Microsoft Azure Test 1

The model must support the latest version of Python.
You need to recommend a deep learning framework for speech recognition to include in the Data Science Virtual Machine (DSVM).

What should you recommend?

Select 1 option(s):

The model uses a PostgreSQL database and requires GPU processing.
You need to create a virtual machine that is pre-configured with the required tools.

What should you do?

Select 1 option(s):

Solution:
Apply an Equal Width with Custom Start and Stop binning mode.

Does the solution meet the goal?

Select 1 option(s):

Question
You need to implement a model development strategy to determine a user’s tendency to respond to an ad.
Which technique should you use?

You need to select a pre-configured DSVM to support the frameworks.
What should you create?

Select 1 option(s):

You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the task.
Scenario:
Replace missing values by removing rows and columns

Which modules should you choose?

Select 1 option(s):

Question
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?

Select 1 option(s):

You are developing a regression model for this.
Your goal is to predict the fare of a taxi trip.
You need to select performance metrics to correctly evaluate the regression model.
Which two metrics can you use? Each correct answer presents a complete solution?

Select 2 option(s):

You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the task.
Scenario:
Convert a categorical feature into a binary indicator

Which modules should you choose?

Select 1 option(s):

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:

Question
DRAG DROP
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence?

Select 1 option(s):

You need to recommend tools and frameworks to perform the following task:
§ Perform interactive data exploration and visualization

Which tools and frameworks should you recommend?


Select 1 option(s):

The environment must support the following requirements:
  § support Python and Scala
  § compose data storage, movement, and processing services into automated data pipelines
  § the same tool should be used for the orchestration of both data engineering and data science
  § support workload isolation and interactive workloads
  § enable scaling across a cluster of machines

You need to create the environment. What should you do?

Select 1 option(s):

You are creating a machine learning model. You have a dataset that contains null rows.
You need to use the Clean Missing Data module in Azure Machine Learning Studio to identify and resolve the null and missing data in the dataset.
Which parameter should you use?

Select 1 option(s):

You use Azure Machine Learning Studio to build a machine learning experiment.
You are asked to divide data into two distinct datasets.
Which module should you use for this requirement?

Select 1 option(s):

Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.
Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo formats.

Penalty detection and sentiment
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.

Advertisements
During the initial weeks in production, the following was observed:
· Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated 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:

Question
You need to implement a scaling strategy for the local penalty detection data.
Which normalization type should you use?

Select 1 option(s):

The deployment fails. You need to troubleshoot the deployment failure by determining the actions that were performed during deployment and identifying the specific action that failed.
Which code segment should you run?

Select 1 option(s):

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?

Select 1 option(s):

§ 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 the three possible ways to achieve the goal? Each correct answer presents a complete solution.

Select 3 option(s):

You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the task.
Scenario:
Increase the number of low-incidence examples in the dataset

Which modules should you choose?

Select 1 option(s):

Solution:
Apply a Quantiles binning mode with a PQuantile normalization.

Does the solution meet the goal?

Select 1 option(s):

You must evaluate your model on a limited data sample by using k-fold cross-validation.
You start by configuring a k parameter as the number of splits.
You need to configure the k parameter for the cross-validation.
Which value should you use?

Select 1 option(s):

You are working in XYZ company as a senior Data Scientist
You are performing sentiment analysis using a CSV file that includes 12,000 customer reviews written in a short sentence format.
You add the CSV file to Azure Machine Learning Studio and configure it as the starting point dataset of an experiment.
You add the Extract N-Gram Features from Text module to the experiment to extract key phrases from the customer review column in the dataset.
You must create a new n-gram dictionary from the customer review text and set the maximum n-gram size to trigrams.
What should you select? To answer, select the appropriate options in the answer area.


Select 1 option(s):

You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning models using Compute Unified Device Architecture (CUDA) computations.
You need to configure the DLVM to support CUDA.

What should you implement for this requirement?

Select 1 option(s):

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.

Select 2 option(s):

You are building an intelligent solution using machine learning models for your company.
The environment must support the following requirements:
§ Data scientists must build notebooks in a cloud environment
§ Data scientists must use automatic feature engineering and model building in machine learning pipelines.
§ Notebooks must be deployed to retrain using Spark instances with dynamic worker allocation.
§ Notebooks must be exportable to be version controlled locally.

