Microsoft Azure Test 12

Free Azure Data Scientist Associate Free Prep Test and Answers. Get ready to be hired for a data scientist career

Welcome to your Microsoft Azure Test-12

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Q1. You need to consider the underlined segment to establish whether it is accurate.To transform a categorical feature into a binary indicator, you should make use of the Clean Missing Data module.Select ′No adjustment required′ if the underlined segment is accurate. If the underlined segment is inaccurate, select the accurate option.

Select 1 option(s):

A. No adjustment required.

B. Convert to Indicator Values

C. Apply SQL Transformation

D. Group Categorical Values

Q2. You have recently concluded the construction of a binary classification machine learning model.You are currently assessing the model. You want to make use of a visualization that allows for precision to be used as the measurement for the assessment.Which of the following actions should you take?

Select 1 option(s):

A. You should consider using Venn diagram visualization.

B. You should consider using Receiver Operating Characteristic (ROC) curve visualization.

C. You should consider using Box plot visualization.

D. You should consider using the Binary classification confusion matrix visualization.

Q3. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of carrying out feature engineering on a dataset.You want to add a feature to the dataset and fill the column value.Recommendation: You must make use of the Group Categorical Values Azure Machine Learning Studio module.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q4. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.Recommendation: You configure the use of the value k=10.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q5. You have been tasked with ascertaining if two sets of data differ considerably. You will make use of Azure Machine Learning Studio to complete your task.You plan to perform a paired t-test.Which of the following are conditions that must apply to use a paired t-test? (Choose all that apply.)

Select 2 option(s):

A. All scores are independent from each other.

B. You have a matched pairs of scores.

C. The sampling distribution of d is normal.

D. The sampling distribution of x1- x2 is normal.

Q6. You want to train a classification model using data located in a comma-separated values (CSV) file.The classification model will be trained via the Automated Machine Learning interface using the Classification task type.You have been informed that only linear models need to be assessed by the Automated Machine Learning.Which of the following actions should you take?

Select 1 option(s):

A. You should disable deep learning.

B. You should enable automatic featurization.

C. You should disable automatic featurization.

D. You should set the task type to Forecasting.

Q7. You are preparing to train a regression model via automated machine learning. The data available to you has features with missing values, as well as categorical features with little discrete values.You want to make sure that automated machine learning is configured as follows:? missing values must be automatically imputed.? categorical features must be encoded as part of the training task.Which of the following actions should you take?

Select 1 option(s):

A. You should make use of the featurization parameter with the ‘auto‘ value pair.

B. You should make use of the featurization parameter with the ‘off‘ value pair.

C. You should make use of the featurization parameter with the ‘on‘ value pair.

D. You should make use of the featurization parameter with the ‘FeaturizationConfig‘ value pair.

Q8. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with constructing a machine learning model that translates language text into a different language text.The machine learning model must be constructed and trained to learn the sequence of the.Recommendation: You make use of Generative Adversarial Networks (GANs).Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q9. You are in the process of constructing a deep convolutional neural network (CNN). The CNN will be used for image classification.You notice that the CNN model you constructed displays hints of overfitting.You want to make sure that overfitting is minimized, and that the model is converged to an optimal fit.Which of the following is TRUE with regards to achieving your goal?

Select 1 option(s):

A. You have to add an additional dense layer with 512 input units, and reduce the amount of training data.

B. You have to add L1/L2 regularization, and reduce the amount of training data.

C. You have to reduce the amount of training data and make use of training data augmentation.

D. You have to add L1/L2 regularization, and make use of training data augmentation.

E. You have to add an additional dense layer with 512 input units, and add L1/L2 regularization.

Q10. You make use of Azure Machine Learning Studio to create a binary classification model.You are preparing to carry out a parameter sweep of the model to tune hyperparameters. You have to make sure that the sweep allows for every possible combination of hyperparameters to be iterated. Also, the computing resources needed to carry out the sweep must be reduced.Which of the following actions should you take?

