Designing and Implementing a Data Science Solution on Azure DP-100 Exam Dumps V11.02

1. Topic 1, Case Study 1

Overview

You are a data scientist in a company that provides data science for professional sporting events.

Models will be global and local market data to meet the following business goals:

• Understand sentiment of mobile device users at sporting events based on audio from crowd reactions.

• Access a user's tendency to respond to an advertisement.

• Customize styles of ads served on mobile devices.

• Use video to detect penalty events.

Current environment

Requirements

• Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats.

• The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events.

• Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats.

Advertisements

• Ad response models must be trained at the beginning of each event and applied during the sporting event.

• Market segmentation nxxlels must optimize for similar ad resporr.r history.

• Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features.

• Local market segmentation models will be applied before determining a user’s propensity to respond to an advertisement.

• Data scientists must be able to detect model degradation and decay.

• Ad response models must support non linear boundaries features.

• The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%.

• The ad propensity model uses cost factors shown in the following diagram:

• The ad propensity model uses proposed cost factors shown in the following diagram:

Performance curves of current and proposed cost factor scenarios are shown in the following diagram:

Penalty detection and sentiment

Findings

• Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection.

• Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.

• Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation

• Notebooks must execute with the same code on new Spark instances to recode only the source of the data.

• Global penalty detection models must be trained by using dynamic runtime graph computation during training.

• Local penalty detection models must be written by using BrainScript.

• Experiments for local crowd sentiment models must combine local penalty detection data.

• Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.

• All shared features for local models are continuous variables.

• Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available.

segments

During the initial weeks in production, the following was observed:

• Ad response rates declined.

• Drops were not consistent across ad styles.

• The distribution of features across training and production data are not consistent.

Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features.

Penalty detection and sentiment

• Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models.

• All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow.

• Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region.

• The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases.

DRAG DROP

You need to define an evaluation strategy for the crowd sentiment models.

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.

2. You need to implement a feature engineering strategy for the crowd sentiment local models.

What should you do?

3. HOTSPOT

You need to use the Python language to build a sampling strategy for the global penalty detection models.

How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

4. You need to implement a scaling strategy for the local penalty detection data.

Which normalization type should you use?

5. 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.

6. You need to select an environment that will meet the business and data requirements.

Which environment should you use?

7. DRAG DROP

You need to define a process for penalty event detection.

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.

8. You need to implement a model development strategy to determine a user’s tendency to respond to an ad.

Which technique should you use?

9. You need to resolve the local machine learning pipeline performance issue.

What should you do?

10. You need to implement a new cost factor scenario for the ad response models as illustrated in the

performance curve exhibit.

Which technique should you use?

11. DRAG DROP

You need to define a modeling strategy for ad response.

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.

12. DRAG DROP

You need to define a process for penalty event detection.

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.

13. HOTSPOT

You need to build a feature extraction strategy for the local models.

How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

14. DRAG DROP

You need to define an evaluation strategy for the crowd sentiment models.

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.

15. Topic 2, Case Study 2

Case study

Overview

You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules.

Datasets

There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns:

The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment.

Dataset issues

The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values.

Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail.

Model fit

The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting.

Experiment requirements

You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance.

In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset.

You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships.

You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns.

Model training

Given a trained model and a test dataset, you need to 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.

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.

You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs.

Testing

You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city’s main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process.

When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent.

Data visualization

You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results.

You must produce a Receiver Operating Characteristic (ROC) curve to conduct a

diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another.

DRAG DROP

You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements.

Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

16. 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.

17. 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.

18. HOTSPOT

You need to configure the Edit Metadata module so that the structure of the datasets

match.

Which configuration options should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

19. HOTSPOT

You need to identify the methods for dividing the data according, to the testing requirements.

Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point.

20. HOTSPOT

You need to replace the missing data in the AccessibilityToHighway columns.

How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

21. HOTSPOT

You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets.

How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

22. DRAG DROP

You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed.

Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order.

23. You need to select a feature extraction method.

Which method should you use?

24. DRAG DROP

You need to correct the model fit 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.

25. HOTSPOT

You need to identify the methods for dividing the data according to the testing requirements.

Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

26. You need to select a feature extraction method.

Which method should you use?

27. DRAG DROP

You need to implement early stopping criteria as suited in the model training requirements.

Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select.

28. Topic 3, Mix Questions

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 using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

29. 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?

30. You train and register a model in your Azure Machine Learning workspace.

You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data.

You need to create the inferencing script for the ParallelRunStep pipeline step.

Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

31. You are evaluating a completed binary classification machine.

You need to use the precision as the evaluation metric.

Which visualization should you use?

32. You run an experiment that uses an AutoMLConfig class to define an automated machine learning task with a maximum of ten model training iterations. The task will attempt to find the best performing model based on a metric named accuracy.

You submit the experiment with the following code:

You need to create Python code that returns the best model that is generated by the automated machine learning task.

Which code segment should you use?

33. You plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment.

You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets.

You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort.

What should you do?

34. You are a data scientist building a deep convolutional neural network (CNN) for image classification.

The CNN model you built shows signs of overfitting.

You need to reduce overfitting and converge the model to an optimal fit.

Which two actions should you perform? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

35. DRAG DROP

You have an Azure Machine Learning workspace that contains a CPU-based compute cluster and an Azure Kubernetes Services (AKS) inference cluster. You create a tabular dataset containing data that you plan to use to create a classification model.

You need to use the Azure Machine Learning designer to create a web service through which client applications can consume the classification model by submitting new data and getting an immediate prediction as a response.

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.

36. You are creating a new Azure Machine Learning pipeline using the designer.

The pipeline must train a model using data in a comma-separated values (CSV) file that is published on a

website. You have not created a dataset for this file.

You need to ingest the data from the CSV file into the designer pipeline using the minimal administrative effort.

Which module should you add to the pipeline in Designer?

37. You are building a regression model tot estimating the number of calls during an event.

You need to determine whether the feature values achieve the conditions to build a Poisson regression model.

Which two conditions must the feature set contain? I ach correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

38. You create an Azure Machine Learning compute resource to train models.

The compute resource is configured as follows:

✑ Minimum nodes: 2

✑ Maximum nodes: 4

You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:

✑ Minimum nodes: 0

✑ Maximum nodes: 8

You need to reconfigure the compute resource.

What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

39. HOTSPOT

You create a binary classification model using Azure Machine Learning Studio.

You must use a Receiver Operating Characteristic (RO C) curve and an F1 score to evaluate the model.

You need to create the required business metrics.

How should you complete the experiment? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point.

40. 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 new experiment in Azure Machine Learning Studio.

One class has a much smaller number of observations than the other classes in the training set.

You need to select an appropriate data sampling strategy to compensate for the class imbalance.

Solution: You use the Scale and Reduce sampling mode.

Does the solution meet the goal?

41. HOTSPOT

You plan to use Hyperdrive to optimize the hyperparameters selected when training a model.

You create the following code to define options for the hyperparameter experiment:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

42. DRAG DROP

You create machine learning models by using Azure Machine Learning.

You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.

You need to select the appropriate compute types.

Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

43. You write five Python scripts that must be processed in the order specified in Exhibit A C which allows the same modules to run in parallel, but will wait for modules with dependencies.

You must create an Azure Machine Learning pipeline using the Python SDK, because you want to script to create the pipeline to be tracked in your version control system. You have created five PythonScriptSteps and have named the variables to match the module names.

You need to create the pipeline shown. Assume all relevant imports have been done.

Which Python code segment should you use?

A)

B)

C)

D)

44. You create an Azure Machine Learning workspace.

You must create a custom role named DataScientist that meets the following requirements:

✑ Role members must not be able to delete the workspace.

✑ Role members must not be able to create, update, or delete compute resource in the workspace.

✑ Role members must not be able to add new users to the workspace.

You need to create a JSON file for the DataScientist role in the Azure Machine Learning workspace.

The custom role must enforce the restrictions specified by the IT Operations team.

Which JSON code segment should you use?

A)

B)

C)

D)

45. You create a datastore named training_data that references a blob container in an Azure Storage account. The blob container contains a folder named csv_files in which multiple comma-separated values (CSV) files are stored.

