Microsoft DP-100 Online Practice Questions and Exam Preparation

Microsoft DP-100 Online Questions & Answers

• Question 41:

  You want to train a classification model using data located in a comma-separated values (CSV) file.

  The classification model will be trained via the Automated Machine Learning interface using the Classification task type.

  You have been informed that only linear models need to be assessed by the Automated Machine Learning.

  Which of the following actions should you take?

  A. You should disable deep learning.
  B. You should enable automatic featurization.
  C. You should disable automatic featurization.
  D. You should set the task type to Forecasting.
  A. You should disable deep learning.

• Question 42:

  HOTSPOT

  You have an Azure Machine Learning workspace.

  You have the following code:

  

  You plan to rely on serverless compute to train a model by using Azure Machine Learning Python SDK v2. The serverless compute must use a designated number of nodes of a specific virtual machine type.

  You need to modify the code to run the training job according to the plan.

  How should you modify the command object? To answer, select the appropriate options in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

• Question 43:

  You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan.

  You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives.

  You need to determine the extent to which each feature in the customer data is influencing predictions.

  What should you do?

  A. Enable data drift monitoring for the model and its training dataset.
  B. Score the model against some test data with known label values and use the results to calculate a confusion matrix.
  C. Use the Hyperdrive library to test the model with multiple hyperparameter values.
  D. Use the interpretability package to generate an explainer for the model.
  E. Add tags to the model registration indicating the names of the features in the training dataset.
  D. Use the interpretability package to generate an explainer for the model.

  ---

  Explanation

  When you compute model explanations and visualize them, you're not limited to an existing model explanation for an automated ML model. You can also get an explanation for your model with different test data. The steps in this section show you how to compute and visualize engineered feature importance based on your test data.

  Incorrect Answers:

  A: In the context of machine learning, data drift is the change in model input data that leads to model performance degradation. It is one of the top reasons where model accuracy degrades over time, thus monitoring data drift helps detect model performance issues.

  B: A confusion matrix is used to describe the performance of a classification model. Each row displays the instances of the true, or actual class in your dataset, and each column represents the instances of the class that was predicted by the model.

  C: Hyperparameters are adjustable parameters you choose for model training that guide the training process. The HyperDrive package helps you automate choosing these parameters.

  https://docs.microsoft.com/en-us/azure/machine-learning/how-to-machine-learning-interpretability-automl

• Question 44:

  You use Azure Machine Learning designer to create a training pipeline for a regression model.

  You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values.

  What should you do?

  A. Clone the training pipeline.
  B. Create a batch inference pipeline from the training pipeline.
  C. Create a real-time inference pipeline from the training pipeline.
  D. Replace the dataset in the training pipeline with an Enter Data Manually module.
  B. Create a batch inference pipeline from the training pipeline.

  ---

  Explanation

  https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

  https://docs.microsoft.com/en-us/azure/machine-learning/algorithm-module-reference/enter-data-manually

• Question 45:

  HOTSPOT

  You have a dataset that contains 2,000 rows. You are building a machine learning classification model by using Azure Learning Studio. You add a Partition and Sample module to the experiment.

  You need to configure the module. You must meet the following requirements:

  1. Divide the data into subsets

  2. Assign the rows into folds using a round-robin method

  3. Allow rows in the dataset to be reused

  How should you configure the module? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Use the Split data into partitions option when you want to divide the dataset into subsets of the data. This option is also useful when you want to create a custom number of folds for cross-validation, or to split rows into several groups.

  Add the Partition and Sample module to your experiment in Studio (classic), and connect the dataset.

  For Partition or sample mode, select Assign to Folds.

  Use replacement in the partitioning: Select this option if you want the sampled row to be put back into the pool of rows for potential reuse. As a result, the same row might be assigned to several folds.

  If you do not use replacement (the default option), the sampled row is not put back into the pool of rows for potential reuse. As a result, each row can be assigned to only one fold.

  Randomized split: Select this option if you want rows to be randomly assigned to folds.

  If you do not select this option, rows are assigned to folds using the round-robin method.

