Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 121:

  DRAG DROP

  You are creating an experiment by using Azure Machine Learning Studio.

  You must divide the data into four subsets for evaluation. There is a high degree of missing values in the data. You must prepare the data for analysis.

  You need to select appropriate methods for producing the experiment.

  Which three modules 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.

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

  Select and Place:

  

  

  ---

  The Clean Missing Data module in Azure Machine Learning Studio, to remove, replace, or infer missing values.

  Incorrect Answers:

  Latent Direchlet Transformation: Latent Dirichlet Allocation module in Azure Machine Learning Studio, to group otherwise unclassified text into a number of categories. Latent Dirichlet Allocation (LDA) is often used in natural language processing (NLP) to find texts that are similar. Another common term is topic modeling.

  Build Counting Transform: Build Counting Transform module in Azure Machine Learning Studio, to analyze training data. From this data, the module builds a count table as well as a set of count-based features that can be used in a predictive model.

  Missing Value Scrubber: The Missing Values Scrubber module is deprecated.

  Feature hashing: Feature hashing is used for linguistics, and works by converting unique tokens into integers.

  Replace discrete values: the Replace Discrete Values module in Azure Machine Learning Studio is used to generate a probability score that can be used to represent a discrete value. This score can be useful for understanding the information value of the discrete values.

  References:

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

• Question 122:

  You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.

  You submit the experiment with the following code:

  

  You need to create Python code that returns the best model that is generated by the automated machine learning task.

  Which code segment should you use?

  A. best_model = automl_run.get_details()
  B. best_model = automl_run.get_metrics()
  C. best_model = automl_run.get_file_names()[1]
  D. best_model = automl_run.get_output()[1]
  D. best_model = automl_run.get_output()[1]

  ---

  Explanation

  The get_output method returns the best run and the fitted model.

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/automated-machine-learning/classification/auto-ml-classification.ipynb

• Question 123:

  HOTSPOT

  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.

  

  

  ---

  Explanation:

  Box 1: User identity-based access Azure Blob Storage

  Create and register datastores

  When you register a storage service on Azure as a datastore, you automatically create and register that datastore to a specific workspace. See Storage access permissions for guidance on required permission types. You can also manually create the storage you want to connect to without any special permissions, and you just need the name.

  In the following code, notice the absence of authentication parameters like sas_token, account_key, subscription_id, and the service principal client_id. This omission indicates that Azure Machine Learning will use identity-based data access for authentication. Creation of datastores typically happens interactively in a notebook or via the studio. So your Azure Active Directory token is used for data access authentication.

  Azure blob container

  To register an Azure blob container as a datastore, use register_azure_blob_container().

  The following code creates the credentialless_blob datastore, registers it to the ws workspace, and assigns it to the blob_datastore variable. This datastore accesses the my_container_name blob container on the my-account-name storage account.

  # Create blob datastore without credentials.

  blob_datastore = Datastore.register_azure_blob_container(workspace=ws, datastore_name='credentialless_blob', container_name='my_container_name', account_name='my_account_name')

  Box 2: Managed identity

  Model training on private data

  Certain machine learning scenarios involve training models with private data. In such cases, data scientists need to run training workflows without being exposed to the confidential input data. In this scenario, a managed identity of the training compute is used for data access authentication. This approach allows storage admins to grant Storage Blob Data Reader access to the managed identity that the training compute uses to run the training job. The individual data scientists don't need to be granted access.

  Prerequisites include

  * Azure subscription

  * An Azure storage account with a supported storage type. These storage types are supported:

  Azure Blob Storage

  Azure Data Lake Storage Gen1

  Azure Data Lake Storage Gen2

  Azure SQL Database

  The Azure Machine Learning SDK for Python.

  * An Azure Machine Learning workspace.

  Note: Azure Data Lake Storage Gen2

  Use register_azure_data_lake_gen2() to register a datastore that connects to Azure Data Lake Storage Gen2.

  The following code creates the credentialless_adls2 datastore, registers it to the ws workspace, and assigns it to the adls2_dstore variable. This datastore accesses the file system tabular in the myadls2 storage account.

