Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 401:

  HOTSPOT

  You are authoring a pipeline by using the Azure Machine Learning SDK for Python. You implement code to import all relevant classes, configure the workspace, and define all pipeline steps.

  You need to initiate pipeline execution.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Explanation:

  Box 1: Experiment

  After you define your steps, you build the pipeline by using some or all of those steps.

  # Build the pipeline pipeline1 = Pipeline(workspace=ws, steps=[compare_models])

  Submit the pipeline

  When you submit the pipeline, Azure Machine Learning checks the dependencies for each step and uploads a snapshot of the source directory you specified. If no source directory is specified, the current local directory is uploaded. The snapshot is also stored as part of the experiment in your workspace.

  from azureml.core import Experiment

  # Submit the pipeline to be run pipeline_run1 = Experiment(ws, 'Compare_Models_Exp').submit(pipeline1) pipeline_run1.wait_for_completion()

  Box 2: submit

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

• Question 402:

  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 a data scientist using Azure Machine Learning Studio.

  You need to normalize values to produce an output column into bins to predict a target column.

  Solution: Apply a Quantiles normalization with a QuantileIndex normalization.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Use the Entropy MDL binning mode which has a target column.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/group-data-into-bins

• Question 403:

  You build a data pipeline in an Azure Machine Learning workspace by using the Azure Machine Learning SDK for Python.

  You need to run a Python script as a pipeline step.

  Which two classes could you use? Each correct answer presents a complete solution.

  NOTE: Each correct selection is worth one point.

  A. PythonScriptStep
  B. AutoMLStep
  C. CommandStep
  D. StepRun
  A. PythonScriptStep
  C. CommandStep

• Question 404:

  HOTSPOT

  Complete the sentence by selecting the correct option in the answer area.

  

  

  ---

  Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns.

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

• Question 405:

  You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step.

  You must update the content of the downstream data source of pipeline1 and run the pipeline again.

  You need to ensure the new run of pipeline1 fully processes the updated content.

  Solution: Set the regenerate_outputs parameter of the pipeline1 experiment's run submit method to True.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes

  ---

  Explanation

  There are a number of optional settings for a Pipeline which can be specified on submission in the submit.

  regenerate_outputs: Whether to force regeneration of all step outputs and disallow data reuse for this run, default is False.

  If False, this run may reuse results from previous runs and subsequent runs may reuse the results of this run.

  https://learn.microsoft.com/en-us/python/api/azureml-pipeline-core/azureml.pipeline.core.pipeline(class)

• Question 406:

  HOTSPOT

  You create a new Azure Machine Learning workspace with a compute cluster.

  You need to create the compute cluster asynchronously by using the Azure Machine Learning Python SDK v2.

  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.

  

  

  ---

  Explanation:

  Box 1: AmlCompute

  To create a persistent Azure Machine Learning Compute resource in Python, specify the size and max_instances properties. Azure Machine Learning then uses smart defaults for the other properties.

  Example:

  from azure.ai.ml.entities import AmlCompute cluster_basic = AmlCompute( name="basic-example", type="amlcompute", size="STANDARD_DS3_v2", location="westus", min_instances=0, max_instances=2, idle_time_before_scale_down=120, ) ml_client.begin_create_or_update(cluster_basic).result()

  You can also configure several advanced properties when you create Azure Machine Learning Compute. The properties allow you to create a persistent cluster of fixed size, or within an existing Azure Virtual Network in your subscription. See the AmlCompute class for details.

  Box 2: ml_client.begin_create_or_update

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-create-attach-compute-cluster

• Question 407:

  You need to implement a model development strategy to determine a user's tendency to respond to an ad.

  Which technique should you use?

  A. Use a Relative Expression Split module to partition the data based on centroid distance.
  B. Use a Relative Expression Split module to partition the data based on distance travelled to the event.
  C. Use a Split Rows module to partition the data based on distance travelled to the event.
  D. Use a Split Rows module to partition the data based on centroid distance.
  A. Use a Relative Expression Split module to partition the data based on centroid distance.

  ---

  Explanation

  Split Data partitions the rows of a dataset into two distinct sets.

  The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression.

  Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date.

  Scenario:

  Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement.

  The distribution of features across training and production data are not consistent

  References:

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

• Question 408:

  You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:

  1. Minimum nodes: 2

  2. Maximum nodes: 4

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

  1. Minimum nodes: 0

  2. Maximum nodes: 8

  You need to reconfigure the compute resource.

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

  NOTE: Each correct selection is worth one point.

  A. Use the Azure Machine Learning studio.
  B. Run the update method of the AmlCompute class in the Python SDK.
  C. Use the Azure portal.
  D. Use the Azure Machine Learning designer.
  E. Run the refresh_state() method of the BatchCompute class in the Python SDK.
  A. Use the Azure Machine Learning studio.
  B. Run the update method of the AmlCompute class in the Python SDK.
  C. Use the Azure portal.

  ---

  Explanation

  A: You can manage assets and resources in the Azure Machine Learning studio.

  B: The update(min_nodes=None, max_nodes=None, idle_seconds_before_scaledown=None) of the AmlCompute class updates the ScaleSettings for this AmlCompute target.

  C: To change the nodes in the cluster, use the UI for your cluster in the Azure portal.

  https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class)

• Question 409:

  DRAG DROP

  You use Azure Machine Learning to deploy a model as a real-time web service.

  You need to create an entry script for the service that ensures that the model is loaded when the service starts and is used to score new data as it is received.

  Which functions should you include in the script? To answer, drag the appropriate functions to the correct actions. Each function 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: init()

  The entry script has only two required functions, init() and run(data). These functions are used to initialize the service at startup and run the model using request data passed in by a client. The rest of the script handles loading and running the model(s).

  Box 2: run()

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-existing-model

• Question 410:

  You create a machine learning model by using the Azure Machine Learning designer. You publish the model as a real-time service on an Azure Kubernetes Service (AKS) inference compute cluster. You make no change to the deployed endpoint configuration.

  You need to provide application developers with the information they need to consume the endpoint.

  Which two values should you provide to application developers? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. The name of the AKS cluster where the endpoint is hosted.
  B. The name of the inference pipeline for the endpoint.
  C. The URL of the endpoint.
  D. The run ID of the inference pipeline experiment for the endpoint.
  E. The key for the endpoint.
  C. The URL of the endpoint.
  E. The key for the endpoint.

  ---

  Explanation

  Deploying an Azure Machine Learning model as a web service creates a REST API endpoint. You can send data to this endpoint and receive the prediction returned by the model.

  You create a web service when you deploy a model to your local environment, Azure Container Instances, Azure Kubernetes Service, or field-programmable gate arrays (FPGA). You retrieve the URI used to access the web service by using the Azure Machine Learning SDK. If authentication is enabled, you can also use the SDK to get the authentication keys or tokens.

  Example:

  # URL for the web service scoring_uri = '<your web service URI>'

  # If the service is authenticated, set the key or token key = '<your key or token>'

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-consume-web-service