Select 1 option(s):

A. You should consider making use of the Selective grid sweep mode.

B. You should consider making use of the Measured grid sweep mode.

C. You should consider making use of the Entire grid sweep mode.

D. You should consider making use of the Random grid sweep mode.

Q11. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are planning to make use of Azure Machine Learning designer to train models.You need choose a suitable compute type.Recommendation: You choose Inference cluster.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q12. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with constructing a machine learning model that translates language text into a different language text.The machine learning model must be constructed and trained to learn the sequence of the.Recommendation: You make use of Convolutional Neural Networks (CNNs).Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q13. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are planning to make use of Azure Machine Learning designer to train models.You need choose a suitable compute type.Recommendation: You choose Compute cluster.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q14. You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.Which of the following is an algorithm that reduces the variances between actual and predicted values?

Select 1 option(s):

A. Fast Forest Quantile Regression

B. Poisson Regression

C. Boosted Decision Tree Regression

D. Linear Regression

Q15. You are making use of the Azure Machine Learning to designer construct an experiment.After dividing a dataset into training and testing sets, you configure the algorithm to be Two-Class Boosted Decision Tree.You are preparing to ascertain the Area Under the Curve (AUC).Which of the following is a sequential combination of the models required to achieve your goal?

Select 1 option(s):

A. Train, Score, Evaluate.

B. Score, Evaluate, Train.

C. Evaluate, Export Data, Train.

D. Train, Score, Export Data.

Q16. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are planning to make use of Azure Machine Learning designer to train models.You need choose a suitable compute type.Recommendation: You choose Attached compute.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q17. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with constructing a machine learning model that translates language text into a different language text.The machine learning model must be constructed and trained to learn the sequence of the.Recommendation: You make use of Recurrent Neural Networks (RNNs).Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q18. 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.

Select 2 option(s):

A. Microsoft Hardware-Assisted Virtualization Detection Tool

B. Kitematic

C. BIOS-enabled virtualization

D. VirtualBox

E. Windows 10 64-bit Professional

Q19. 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?

Select 1 option(s):

A. Yes

B. No

Q20. You are implementing a machine learning model to predict stock prices.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):

A. Create a Data Science Virtual Machine (DSVM) Windows edition.

B. Create a Geo Al Data Science Virtual Machine (Geo-DSVM) Windows edition.

C. Create a Deep Learning Virtual Machine (DLVM) Linux edition.

D. Create a Deep Learning Virtual Machine (DLVM) Windows edition.

Q21. You plan to build a team data science environment. Data for training models in machine learning pipelines will be over 20 GB in size.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):

A. Azure Machine Learning Service

B. Azure Machine Learning Studio

C. Azure Databricks

D. Azure Kubernetes Service (AKS)

Q22. 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):

A. Split Data

B. Load Trained Model

C. Assign Data to Clusters

D. Group Data into Bins

Q23. Your team is building a data engineering and data science development environment.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 machinesYou need to create the environment.What should you do?

Select 1 option(s):

A. Build the environment in Apache Hive for HDInsight and use Azure Data Factory for orchestration.

B. Build the environment in Azure Databricks and use Azure Data Factory for orchestration.

C. Build the environment in Apache Spark for HDInsight and use Azure Container Instances for orchestration.

D. Build the environment in Azure Databricks and use Azure Container Instances for orchestration.

Q24. You are developing deep learning models to analyze semi-structured, unstructured, and structured data types.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 eventsYou need to select an environment for creating the model.Which environment should you use?

Select 1 option(s):

A. Azure Cognitive Services

B. Azure Data Lake Analytics

C. Azure HDInsight with Spark MLib

D. Azure Machine Learning Studio

Q25. 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):

A. Assign Data to Clusters

B. Load Trained Model

C. Partition and Sample

D. Tune Model-Hyperparameters

Q26. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are a data scientist using Azure Machine Learning Studio.You need to normalize values to produce an output column into bins to predict a target column.Solution: Apply a Quantiles normalization with a QuantileIndex normalization.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q27. 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?

Select 1 option(s):

A. Solid State Drives (SSD)

B. Computer Processing Unit (CPU) speed increase by using overclocking

C. Graphic Processing Unit (GPU)

D. High Random Access Memory (RAM) configuration

E. Intel Software Guard Extensions (Intel SGX) technology

Q28. 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.

