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Question 31

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You have a Python script that executes a pipeline. The script includes the following code:

from azureml.core import Experiment pipeline_run = Experiment(ws, 'pipeline_test').submit(pipeline)

You want to test the pipeline before deploying the script.

You need to display the pipeline run details written to the STDOUT output when the pipeline completes.

Which code segment should you add to the test script?

pipeline_run.get.metrics()
pipeline_run.get.metrics()
pipeline_run.wait_for_completion(show_output=True)
pipeline_run.wait_for_completion(show_output=True)
pipeline_param = PipelineParameter(name="stdout",default_value="console")
pipeline_param = PipelineParameter(name="stdout",default_value="console")
pipeline_run.get_status()
pipeline_run.get_status()
Suggested answer: B
Explanation:

wait_for_completion: Wait for the completion of this run. Returns the status object after the wait.

Syntax: wait_for_completion(show_output=False, wait_post_processing=False, raise_on_error=True)

Parameter: show_output

Indicates whether to show the run output on sys.stdout.

asked 02/10/2024
hamza reza
54 questions

Question 32

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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?

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

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

asked 02/10/2024
Francinilo Leitao Ferreira
37 questions

Question 33

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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?

Yes
Yes
No
No
Suggested answer: B
Explanation:

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

asked 02/10/2024
Lawrence Bargers
37 questions

Question 34

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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?

Yes
Yes
No
No
Suggested answer: B
Explanation:

Instead use Permutation Feature Importance Explainer (PFI).

Note 1:

Microsoft DP-100 image Question 3 explanation 89259 10022024015826000000

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

asked 02/10/2024
A R
41 questions

Question 35

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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?

Yes
Yes
No
No
Suggested answer: A
Explanation:

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

asked 02/10/2024
Verónica Crespo
42 questions

Question 36

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You are determining if two sets of data are significantly different from one another by using Azure Machine Learning Studio.

Estimated values in one set of data may be more than or less than reference values in the other set of data. You must produce a distribution that has a constant Type I error as a function of the correlation.

You need to produce the distribution.

Which type of distribution should you produce?

Unpaired t-test with a two-tail option
Unpaired t-test with a two-tail option
Unpaired t-test with a one-tail option
Unpaired t-test with a one-tail option
Paired t-test with a one-tail option
Paired t-test with a one-tail option
Paired t-test with a two-tail option
Paired t-test with a two-tail option
Suggested answer: D
Explanation:

Choose a one-tail or two-tail test. The default is a two-tailed test. This is the most common type of test, in which the expected distribution is symmetric around zero.

Example: Type I error of unpaired and paired two-sample t-tests as a function of the correlation. The simulated random numbers originate from a bivariate normal distribution with a variance of 1.

Microsoft DP-100 image Question 5 explanation 89261 10022024015826000000

Reference:

https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/test-hypothesis-using-t-test

https://en.wikipedia.org/wiki/Student%27s_t-test

asked 02/10/2024
Petros Kapouleas
51 questions

Question 37

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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?

Extract N-Gram Features from Text
Extract N-Gram Features from Text
Edit Metadata
Edit Metadata
Preprocess Text
Preprocess Text
Apply SQL Transformation
Apply SQL Transformation
Suggested answer: B
Explanation:

Typical metadata changes might include marking columns as features.

Reference:

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

asked 02/10/2024
Paramdeep Saini
45 questions

Question 38

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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: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: A
Explanation:

The following metrics are reported for evaluating regression models. When you compare models, they are ranked by the metric you select for evaluation.

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

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.

Relative absolute error (RAE) is the relative absolute difference between expected and actual values; relative because the mean difference is divided by the arithmetic mean.

Relative squared error (RSE) similarly normalizes the total squared error of the predicted values by dividing by the total squared error of the actual values.

Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In other words: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.

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.

AUC.

Reference:

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

asked 02/10/2024
Yahya Ozer
39 questions

Question 39

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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: Accuracy, Precision, Recall, F1 score, and AUC.

Does the solution meet the goal?

Yes
Yes
No
No
Suggested answer: B
Explanation:

Those are metrics for evaluating classification models, instead use: Mean Absolute Error, Root Mean Absolute Error, Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.

Reference:

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

asked 02/10/2024
Sergy Camilo
40 questions

Question 40

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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?

Yes
Yes
No
No
Suggested answer: B
Explanation:

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

Reference:

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

asked 02/10/2024
Pedro Pereira
45 questions
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