Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 411:

  HOTSPOT

  You are the owner of an Azure Machine Learning workspace.

  You must prevent the creation or deletion of compute resources by using a custom role. You must allow all other operations inside the workspace.

  You need to configure the custom role.

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

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: Microsoft.MachineLearningServices/workspaces/*/read

  Reader role: Read-only actions in the workspace. Readers can list and view assets, including datastore credentials, in a workspace. Readers can't create or update these assets.

  Box 2: Microsoft.MachineLearningServices/workspaces/*/write

  If the roles include Actions that have a wildcard (*), the effective permissions are computed by subtracting the NotActions from the allowed Actions.

  Box 3: Box 2: Microsoft.MachineLearningServices/workspaces/computes/*/delete

  Box 4: Microsoft.MachineLearningServices/workspaces/computes/*/write

  https://docs.microsoft.com/en-us/azure/role-based-access-control/overview#how-azure-rbac-determines-if-a-user-has-access-to-a-resource

• Question 412:

  You have an Azure Machine Learning workspace.

  You plan to use the workspace to set up automated machine learning training for an image classification model.

  You need to choose the primary metric to optimize the model training.

  Which primary metric should you choose?

  A. r2_score
  B. mean_absolute_error
  C. accuracy
  D. root_mean_squared_log_error
  C. accuracy

• Question 413:

  HOTSPOT

  You have a dataset that includes home sales data for a city. The dataset includes the following columns.

  

  Each row in the dataset corresponds to an individual home sales transaction.

  You need to use automated machine learning to generate the best model for predicting the sales price based on the features of the house.

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

  NOTE: Each correct selection is worth one point.

  

  

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  Box 1: Regression

  Regression is a supervised machine learning technique used to predict numeric values.

  Box 2: Price

  https://docs.microsoft.com/en-us/learn/modules/create-regression-model-azure-machine-learning-designer

• Question 414:

  You have recently concluded the construction of a binary classification machine learning model.

  You are currently assessing the model. You want to make use of a visualization that allows for precision to be used as the measurement for the assessment.

  Which of the following actions should you take?

  A. You should consider using Venn diagram visualization.
  B. You should consider using Receiver Operating Characteristic (ROC) curve visualization.
  C. You should consider using Box plot visualization.
  D. You should consider using the Binary classification confusion matrix visualization.
  D. You should consider using the Binary classification confusion matrix visualization.

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion-matrix

• Question 415:

  You create an Azure Machine Learning workspace. You train an MLflow-formatted regression model by using tabular structured data.

  You must use a Responsible Al dashboard to assess the model.

  You need to use the Azure Machine Learning studio Ul to generate the Responsible A dashboard.

  What should you do first?

  A. Deploy the model to a managed online endpoint.
  B. Register the model with the workspace.
  C. Create the model explanations.
  D. Convert the model from the MLflow format to a custom format.
  B. Register the model with the workspace.

• Question 416:

  DRAG DROP

  You have been tasked with evaluating the performance of a binary classification model that you created.

  You need to choose evaluation metrics to achieve your goal.

  Which of the following are the metrics you would choose? Answer by dragging the correct options from the list to the answer area.

  Select and Place:

  

  

  ---

  The evaluation metrics available for binary classification models are: Accuracy, Precision, Recall, F1 Score, and AUC.

  Note: A very natural question is: 'Out of the individuals whom the model, how many were classified correctly (TP)?'

  This question can be answered by looking at the Precision of the model, which is the proportion of positives that are classified correctly.

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

• Question 417:

  You are a data scientist creating a linear regression model.

  You need to determine how closely the data fits the regression line.

  Which metric should you review?

  A. Root Mean Square Error
  B. Coefficient of determination
  C. Recall
  D. Precision
  E. Mean absolute error
  B. Coefficient of determination

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  Explanation

  Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect.

  Incorrect Answers:

  A: Root mean squared error (RMSE) creates a single value that summarizes the error in the model. By squaring the difference, the metric disregards the difference between over-prediction and under-prediction.

  C: Recall is the fraction of all correct results returned by the model.

  D: Precision is the proportion of true results over all positive results.

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

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

• Question 418:

  HOTSPOT

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

  

  

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  A Deep Learning Virtual Machine is a pre-configured environment for deep learning using GPU instances.

• Question 419:

  You are evaluating a completed binary classification machine.

  You need to use the precision as the evaluation metric.

  Which visualization should you use?

  A. scatter plot
  B. coefficient of determination
  C. Receiver Operating Characteristic CROC) curve
  D. Gradient descent
  C. Receiver Operating Characteristic CROC) curve

  ---

  Explanation

  Receiver operating characteristic (or ROC) is a plot of the correctly classified labels vs. the incorrectly classified labels for a particular model.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-understand-automated-ml#confusion-

  matrix

• Question 420:

  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 train a classification model by using a logistic regression algorithm.

  You must be able to explain the model's predictions by calculating the importance of each feature, both as an overall global relative importance value and as a measure of local importance for a specific set of predictions.

  You need to create an explainer that you can use to retrieve the required global and local feature importance values.

  Solution: Create a TabularExplainer.

  Does the solution meet the goal?

  A. Yes
  B. No
  A. Yes