DP-100 Exam

Spearman correlation coefficient

statistical measure of the strength of a monotonic relationship between paired data. The closer r is to +/- 1 the stronger the monotonic relationship Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.

You need to configure the Edit Metadata module so that the structure of the datasets match. Which configuration options should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Choose one of (Flaoting Point, DateTime, TimeSpan, Integer) Choose one of (Unchanged, Make Categorical, Make Uncategorical)

1. TimeSpan 2. Unchanged

You use Data Science Virtual Machines (DSVMs) for Windows and Linux in Azure. You need to access the DSVMs. Which utilities should you use? To answer, select the appropriate options in the answer area. 1. User terminal sessions to access a DSVM for linux (choose 1 out of: SSH Client, X2Go, JupyterLab, Remote Desktop) 2. Access Jupyter notebooks on a DSVM for linux (choose 1 out of: SSH Client, X2Go, JupyterLab, Remote Desktop) 3. Access Jupyter notebooks on a DSVM for linux (choose 1 out of: SSH Client, X2Go, JupyterLab, Remote Desktop) 4. Access a DSVM for windows (choose 1 out of: SSH Client, X2Go, JupyterLab, Remote Desktop)

1. X2Go 2. Remote Desktop 3. SSH Client 4. JupterLab

Linear Discriminant Analysis

used as a dimensionality reduction technique. It is commonly used in the pre-processing step in machine learning and pattern classification projects. helps to reduce high-dimensional data set onto a lower-dimensional space. The goal is to do this while having a decent separation between classes and reducing resources and costs of computing. The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables. Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.

You need to select a pre built development environment for a series of data science experiments. You must use the R language for the experiments. Which three environments can you use? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. A . M B . NET Library on a local environment C . Azure Machine Learning Studio D . Data Science Virtual Machine (OSVM) E . Azure Data bricks F . Azure Cognitive Services

Answer: ABD

You need to implement a scaling strategy for the local penalty detection data. Which normalization type should you use? A . Streaming B . Weight C . Batch D . Cosine

Answer: C Explanation: Post batch normalization statistics (PBN) is the Microsoft Cognitive Toolkit (CNTK) version of how to evaluate the population mean and variance of Batch Normalization which could be used in inference Original Paper. In CNTK, custom networks are defined using the BrainScriptNetworkBuilder and described in the CNTK network description language "BrainScript." Scenario: Local penalty detection models must be written by using BrainScript.

Define an evaluation strategy for crowd sentiment models. Which three actions to perform in sequence? 1. Add new features for retraining supervised models. 2. Filter labeled cases for retraining using the shortest distance from centroids. 3. Evaluate the changes in correlation between model error rate and centroid distance 4. Impute unavailable features with centroid aligned models 5. Filter labeled cases for retraining using the longest distance from centroids 6. Remove features before retraining supervised models

1. Add new features for retraining supervised models. 2. Evaluate the changes in correlation between model error rate and centroid distance 3. Filter labeled cases for retraining using the longest distance from centroids 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. Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.

You need to correct the model fit issue. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Build the global model using Microsoft Cognitive Toolkit (CNTK) 2. Import the global model and build a local model using CNTK 3. Export the global model using Neural Network Exchange Format (NNEF) 4. Import the global model and build the local model using PyTorch 5. Build the global model using PyTorch 6. Build the global model using Tensorflow 7. Import the global model and build the local model using TensorFlow 8. Export the global model using the Open Neural Network Exchange (ONNX) format

1. Build the global model using PyTorch 2. Export the global model using Neural Network Exchange Format (NNEF) 3. Import the global model and build the local model using TensorFlow

You need to correct the model fit issue. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Add the multiclass decision jungle module 2. Add the bayesian linear regression module 3. Augment the data 4. Add the ordinal regression module 5. Decrease the memory size for L-BFGS 6 Add the Two-Class Averaged Perception module 7. Configure the regularization weight

1. Decrease the memory size for L-BFGS 2. Add the bayesian linear regression module 3. Configure the regularization weight

You need to define an evaluation strategy for the crowd sentiment models. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Define a cross-entropy function activation 2. Add cost functions for each target state 3. Evaluate the classification error metric 4. Evaluate the distance error metric 5. Add cost functions for each component metric 6. Define a sigmoid loss function activation

1. Define a cross-entropy function activation 2. Add cost functions for each target state 3. Evaluate the distance error metric 21 July 2019examsLeave a comment Post navigation DRAG DROP You need to define an evaluation strategy for the crowd sentiment models. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. Answer: Explanation: Step 1: Define a cross-entropy function activation When using a neural network to perform classification and prediction, it is usually better to use cross-entropy error than classification error, and somewhat better to use cross-entropy error than mean squared error to evaluate the quality of the neural network. Step 2: Add cost functions for each target state. Step 3: Evaluated the distance error metric.

