Microsoft Microsoft Certifications DP-100 Questions & Answers

• Question 281:

  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 an Equal Width with Custom Start and Stop binning mode.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  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 282:

  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 PFIExplainer.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: A

  Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

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

• Question 283:

  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 MimicExplainer.

  Does the solution meet the goal?

  A. Yes

  B. No

  Correct Answer: B

  Instead use Permutation Feature Importance Explainer (PFI).

  Note 1: Mimic explainer is based on the idea of training global surrogate models to mimic blackbox models. A global surrogate model is an intrinsically interpretable model that is trained to approximate the predictions of any black box model as accurately as possible. Data scientists can interpret the surrogate model to draw conclusions about the black box model.

  Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

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

• Question 284:

  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

  Correct Answer: B

  Instead use Permutation Feature Importance Explainer (PFI). Note 1:

  

  Note 2: Permutation Feature Importance Explainer (PFI): Permutation Feature Importance is a technique used to explain classification and regression models. At a high level, the way it works is by randomly shuffling data one feature at a time for the entire dataset and calculating how much the performance metric of interest changes. The larger the change, the more important that feature is. PFI can explain the overall behavior of any underlying model but does not explain individual predictions.

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

• Question 285:

  You create a multi-class image classification deep learning model that uses the PyTorch deep learning framework.

  You must configure Azure Machine Learning Hyperdrive to optimize the hyperparameters for the classification model.

  You need to define a primary metric to determine the hyperparameter values that result in the model with the best accuracy score.

  Which three actions must you perform? Each correct answer presents part of the solution.

  NOTE: Each correct selection is worth one point.

  A. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to maximize.

  B. Add code to the bird_classifier_train.py script to calculate the validation loss of the model and log it as a float value with the key loss.

  C. Set the primary_metric_goal of the estimator used to run the bird_classifier_train.py script to minimize.

  D. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to accuracy.

  E. Set the primary_metric_name of the estimator used to run the bird_classifier_train.py script to loss.

  F. Add code to the bird_classifier_train.py script to calculate the validation accuracy of the model and log it as a float value with the key accuracy.

  Correct Answer: ADF

  AD:

  primary_metric_name="accuracy",

  primary_metric_goal=PrimaryMetricGoal.MAXIMIZE

  Optimize the runs to maximize "accuracy". Make sure to log this value in your training script.

  Note:

  primary_metric_name: The name of the primary metric to optimize. The name of the primary metric needs to exactly match the name of the metric logged by the training script. primary_metric_goal: It can be either

  PrimaryMetricGoal.MAXIMIZE or PrimaryMetricGoal.MINIMIZE and determines whether the primary metric will be maximized or minimized when evaluating the runs.

  F: The training script calculates the val_accuracy and logs it as "accuracy", which is used as the primary metric.

• Question 286:

  You plan to use automated machine learning to train a regression model. You have data that has features which have missing values, and categorical features with few distinct values.

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

  Which parameter and value pair should you use in the AutoMLConfig class?

  A. featurization = 'auto'

  B. enable_voting_ensemble = True

  C. task = 'classification'

  D. exclude_nan_labels = True

  E. enable_tf = True

  Correct Answer: A

  Featurization str or FeaturizationConfig

  Values: '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.

  Reference:

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

• Question 287:

  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.

  Correct Answer: D

  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.

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

• Question 288:

  You are performing feature engineering on a dataset.

  You must add a feature named CityName and populate the column value with the text London.

  You need to add the new feature to the dataset.

  Which Azure Machine Learning Studio module should you use?

  A. Extract N-Gram Features from Text

  B. Edit Metadata

  C. Preprocess Text

  D. Apply SQL Transformation

  Correct Answer: B

  Typical metadata changes might include marking columns as features.

  Reference:

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

• Question 289:

  You run an automated machine learning experiment in an Azure Machine Learning workspace. Information about the run is listed in the table below:

  

  You need to write a script that uses the Azure Machine Learning SDK to retrieve the best iteration of the experiment run. Which Python code segment should you use?

  

  A. Option A

  B. Option B

  C. Option C

  D. Option D

  E. Option E

  Correct Answer: D

  The get_output method on automl_classifier returns the best run and the fitted model for the last invocation. Overloads on get_output allow you to retrieve the best run and fitted model for any logged metric or for a particular iteration.

  In [ ]:

  best_run, fitted_model = local_run.get_output()

  Reference:

  https://notebooks.azure.com/azureml/projects/azureml-getting-started/html/how-to-use-azureml/ automated-machine-learning/classification-with-deployment/auto-ml-classification-with-deployment.ipynb

• Question 290:

  You have a comma-separated values (CSV) file containing data from which you want to train a classification model.

  You are using the Automated Machine Learning interface in Azure Machine Learning studio to train the classification model. You set the task type to Classification.

  You need to ensure that the Automated Machine Learning process evaluates only linear models.

  What should you do?

  A. Add all algorithms other than linear ones to the blocked algorithms list.

  B. Set the Exit criterion option to a metric score threshold.

  C. Clear the option to perform automatic featurization.

  D. Clear the option to enable deep learning.

  E. Set the task type to Regression.

  Correct Answer: C

  Automatic featurization can fit non-linear models.

  Reference: https://econml.azurewebsites.net/spec/estimation/dml.html https://docs.microsoft.com/en-us/azure/machine-learning/how-to-use-automated-ml-for-ml-models