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample

• Question 46:

  You use the designer to create a training pipeline for a classification model. The pipeline uses a dataset that includes the features and labels required for model training.

  You create a real-time inference pipeline from the training pipeline. You observe that the schema for the generated web service input is based on the dataset and includes the label column that the model predicts. Client applications that use the service must not be required to submit this value.

  You need to modify the inference pipeline to meet the requirement.

  What should you do?

  A. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.
  B. Delete the dataset from the training pipeline and recreate the real-time inference pipeline.
  C. Delete the Web Service Input module from the inference pipeline.
  D. Replace the dataset in the inference pipeline with an Enter Data Manually module that includes data for the feature columns but not the label column.
  A. Add a Select Columns in Dataset module to the inference pipeline after the dataset and use it to select all columns other than the label.

  ---

  Explanation

  By default, the Web Service Input will expect the same data schema as the module output data which connects to the same downstream port as it. You can remove the target variable column in the inference pipeline using Select Columns in Dataset module. Make sure that the output of Select Columns in Dataset removing target variable column is connected to the same port as the output of the Web Service Intput module.

  https://docs.microsoft.com/en-us/azure/machine-learning/tutorial-designer-automobile-price-deploy

• Question 47:

  DRAG DROP

  You need to implement source control for scripts in an Azure Machine Learning workspace. You use a terminal window in the Azure Machine Learning Notebook tab

  You must authenticate your Git account with SSH.

  You need to generate a new SSH key.

  Which four actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them m the correct order.

  Select and Place:

  

  

  ---

  
  

• Question 48:

  You plan to use automated machine learning by using Azure Machine Learning Python SDK v2 to train a regression model. You have data that has features with missing values, and categorical features with few distinct values.

  You need to control whether automated machine learning automatically imputes missing values and encode categorical features as part of the training task.

  Which enum of the automl package should you use?

  A. ForecastHorizonMode
  B. RegressionModels
  C. FeaturizationMode
  D. RegressionPrimaryMetrics
  C. FeaturizationMode

  ---

  Explanation

  AutoMLConfig Class

  Represents configuration for submitting an automated ML experiment in Azure Machine Learning.

  This configuration object contains and persists the parameters for configuring the experiment run, as well as the training data to be used at run time.

  Constructor

  Featurization str or FeaturizationConfig

  'auto' / 'off' / FeaturizationConfig Indicator for whether featurization step should be done automatically or not, or whether customized featurization should be used.

  Column type is automatically detected. Based on the detected column type preprocessing/featurization is done as follows:

  Categorical: Target encoding, one hot encoding, drop high cardinality categories, impute missing values.

  *-> Numeric: Impute missing values, cluster distance, weight of evidence.

  DateTime: Several features such as day, seconds, minutes, hours etc.

  Text: Bag of words, pre-trained Word embedding, text target encoding.

  https://learn.microsoft.com/en-us/python/api/azureml-train-automl-client/azureml.train.automl.automlconfig.automlconfig

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

• Question 49:

  You manage an Azure Machine Learning workspace.

  You must create and configure a compute cluster for a training job by using Python SDK v2.

  You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties.

  Which two properties should you define? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. size
  B. win_instances
  C. type
  D. name
  E. max_instances
  A. size
  E. max_instances

• Question 50:

  HOTSPOT

  You need to configure the Permutation Feature Importance module for the model training requirements.

  What should you do? To answer, select the appropriate options in the dialog box in the answer area.

  NOTE: Each correct selection is worth one point.

  

  

  ---

  Box 1: 500

  For Random seed, type a value to use as seed for randomization. If you specify 0 (the default), a number is generated based on the system clock.

  A seed value is optional, but you should provide a value if you want reproducibility across runs of the same experiment.

  Here we must replicate the findings.

  Box 2: Mean Absolute Error

  Scenario: Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings.

  Regression. Choose one of the following: Precision, Recall, Mean Absolute Error , Root Mean Squared Error, Relative Absolute Error, Relative Squared Error, Coefficient of Determination

  References:

  https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/permutation-feature-importance