  # Create Azure Data Lake Storage Gen2 datastore without credentials.

  adls2_dstore = Datastore.register_azure_data_lake_gen2(workspace=ws, datastore_name='credentialless_adls2', filesystem='tabular', account_name='myadls2')

  https://learn.microsoft.com/en-us/azure/machine-learning/v1/how-to-identity-based-data-access

• Question 124:

  You create a script that trains a convolutional neural network model over multiple epochs and logs the validation loss after each epoch. The script includes arguments for batch size and learning rate.

  You identify a set of batch size and learning rate values that you want to try.

  You need to use Azure Machine Learning to find the combination of batch size and learning rate that results in the model with the lowest validation loss.

  What should you do?

  A. Run the script in an experiment based on an AutoMLConfig object
  B. Create a PythonScriptStep object for the script and run it in a pipeline
  C. Use the Automated Machine Learning interface in Azure Machine Learning studio
  D. Run the script in an experiment based on a ScriptRunConfig object
  E. Run the script in an experiment based on a HyperDriveConfig object
  E. Run the script in an experiment based on a HyperDriveConfig object

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-tune-hyperparameters

• Question 125:

  You manage an Azure Machine Learning workspace named workspace1.

  You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2.

  You must convert Python code into a YAML specification for pipeline processing.

  You need to import the Python library that provides the transformation functionality.

  Which Python library provides this functionality?

  A. azure.ai.ml.automl
  B. azure.ai.ml.entities
  C. sklearn
  D. mldesigner
  D. mldesigner

• Question 126:

  HOTSPOT

  You have a binary classifier that predicts positive cases of diabetes within two separate age groups.

  The classifier exhibits a high degree of disparity between the age groups. You need to modify the output of the classifier to maximize its degree of fairness across the age groups and meet the following requirements:

  1. Eliminate the need to retrain the model on which the classifier is based.

  2. Minimize the disparity between true positive rates and false positive rates across age groups.

  Which algorithm and panty constraint should you use? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

  

  

• Question 127:

  You have a dataset that includes confidential data. You use the dataset to train a model.

  You must use a differential privacy parameter to keep the data of individuals safe and private.

  You need to reduce the effect of user data on aggregated results.

  What should you do?

  A. Decrease the value of the epsilon parameter to reduce the amount of noise added to the data
  B. Increase the value of the epsilon parameter to decrease privacy and increase accuracy
  C. Decrease the value of the epsilon parameter to increase privacy and reduce accuracy
  D. Set the value of the epsilon parameter to 1 to ensure maximum privacy
  C. Decrease the value of the epsilon parameter to increase privacy and reduce accuracy

  ---

  Explanation

  Differential privacy tries to protect against the possibility that a user can produce an indefinite number of reports to eventually reveal sensitive data. A value known as epsilon measures how noisy, or private, a report is. Epsilon has an inverse relationship to noise or privacy. The lower the epsilon, the more noisy (and private) the data is.

  https://docs.microsoft.com/en-us/azure/machine-learning/concept-differential-privacy

• Question 128:

  DRAG DROP

  You manage an Azure Machine Learning workspace. You build automated machine learning training experiments for computer vision models.

  You need to use a primary metric for model optimization and hyperparameter tuning for each model.

  Which primary metrics should you use for the models? To answer, move the appropriate primary metrics to the correct computer vision models. You may use each primary metric 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 129:

  You have a dataset that is stored m an Azure Machine Learning workspace.

  You must perform a data analysis for differentiate privacy by using the SmartNoise SDK.

  You need to measure the distribution of reports for repeated queries to ensure that they are balanced.

  Which type of test should you perform?

  A. Bias
  B. Accuracy
  C. Privacy
  D. Utility
  A. Bias

• Question 130:

  You manage an Azure Machine Learning Workspace named Workspace1 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?

  A. Primary access key
  B. Anonymous access
  C. Account SAS key
  D. User delegation SAS key
  C. Account SAS key