.getenv("ADLSGEN2_TENANT", "<my_tenant_id>") # tenant id of service principal client_id=os.getenv("ADLSGEN2_CLIENTID", "<my_client_id>") # client id of service principal client_secret=os.getenv("ADLSGEN2_CLIENT_SECRET", "<my_client_secret>") # the secret of service principal adlsgen2_datastore = Datastore.register_azure_data_lake_gen2(workspace=ws, datastore_name=adlsgen2_datastore_name, account_name=account_name, # ADLS Gen2 account name filesystem='test', # ADLS Gen2 filesystem tenant_id=tenant_id, # tenant id of service principal client_id=client_id, # client id of service principal client_secret=client_secret) # the secret of service principal

  https://github.com/MicrosoftDocs/azure-docs/blob/main/articles/machine-learning/v1/how-to-access-data.md