You need to create the environment. Which four actions should you perform in sequence?


Select 1 option(s):

You are a data scientist using Azure Machine Learning Studio.
You are analyzing a raw dataset that requires cleaning.
You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct modules to perform the below task.
Scenario:
Remove potential duplicates from a dataset

Which modules should you choose?

Select 1 option(s):

You have the following requirements:
  § Models must be built using Caffe2 or Chainer frameworks.
  § Data scientists must be able to use a data science environment to build the machine learning pipelines and train models   
    on their personal devices in both connected and disconnected network environments.
§  Personal devices must support updating machine learning pipelines when connected to a network.

You need to select a data science environment. Which environment should you use?

Select 1 option(s):

Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.
Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo formats.

Penalty detection and sentiment
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.

Advertisements
During the initial weeks in production, the following was observed:
· Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated 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:

Question
DRAG DROP
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence?


Select 1 option(s):

Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.
Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo formats.

Penalty detection and sentiment
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.

Advertisements
During the initial weeks in production, the following was observed:
· Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated 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:

Question
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?

Select 1 option(s):

You create the following code to select hyperparameter values when training the model.

Select the appropriate answer


Select 1 option(s):

You need to format the data for the Weka environment.
Which module of Azure should you use?

Select 1 option(s):

The dataset is imbalanced.
You need to select an Azure Machine Learning Studio module to improve the classification accuracy.
Which module should you use?
 
Select 1 option(s):

You need to recommend tools and frameworks to perform the following task:
§ Build deep neural network (DNN) models

Which tools and frameworks should you recommend?


Select 1 option(s):

You have the following data available for model building:
  § Video recordings of sporting events
  § Transcripts of radio commentary about events
  § Logs from related social media feeds captured during sporting events

You need to select an environment for creating the model. Which environment should you use?

Select 1 option(s):

Select 1 option(s):

You are a data scientist using Azure Machine Learning Studio.
You are retrieving data from a large datastore by using Azure Machine Learning Studio.
You must create a subset of the data for testing purposes using a random sampling seed based on the system clock.
You add the Partition and Sample module to your experiment.
You need to select the properties for the module.

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


Select 1 option(s):

You need to select a compute target to deploy the workspace.
What should you use?

Select 1 option(s):

You must perform transformations and manipulations by using Azure Machine Learning Studio.
You need to identify the correct module to perform the below transformation.
Which module should you choose?

Scenario:
Convert a categorical feature into a binary indicator

Select 1 option(s):

Your manager have asked you to create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for below description?


Select 1 option(s):

You plan to deliver a hands-on workshop to several students.
The workshop will focus on creating data visualizations using Python.
Each student will use a device that has internet access.
Student devices are not configured for Python development.
Students do not have administrator access to install software on their devices.
Azure subscriptions are not available for students.
You need to ensure that students can run Python-based data visualization code.

Which Azure tool should you use?

Select 1 option(s):

You are a data scientist using Azure Machine Learning Studio.
You are creating a machine learning model in Python.
The provided dataset contains several numerical columns and one text column.
The text column represents a product’s category.
The product category will always be one of the following:
§ Bikes
§ Cars
§ Vans
§ Boats

You are building a regression model using the scikit-learn Python package.
You need to transform the text data to be compatible with the scikit-learn Python package.
How should you complete the code segment? To answer, select the appropriate options in the answer area.

Select 1 option(s):

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:
Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.

Does the solution meet the goal?

Select 1 option(s):

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an
All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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. You add both files to Azure Machine Learning Studio as separate datasets to the starting point for an experiment. Both datasets contain the following columns:

An initial investigation shows 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.

Data issues
Missing values
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 datasets also contain 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. 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-parametric statistics to measure relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsInHouse columns.

Model training
Permutation Feature Importance
Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You must be determined the absolute fit for the model.

Hyperparameters
You must configure hyperparameters in the model learning process to speed the learning phase. 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, must 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.

Cross-validation
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. You must complete this task before the data goes through the sampling process.

