Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 91:

  HOTSPOT

  You are developing a linear regression model in Azure Machine Learning Studio. You run an experiment to compare different algorithms.

  The following image displays the results dataset output:

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the image.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Boosted Decision Tree Regression

  Mean absolute error (MAE) measures how close the predictions are to the actual outcomes; thus, a lower score is better.

  Box 2:

  Online Gradient Descent: If you want the algorithm to find the best parameters for you, set Create trainer mode option to Parameter Range. You can then specify multiple values for the algorithm to try.

  References:

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

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

• Question 92:

  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 have a Python script named train.py in a local folder named scripts. The script trains a regression model by using scikit-learn. The script includes code to load a training data file which is also located in the scripts folder.

  You must run the script as an Azure ML experiment on a compute cluster named aml-compute.

  You need to configure the run to ensure that the environment includes the required packages for model training. You have instantiated a variable named aml-compute that references the target compute cluster.

  Solution: Run the following code:

  

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  The scikit-learn estimator provides a simple way of launching a scikit-learn training job on a compute target. It is implemented through the SKLearn class, which can be used to support single-node CPU training.

  Example:

  from azureml.train.sklearn import SKLearn

  } estimator = SKLearn(source_directory=project_folder, compute_target=compute_target, entry_script='train_iris.py' )

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-scikit-learn

• Question 93:

  You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model.

  You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized.

  You need to define the estimator that will be used to run the script.

  Which estimator type should you use?

  A. TensorFlow
  B. PyTorch
  C. SKLearn
  D. Estimator
  B. PyTorch

  ---

  Explanation

  For PyTorch, TensorFlow and Chainer tasks, Azure Machine Learning provides respective PyTorch, TensorFlow, and Chainer estimators to simplify using these frameworks.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-train-ml-models

• Question 94:

  HOTSPOT

  You use Azure Machine Learning to implement hyperparameter tuning with a Bandit early termination policy.

  The policy uses a slack_factor set to 01. an evaluation interval set to 1, and an evaluation delay set to b.

  You need to evaluate the outcome of the early termination policy

  What should you evaluate? To answer, select the appropriate options m the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 95:

  You create an Azure Machine learning workspace.

  You must use the Azure Machine Learning Python SDK v2 to define the search space for discrete hyperparameters. The hyperparameters must consist of a list of predetermined, comma-separated integer values.

  You need to import the class from the azure.ai.ml.sweep package used to create the list of values.

  Which class should you import?

  A. Choice
  B. Randint
  C. Uniform
  D. Normal
  A. Choice

  ---

  Explanation

  Define the search space

  Tune hyperparameters by exploring the range of values defined for each hyperparameter.

  Hyperparameters can be discrete or continuous, and has a distribution of values described by a parameter expression.

  Discrete hyperparameters

  Discrete hyperparameters are specified as a Choice among discrete values. Choice can be:

  one or more comma-separated values a range object any arbitrary list object

  Example:

  from azure.ai.ml.sweep import Choice command_job_for_sweep = command_job( batch_size=Choice(values=[16, 32, 64, 128]), number_of_hidden_layers=Choice(values=range(1,5)), )

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

• Question 96:

  DRAG DROP

  You manage an Azure Machine Learning workspace. You train a model named model1.

  You must identify the features to modify for a differing model prediction result.

  You need to configure the Responsible AI (RAI) dashboard for model1.

  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.

  Select and Place:

  

  

  ---

  Explanation:

  Generate a Responsible AI insights with YAML and Python

  You can generate a Responsible AI dashboard and scorecard via a pipeline job by using Responsible AI components. There are six core components for creating Responsible AI dashboards, along with a couple of helper components.

  Step 1: Load and configure the Responsible AI Insights dashboard constructor component.

  Generate a Responsible AI insights with YAML and Python

  The core components for constructing the Responsible AI dashboard in Azure Machine Learning are:

  RAI Insights dashboard constructor (Step 1)

  The tool components:

  Add Explanation to RAI Insights dashboard

  Add Causal to RAI Insights dashboard

  Add Counterfactuals to RAI Insights dashboard (Step 2) Add Error Analysis to RAI Insights dashboard Gather RAI Insights dashboard (Step 3) Gather RAI Insights score card

  Step 2: Add the counterfactuals component to the Responsible AI insights dashboard.

  You must identify the features to modify for a differing model prediction result.

  Examples of how the dashboard's components can be put together to analyze scenarios in diverse ways:

  - Model overview > error analysis > counterfactuals analysis and what-if To diagnose errors in individual instances with counterfactual analysis (minimum change to lead to a different model prediction)

  - Data analysis > counterfactuals analysis and what-if To address customers' questions about what they can do next time to get a different outcome from an AI system

  Note: Full functionality with integrated compute resource

  Some features of the Responsible AI dashboard require dynamic, on-the-fly, and real-time computation (for example, what-if analysis). Unless you connect a compute resource to the dashboard, you might find some functionality missing. When you connect to a compute resource, you enable full functionality of your Responsible AI dashboard for the following components:

  * Counterfactual what-if Generating a new what-if counterfactual data point to understand the minimum change required for a desired outcome is supported.

  Step 3: Use the Gather Responsible AI Insights dashboard component to present the dashboard.

  Incorrect:

  * Add Causal component to the Responsible Insights dashboard

  Selecting any individual data point, perturbing its treatment features, and seeing the expected causal outcome of causal what-if is supported (only for regression machine learning scenarios).

  This question is not a regression scenario.

  * Error analysis

  Setting your global data cohort to any cohort of interest will update the error tree instead of disabling it.

  Selecting other error or performance metrics is supported.

  Selecting any subset of features for training the error tree map is supported.

  Changing the minimum number of samples required per leaf node and error tree depth is supported.

  Dynamically updating the heat map for up to two features is supported.

  https://learn.microsoft.com/en-us/azure/machine-learning/how-to-responsible-ai-insights-sdk-cli

  https://learn.microsoft.com/en-us/azure/machine-learning/concept-responsible-ai-dashboard

• Question 97:

  You use the Azure Machine Learning Python SDK to define a pipeline to train a model.

  The data used to train the model is read from a folder in a datastore.

  You need to ensure the pipeline runs automatically whenever the data in the folder changes.

  What should you do?

  A. Set the regenerate_outputs property of the pipeline to True
  B. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline
  C. Create a PipelineParameter with a default value that references the location where the training data is stored
  D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property
  D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-trigger-published-pipeline

• Question 98:

  You have an Azure Machine Learning workspace named workspace1.

  You must add a datastore that connects an Azure Blob storage container to workspace1.

  You must be able to configure a privilege level.

  You need to configure authentication.

  Which authentication method should you use?

  A. Account key
  B. SAS token
  C. Service principal
  D. Managed identity
  B. SAS token

• Question 99:

  HOTSPOT

  You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:

  

  Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  
  

• Question 100:

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

  You are deploying the model to use a low-pointy VM mm a pacing discount.

  You need to deploy the model.

  Which compute large!

  should you use?

  A. Azure Machine Learning coulee clusters
  B. Azure Container instances (ACI)
  C. Azure Kubemetes Service (AKS)
  D. local deployment
  C. Azure Kubemetes Service (AKS)