Select 3 option(s):

A. Bulk Insert SQL Query

B. AzCopy

C. Python script

D. Azure Storage Explorer

E. Bulk Copy Program (BCP)

Q29. You plan to create a speech recognition deep learning model.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):

A. Rattle

B. TensorFlow

C. Weka

D. Scikit-learn

Q30. 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 count for each feature column.Which two modules can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. Computer Linear Correlation

B. Export Count Table

C. Execute Python Script

D. Convert to Indicator Values

E. Summarize Data

Q31. 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):

A. Replace with mean

B. Remove entire column

C. Remove entire row

D. Hot Deck

E. Custom substitution value

F. Replace with mode

Q32. You create an Azure Machine Learning compute resource to train models. The compute resource is configured as follows:? Minimum nodes: 2? Maximum nodes: 4You must decrease the minimum number of nodes and increase the maximum number of nodes to the following values:? Minimum nodes: 0? Maximum nodes: 8You 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.

Select 3 option(s):

A. Use the Azure Machine Learning studio.

B. Run the update method of the AmlCompute class in the Python SDK.

C. Use the Azure portal.

D. Use the Azure Machine Learning designer.

E. Run the refresh_state() method of the BatchCompute class in the Python SDK.

Q33. You plan to provision an Azure Machine Learning Basic edition workspace for a data science project.You need to identify the tasks you will be able to perform in the workspace.Which three tasks will you be able to perform? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Create a Compute Instance and use it to run code in Jupyter notebooks.

B. Create an Azure Kubernetes Service (AKS) inference cluster.

C. Use the designer to train a model by dragging and dropping pre-defined modules.

D. Create a tabular dataset that supports versioning.

E. Use the Automated Machine Learning user interface to train a model.

Q34. You create an Azure Machine Learning workspace. You are preparing a local Python environment on a laptop computer. You want to use the laptop to connect to the workspace and run experiments.You create the following config.json file.{“workspace_name“ : “ml-workspace“}You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace.You need to configure the config.json file to connect to the workspace from the Python environment.Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution.NOTE: Each correct selection is worth one point.

Select 2 option(s):

A. login

B. resource_group

C. subscription_id

D. key

E. region

Q35. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are a data scientist using Azure Machine Learning Studio.You need to normalize values to produce an output column into bins to predict a target column.Solution: Apply a Quantiles binning mode with a PQuantile normalization.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q36. You are solving a classification task.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):

A. Permutation Feature Importance

B. Filter Based Feature Selection

C. Fisher Linear Discriminant Analysis

D. Synthetic Minority Oversampling Technique (SMOTE)

Q37. 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?

Select 1 option(s):

A. Yes

B. No

Q38. 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 Studio to perform feature engineering on a dataset.You need to normalize values to produce a feature column grouped into bins.Solution: Apply an Entropy Minimum Description Length (MDL) binning mode.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q39. 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: Replace each missing value using the Multiple Imputation by Chained Equations (MICE) method.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q40. You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.You need to format the data for the Weka environment.Which module should you use?

Select 1 option(s):

A. Convert to CSV

B. Convert to Dataset

C. Convert to ARFF

D. Convert to SVMLight

Q41. You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.You need to select a pre-configured DSVM to support the frameworks.What should you create?

Select 1 option(s):

A. Data Science Virtual Machine for Windows 2012

B. Data Science Virtual Machine for Linux (CentOS)

C. Geo AI Data Science Virtual Machine with ArcGIS

D. Data Science Virtual Machine for Windows 2016

E. Data Science Virtual Machine for Linux (Ubuntu)

Q42. Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.You are a data scientist using Azure Machine Learning Studio.You need to normalize values to produce an output column into bins to predict a target column.Solution: Apply an Equal Width with Custom Start and Stop binning mode.Does the solution meet the goal?

Select 1 option(s):

A. Yes

B. No

Q43. You are with a time series dataset in Azure Machine Learning Studio.You need to split your dataset into training and testing subsets by using the Split Data module.Which splitting mode should you use?

Select 1 option(s):

A. Recommender Split

B. Regular Expression Split

C. Relative Expression Split

D. Split Rows with the Randomized split parameter set to true

Q44. You are a lead data scientist for a project that tracks the health and migration of birds. You create a multi-class image classification deep learning model that uses a set of labeled bird photographs collected by experts.You have 100,000 photographs of birds. All photographs use the JPG format and are stored in an Azure blob container in an Azure subscription.You need to access the bird photograph files in the Azure blob container from the Azure Machine Learning service workspace that will be used for deep learning model training. You must minimize data movement.What should you do?