You need to define a modeling strategy for ad response. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Implement a K-Means Clustering model 2. Use the raw scores as a feature in a Score Matchbox Recommender model 3. Use the cluster as a feature in a Decision Jungle model 4. Use the raw score as a feature in a logistic regression model 5. Implement a sweep clustering

1. Implement a K-means clustering model 2. use the cluster as a feature in a decision jungle model 3. use the raw score as a feature in a score matchbox recommender model Step 1: Implement a K-Means Clustering model Step 2: Use the cluster as a feature in a Decision jungle model. Decision jungles are non-parametric models, which can represent non-linear decision boundaries. Step 3: Use the raw score as a feature in a Score Matchbox Recommender model The goal of creating a recommendation system is to recommend one or more "items" to "users" of the system. Examples of an item could be a movie, restaurant, book, or song. A user could be a person, group of persons, or other entity with item preferences. Scenario: Ad response rated declined. 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. Ad response models must support non-linear boundaries of features.

HOTSPOT You need to build a feature extraction strategy for the local models. How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point.

1. MaxPooling 2. Ma... see question online

HOTSPOT You need to configure the Feature Based Feature Selection module based on the experiment requirements and datasets. How should you configure the module properties? To answer, select the appropriate options in the dialog box in the answer area. NOTE: Each correct selection is worth one point. Choose 1 from (Fisher Score, Chi-squared, Mutual information, Counts) Choose 1 from (MedianValue, AvgRooms/nHouse)

1. Mutual information 2. MedianValue Box 1: Mutual Information. The mutual information score is particularly useful in feature selection because it maximizes the mutual information between the joint distribution and target variables in datasets with many dimensions. Box 2: MedianValue MedianValue is the feature column, , it is the predictor of the dataset. Scenario: 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.

DRAG DROP You configure a Deep Learning Virtual Machine for Windows. You need to recommend tools and frameworks to perform the following: Build deep rwur.il network (DNN) models. Perform interactive data exploration and visualization. Which tools and frameworks should you recommend? To answer, drag the appropriate tools to the correct tasks. Each tool may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content. NOTE: Each correct selection is worth one point. Match two of these 4 (Vowpal Wabbit, PowerBI Desktop, Azure Data Factory, Microsoft Cognitive Toolkit (CNTK)) to the following two: 1. Build DNN models 2. Enable interactive data exploration and visualization

1. PowerBI Desktop 2. Microsoft Cognitive Toolkit (CNTK)

HOTSPOT You need to identify the methods for dividing the data according, to the testing requirements. Which properties should you select? To answer, select the appropriate option-, m the answer area. NOTE: Each correct selection is worth one point. Choose 1 of Partition or sample mode: (assign to folds, sampling, head)

1. Sampling

RAG DROP You need to modify the inputs for the global penalty event model to address the bias and variance issue. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Build ratios 2. Bin the new data 3. Add a K-means clustering module with 10 clusters 4. Select the behavior data 5. Select the location data 6. Perform a primary component analysis

1. Select the behavior data 2. Add a K-Means clustering module with 10 clusters 3. Perform a primary component analysis (PCA)

DRAG DROP YOU have a data-set that contains over 150 features. You use the dataset to train a Support Vector Machine (SVM) binary classifirer. You need to use the Permutation Feature Importance module in Azure Machine Learning Studio to compute a set of feature importance scores for the dataset. In which order should you perform the actions? To answer move al actions from from the list of Actions to the answer area and arrange them in the correct order. 1. Adda a Split Data Module to creae training and test datasets 2. Set the Metric for measuring perforemance property to Classification - Accuracy and then run the experiment 3. Add a Permutation Feature Importance module and connect the trained model and test dataset 4. Add a dataset to the experiment 5. Add a Two-Class Support Vector Machine module to initialize the SVM classifier

1. Set the Metric for measuring perforemance property to Classification - Accuracy and then run the experiment 2. Adda a Split Data Module to creae training and test datasets 3. Add a dataset to the experiment 4. Add a Two-Class Support Vector Machine module to initialize the SVM classifier 5. Add a Permutation Feature Importance module and connect the trained model and test dataset