Linear regression module
When you train a Linear Regression module, you must 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. The distribution of features across multiple training models must be 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.
Question
HOTSPOT
You need to replace the missing data in the AccessibilityToHighway columns.
How should you configure the Clean Missing Data module?


Select 1 option(s):

You are asked to normalize values to produce an output column into bins to predict a target column.
Solution:
Apply a Quantiles normalization with a QuantileIndex normalization.

Does the solution meet the goal?

Select 1 option(s):

You create a variable that references the dataset using the following code:
training_ds = workspace.datasets.get(“training_data”)
You define an estimator to run the script.
You need to set the correct property of the estimator to ensure that your script can access the training_data dataset.
Which property should you set?

Select 1 option(s):

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? Each correct answer presents part of the solution.

Select 2 option(s):

You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?

Select 1 option(s):

Your supervisor asked you to preprocess text from CSV files.
You load the Azure Machine Learning Studio default stop words list.
You need to configure the Preprocess Text module to meet the following requirements:
§ Ensure that multiple related words from a single canonical form.
§ Remove pipe characters from text.
§ Remove words to optimize information retrieval.

Which three options should you select?

Select 3 option(s):

You are evaluating a completed binary classification machine learning model.
You need to use the precision as the evaluation metric.
Which visualization should you use?

Select 1 option(s):

Your manager have asked you to create a binary classification model to predict whether a person has a disease.
You need to detect possible classification errors.
Which error type should you choose for below description?

Select 1 option(s):

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?


Select 1 option(s):

You are performing a filter-based feature selection for a dataset to build a multi-class classifier 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?

Select 1 option(s):

This is a case study. Case studies are not timed separately. You can use as much exam time as you would like to complete each case. However, there may be additional case studies and sections on this exam. You must manage your time to ensure that you are able to complete all questions included on this exam in the time provided.
To answer the questions included in a case study, you will need to reference information that is provided in the case study. Case studies might contain exhibits and other resources that provide more information about the scenario that is described in the case study. Each question is independent of the other questions in this case study.
At the end of this case study, a review screen will appear. This screen allows you to review your answers and to make changes before you move to the next section of the exam. After you begin a new section, you cannot return to this section.

To start the case study
To display the first question in this case study, click the Next button. Use the buttons in the left pane to explore the content of the case study before you answer the questions. Clicking these buttons displays information such as business requirements, existing environment, and problem statements. If the case study has an
All Information tab, note that the information displayed is identical to the information displayed on the subsequent tabs. When you are ready to answer a question, click the Question button to return to the question.

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. You add both files to Azure Machine Learning Studio as separate datasets to the starting point for an experiment. Both datasets contain the following columns:

An initial investigation shows 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.

Data issues
Missing values
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 datasets also contain 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. 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-parametric statistics to measure relationships.
You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsInHouse columns.

Model training
Permutation Feature Importance
Given a trained model and a test dataset, you must compute the Permutation Feature Importance scores of feature variables. You must be determined the absolute fit for the model.

Hyperparameters
You must configure hyperparameters in the model learning process to speed the learning phase. 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, must 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.

Cross-validation
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. You must complete this task before the data goes through the sampling process.

Linear regression module
When you train a Linear Regression module, you must 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. The distribution of features across multiple training models must be 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.
Question
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?


Select 1 option(s):

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?

Select 1 option(s):

Overview
You are a data scientist in a company that provides data science for professional sporting events. Models will use 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.
Assess 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
Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and shared 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 video transcripts or radio commentary, and logs from related social media feeds captured during the sporting events.
Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo formats.

Penalty detection and sentiment
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.

Advertisements
During the initial weeks in production, the following was observed:
· Ad response rated 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 uncorrelated features.
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 slow.
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 shows 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.
Ad response models must be trained at the beginning of each event and applied during the sporting event.
Market segmentation models must optimize for similar ad response history.
Sampling must guarantee mutual and collective exclusively between 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.
Ad response models must support non-linear boundaries of features.
The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviated 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:

Question
DRAG DROP
You need to define a modeling strategy for ad response.
Which three actions should you perform in sequence?


Select 1 option(s):