Select 1 option(s):

A. Create an Azure Data Lake store and move the bird photographs to the store.

B. Create an Azure Cosmos DB database and attach the Azure Blob containing bird photographs storage to the database.

C. Create and register a dataset by using TabularDataset class that references the Azure blob storage containing bird photographs.

D. Register the Azure blob storage containing the bird photographs as a datastore in Azure Machine Learning service.

E. Copy the bird photographs to the blob datastore that was created with your Azure Machine Learning service workspace.

Q45. You plan to create a compute instance as part of an Azure Machine Learning development workspace.You must interactively debug code running on the compute instance by using Visual Studio Code Remote.You need to provision the compute instance.What should you do?

Select 1 option(s):

A. Enable Remote Desktop Protocol (RDP) access.

B. Modify role-based access control (RBAC) settings at the workspace level.

C. Enable Secure Shell Protocol (SSH) access.

D. Modify role-based access control (RBAC) settings at the compute instance level.

Q46. Introductory Info Case study – 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 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? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

Select 3 option(s):

A. Create Scatterplot

B. Summarize Data

C. Clip Values

D. Replace Discrete Values

E. Build Counting Transform

Q48. Introductory Info Case study – 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 You need to select a feature extraction method. Which method should you use?

Select 1 option(s):

A. Mutual information

B. Pearson‘s correlation

C. Spearman correlation

D. Fisher Linear Discriminant Analysis

Q49. Introductory Info Case study – 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 You need to select a feature extraction method. Which method should you use?

Select 1 option(s):

A. Mutual information

B. Mood‘s median test

C. Kendall correlation

D. Permutation Feature Importance

Q49. You create a binary classification model. You use the Fairlearn package to assess model fairness.You must eliminate the need to retrain the model.You need to implement the Fairlearn package.Which algorithm should you use?

Select 1 option(s):

A. fairlearn.reductions.ExponentiatedGradient

B. fairlearn.postprocessing.ThresholdOptimizer

C. fairlearnpreprocessing.CorrelationRemover

D. fairlearn.reductions.GridSearch

Q50. You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.Which of the following DSVM should you create?

Select 1 option(s):

A. Windows Server 2012 DSVM

B. Windows Server 2016 DSVM

C. Ubuntu 16.04 DSVM

D. CentOS 7.4 DSVM

Q51. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.You are preparing to create a virtual machine that has the necessary tools built into it.You need to make use of the correct virtual machine type.Recommendation: You make use of a Geo AI Data Science Virtual Machine (Geo-DSVM) Windows edition.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q52. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.You are preparing to create a virtual machine that has the necessary tools built into it.You need to make use of the correct virtual machine type.Recommendation: You make use of a Deep Learning Virtual Machine (DLVM) Windows edition.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q53. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with employing a machine learning model, which makes use of a PostgreSQL database and needs GPU processing, to forecast prices.You are preparing to create a virtual machine that has the necessary tools built into it.You need to make use of the correct virtual machine type.Recommendation: You make use of a Data Science Virtual Machine (DSVM) Windows edition.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q54. You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).Which of the following actions should you take?

Select 1 option(s):

A. You should consider including Rattle.

B. You should consider including TensorFlow.

C. You should consider including Theano.

D. You should consider including Chainer.

Q55. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.Recommendation: You make use of the Replace with median option.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q56. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.Recommendation: You make use of the Remove entire row option.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q57. You are planning to host practical training to acquaint learners with data visualization creation using Python. Learner devices are able to connect to the internet.Learner devices are currently NOT configured for Python development. Also, learners are unable to install software on their devices as they lack administrator permissions. Furthermore, they are unable to access Azure subscriptions.It is imperative that learners are able to execute Python-based data visualization code.Which of the following actions should you take?

Select 1 option(s):

A. You should consider configuring the use of Azure Container Instance.

B. You should consider configuring the use of Azure BatchAI.

C. You should consider configuring the use of Azure Notebooks.

D. You should consider configuring the use of Azure Kubernetes Service.

Q58. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.Recommendation: You configure the use of the value k=3.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q59. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You have been tasked with evaluating your model on a partial data sample via k-fold cross-validation.You have already configured a k parameter as the number of splits. You now have to configure the k parameter for the cross-validation with the usual value choice.Recommendation: You configure the use of the value k=1.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q60. You construct a machine learning experiment via Azure Machine Learning Studio.You would like to split data into two separate datasets.Which of the following actions should you take?