You have a script named train.py in a local folder named ./script that you plan to run as an experiment using an estimator.

The script includes the following code to read data from the csv_files folder:

You have the following script.

You need to configure the estimator for the experiment so that the script can read the data from a data reference named data_ref that references the csv_files folder in the training_data datastore.

Which code should you use to configure the estimator?

A)

B)

C)

D)

E)

46. You are creating a binary classification by using a two-class logistic regression model.

You need to evaluate the model results for imbalance.

Which evaluation metric should you use?

47. You use the Two-Class Neural Network module in Azure Machine Learning Studio to build a binary

classification model. You use the Tune Model Hyperparameters module to tune accuracy for the model.

You need to select the hyperparameters that should be tuned using the Tune Model Hyperparameters module.

Which two hyperparameters should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

48. You are developing a hands-on workshop to introduce Docker for Windows to attendees.

You need to ensure that workshop attendees can install Docker on their devices.

Which two prerequisite components should attendees install on the devices? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

49. HOTSPOT

You are preparing to use the Azure ML SDK to run an experiment and need to create compute.

You run the following code:

For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.

50. 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 analyzing a numerical dataset which contains missing values in several columns.

You must clean the missing values using an appropriate operation without affecting the dimensionality of the feature set.

You need to analyze a full dataset to include all values.

Solution: Calculate the column median value and use the median value as the replacement for any missing value in the column.

Does the solution meet the goal?

51. You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio.

The dataset contains categorical features that are highly correlated to the output label column.

You need to select the appropriate feature scoring statistical method to identify the key predictors.

Which method should you use?

52. 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 using Azure Machine Learning to run an experiment that trains a classification model.

You want to use Hyperdrive to find parameters that optimize the AUC metric for the model.

You configure a HyperDriveConfig for the experiment by running the following code:

You plan to use this configuration to run a script that trains a random forest model and then tests it with validation data. The label values for the validation data are stored in a variable named y_test variable, and the predicted probabilities from the model are stored in a

variable named y_predicted.

You need to add logging to the script to allow Hyperdrive to optimize hyperparameters for the AUC metric.

Solution: Run the following code:

Does the solution meet the goal?

53. You must store data in Azure Blob Storage to support Azure Machine Learning.

You need to transfer the data into Azure Blob Storage.

What are three possible ways to achieve the goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

54. You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website.

Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand.

Which deployment compute option should you use?

55. HOTSPOT

You are tuning a hyperparameter for an algorithm.

The following table shows a data set with different hyperparameter, training error, and validation errors.

Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.

56. DRAG DROP

You plan to explore demographic data for home ownership in various cities.

The data is in a CSV file with the following format:

age,city,income,home_owner

21, Chicago, 50000,0

35, Seattle, 120000,1

23, Seattle, 65000,0

45, Seattle, 130000,1

18, Chicago, 48000,0

You need to run an experiment in your Azure Machine Learning workspace to explore the data and log the results.

The experiment must log the following information:

✑ the number of observations in the dataset

✑ a box plot of income by home_owner

✑ a dictionary containing the city names and the average income for each city

You need to use the appropriate logging methods of the experiment’s run object to log the required information.

How should you complete the code? To answer, drag the appropriate code segments to the correct locations. Each code segment may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point.

57. You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model.

You must use Hyperdrive to try combinations of the following hyperparameter values. You must not apply an early termination policy.

learning_rate: any value between 0.001 and 0.1

• batch_size: 16, 32, or 64

You need to configure the sampling method for the Hyperdrive experiment

Which two sampling methods can you use? Each correct answer is a complete solution. NOTE: Each correct selection is worth one point.

58. You are analyzing a dataset by using Azure Machine Learning Studio.

YOU need to generate a statistical summary that contains the p value and the unique value count for each feature column.

Which two modules can you users? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point.

59. HOTSPOT

You are performing a classification task in Azure Machine Learning Studio.

You must prepare balanced testing and training samples based on a provided data set.

You need to split the data with a 0.75:0.25 ratio.

Which value should you use for each parameter? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

60. 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?