Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 441:

  DRAG DROP

  You are producing a multiple linear regression model in Azure Machine learning Studio.

  Several independent variables are highly correlated.

  You need to select appropriate methods for conducting elective feature engineering on all the data;

  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:

  

  

• Question 442:

  You manage an Azure Machine Learning workspace.

  An MLflow model is already registered. You plan to customize how the deployment does inference.

  You need to deploy the MLflow model to a batch endpoint for batch inferencing.

  What should you create first?

  A. scoring script
  B. deployment
  C. environment
  D. deployment definition
  A. scoring script

• Question 443:

  HOTSPOT

  You have an Azure Machine Learning workspace and a collection of image files stored in two Azure Blob Storage accounts. You need to configure data asset properties.

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

  NOTE: Each correct selection is worth one point.

  

  

• Question 444:

  You use the Azure Machine Learning designer to create and run a training pipeline.

  The pipeline must be run every night to inference predictions from a large volume of files. The folder where the files will be stored is defined as a dataset.

  You need to publish the pipeline as a REST service that can be used for the nightly inferencing run.

  What should you do?

  A. Create a batch inference pipeline
  B. Set the compute target for the pipeline to an inference cluster
  C. Create a real-time inference pipeline
  D. Clone the pipeline
  A. Create a batch inference pipeline

  ---

  Explanation

  Azure Machine Learning Batch Inference targets large inference jobs that are not time-sensitive. Batch Inference provides cost-effective inference compute scaling, with unparalleled throughput for asynchronous applications. It is optimized for high-throughput, fire-and-forget inference over large collections of data.

  You can submit a batch inference job by pipeline_run, or through REST calls with a published pipeline.

  https://github.com/Azure/MachineLearningNotebooks/blob/master/how-to-use-azureml/machine-learning-pipelines/parallel-run/README.md

• Question 445:

  HOTSPOT

  You are using C-Support Vector classification to do a multi-class classification with an unbalanced training dataset. The C-Support Vector classification using Python code shown below:

  

  You need to evaluate the C-Support Vector classification code.

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

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Automatically adjust weights inversely proportional to class frequencies in the input data

  The "balanced" mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y)).

  Box 2: Penalty parameter

  Parameter: C : float, optional (default=1.0) Penalty parameter C of the error term.

  References:

  https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html

• Question 446:

  You create a Python script that runs a training experiment in Azure Machine Learning. The script uses the Azure Machine Learning SDK for Python.

  You must add a statement that retrieves the names of the logs and outputs generated by the script.

  You need to reference a Python class object from the SDK for the statement.

  Which class object should you use?

  A. Run
  B. ScriptRunConfig
  C. Workspace
  D. Experiment
  A. Run

  ---

  Explanation

  A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

  The run Class get_all_logs method downloads all logs for the run to a directory.

  Incorrect Answers:

  A: A run represents a single trial of an experiment. Runs are used to monitor the asynchronous execution of a trial, log metrics and store output of the trial, and to analyze results and access artifacts generated by the trial.

  B: A ScriptRunConfig packages together the configuration information needed to submit a run in Azure ML, including the script, compute target, environment, and any distributed job-specific configs.

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

• Question 447:

  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 manage an Azure Machine Learning workspace.

  The Python script named script.py reads an argument named training_data.

  The training_data argument specifies the path to the training data in a file named dataset1.csv.

  You plan to run the script.py Python script as a command job that trains a machine learning model.

  You need to provide the command to pass the path for the dataset as a parameter value when you submit the script as a training job.

  Solution: python script.py --training_data ${{inputs.training_data}}

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  
  

• Question 448:

  DRAG DROP

  You need to modify the inputs for the global penalty event model to address the bias and variance issue.

  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:

  

  

• Question 449:

  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 creating a model to predict the price of a student's artwork depending on the following variables: the student's length of education, degree type, and art form.

  You start by creating a linear regression model.

  You need to evaluate the linear regression model.

  Solution: Use the following metrics: Relative Squared Error, Coefficient of Determination, Accuracy, Precision, Recall, F1 score, and AUC.

  Does the solution meet the goal?

  A. Yes
  B. No
  B. No

  ---

  Explanation

  Relative Squared Error, Coefficient of Determination are good metrics to evaluate the linear regression model, but the others are metrics for classification models.

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

• Question 450:

  HOTSPOT

  You need to set up the Permutation Feature Importance module according to the model training requirements.

  

  Which properties should you select? To answer, select the appropriate options in the answer area;

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: Accuracy

  Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful.

  Box 2: R-Squared