Select 1 option(s):

A. You should make use of the Split Data module.

B. You should make use of the Group Categorical Values module.

C. You should make use of the Clip Values module.

D. You should make use of the Group Data into Bins module.

Q61. You have been tasked with creating a new Azure pipeline via the Machine Learning designer.You have to makes sure that the pipeline trains a model using data in a comma-separated values (CSV) file that is published on a website. A dataset for the file for this file does not exist.Data from the CSV file must be ingested into the designer pipeline with the least amount of administrative effort as possible.Which of the following actions should you take?

Select 1 option(s):

A. You should make use of the Convert to TXT module.

B. You should add the Copy Data object to the pipeline.

C. You should add the Import Data object to the pipeline.

D. You should add the Dataset object to the pipeline.

Q62. You need to consider the underlined segment to establish whether it is accurate.To improve the amount of low incidence cases in a dataset, you should make use of the SMOTE module.Select ′No adjustment required′ if the underlined segment is accurate. If the underlined segment is inaccurate, select the accurate option.

Select 1 option(s):

A. No adjustment required.

B. Remove Duplicate Rows

C. Join Data

D. Edit Metadata

Q63. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of carrying out feature engineering on a dataset.You want to add a feature to the dataset and fill the column value.Recommendation: You must make use of the Join Data Azure Machine Learning Studio module.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q64. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of creating a machine learning model. Your dataset includes rows with null and missing values.You plan to make use of the Clean Missing Data module in Azure Machine Learning Studio to detect and fix the null and missing values in the dataset.Recommendation: You make use of the Custom substitution value option.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Q65. This question is included in a number of questions that depicts the identical set-up. However, every question has a distinctive result. Establish if the recommendation satisfies the requirements.You are in the process of carrying out feature engineering on a dataset.You want to add a feature to the dataset and fill the column value.Recommendation: You must make use of the Edit Metadata Azure Machine Learning Studio module.Will the requirements be satisfied?

Select 1 option(s):

A. Yes

B. No

Microsoft Azure Test 12

Q2. You have recently concluded the construction of a binary classification machine learning model.You are currently assessing the model. You want to make use of a visualization that allows for precision to be used as the measurement for the assessment.Which of the following actions should you take?

Q5. You have been tasked with ascertaining if two sets of data differ considerably. You will make use of Azure Machine Learning Studio to complete your task.You plan to perform a paired t-test.Which of the following are conditions that must apply to use a paired t-test? (Choose all that apply.)

Q14. You make use of Azure Machine Learning Studio to develop a linear regression model. You perform an experiment to assess various algorithms.Which of the following is an algorithm that reduces the variances between actual and predicted values?

Q20. You are implementing a machine learning model to predict stock prices.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?

Q22. 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?

Q25. 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?

Q27. 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?

Q28. 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.

Q29. You plan to create a speech recognition deep learning model.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?

Q31. 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?

Q36. You are solving a classification task.The dataset is imbalanced.You need to select an Azure Machine Learning Studio module to improve the classification accuracy.Which module should you use?

Q40. You are moving a large dataset from Azure Machine Learning Studio to a Weka environment.You need to format the data for the Weka environment.Which module should you use?

Q41. You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and PyTorch.You need to select a pre-configured DSVM to support the frameworks.What should you create?

Q43. You are with a time series dataset in Azure Machine Learning Studio.You need to split your dataset into training and testing subsets by using the Split Data module.Which splitting mode should you use?

Q45. You plan to create a compute instance as part of an Azure Machine Learning development workspace.You must interactively debug code running on the compute instance by using Visual Studio Code Remote.You need to provision the compute instance.What should you do?

Q49. You create a binary classification model. You use the Fairlearn package to assess model fairness.You must eliminate the need to retrain the model.You need to implement the Fairlearn package.Which algorithm should you use?

Q50. You need to implement a Data Science Virtual Machine (DSVM) that supports the Caffe2 deep learning framework.Which of the following DSVM should you create?

Q54. You have been tasked with designing a deep learning model, which accommodates the most recent edition of Python, to recognize language.You have to include a suitable deep learning framework in the Data Science Virtual Machine (DSVM).Which of the following actions should you take?

Q60. You construct a machine learning experiment via Azure Machine Learning Studio.You would like to split data into two separate datasets.Which of the following actions should you take?

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