DRAG DROP You need to visually identify whether outliers exist in the Age column and quantify the outliers before the outliers are removed. Which three Azure Machine Learning Studio modules should you use in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order. 1. Compute Linear Correlation 2. Create scatterplot module 3. Build counting transform 4. Clip values 5. Summarize data 6. Latent dirichlet allocation 7. Feature hashing 8. Replace discrete values

1. Summarize data 2. Create scatterplot module 3. Feature hashing

DRAG DROP You need to produce a visualization for the diagnostic test evaluation according to the data visualization requirements. Which three modules should you recommend be used in sequence? To answer, move the appropriate modules from the list of modules to the answer area and arrange them in the correct order. 1. Score Matchbox Recommender 2. Apply Transformation 3. Evaluate Recommender 4. Evaluate model 5. Train model 6. Sweep clustering 7. Score model 8. Load trained model

1. Sweep clustering 2. Train model 3. Evaluate model Step 1: Sweep Clustering Start by using the "Tune Model Hyperparameters" module to select the best sets of parameters for each of the models we're considering. One of the interesting things about the "Tune Model Hyperparameters" module is that it not only outputs the results from the Tuning, it also outputs the Trained Model. Step 2: Train Model Step 3: Evaluate Model Scenario: 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.

You 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 each description? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. True Positive Question...

1. TP 2. TN 3. FP 4. FN

DRAG DROP You are producing a multiple linear regression model in Azure Machine learning Studio. Several independent variables are highly correlated. You need to select appropriate methods for conducting elective feature engineering on all the data. Which three actions should you perform in sequence? To answer, move the appropriate Actions from the list of actions to the answer area and arrange them in the correct order. 1. Evaluate the probability function 2. Build a counting transform 3. Remove duplicate rows 4. Use the Filter Based Feature Selection module 5. Test the hypothesis using t-Test 6. Compute linear correlation

1. Use the Filter Based Feature Selection module 2. Build a counting transform 3. Test the hypothesis using t-Test

DRAG DROP You need to define a process for penalty event detection. Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order. 1. Standardize to mono audio clips 2. Vary the length of sliding windows between modelling epochs 3. Vary the length of frequency bands between modelling epochs 4. Use an Inverse Fourier transform on frequency changes over time 5. use a Fast Fourier transform on frequency changes over time 6. Standardize to stereo audio clips

1. Vary the length of frequency bands between modelling epochs 2. Standardize to stereo audio clips 3. Use an Inverse Fourier transform on frequency changes over time

Case study Overview You are a data scientist for Fabrikam Residences, a company specializing in quality private and commercial property in the United States. Fabrikam Residences is considering expanding into Europe and has asked you to investigate prices for private residences in major European cities. You use Azure Machine Learning Studio to measure the median value of properties. You produce a regression model to predict property prices by using the Linear Regression and Bayesian Linear Regression modules. Datasets There are two datasets in CSV format that contain property details for two cities, London and Paris, with the following columns: The two datasets have been added to Azure Machine Learning Studio as separate datasets and included as the starting point of the experiment. Dataset issues The AccessibilityToHighway column in both datasets contains missing values. The missing data must be replaced with new data so that it is modeled conditionally using the other variables in the data before filling in the missing values. Columns in each dataset contain missing and null values. The dataset also contains many outliers. The Age column has a high proportion of outliers. You need to remove the rows that have outliers in the Age column. The MedianValue and AvgRoomsinHouse columns both hold data in numeric format. You need to select a feature selection algorithm to analyze the relationship between the two columns in more detail. Model fit The model shows signs of overfitting. You need to produce a more refined regression model that reduces the overfitting. Experiment requirements You must set up the experiment to cross-validate the Linear Regression and Bayesian Linear Regression modules to evaluate performance. In each case, the predictor of the dataset is the column named MedianValue. An initial investigation showed that the datasets are identical in structure apart from the MedianValue column. The smaller Paris dataset contains the MedianValue in text format, whereas the larger London dataset contains the MedianValue in numerical format. You must ensure that the datatype of the MedianValue column of the Paris dataset matches the structure of the London dataset. You must prioritize the columns of data for predicting the outcome. You must use non-parameters statistics to measure the relationships. You must use a feature selection algorithm to analyze the relationship between the MedianValue and AvgRoomsinHouse columns. Model training Given a trained model and a test dataset, you need to compute the permutation feature importance scores of feature variables. You need to set up the Permutation Feature Importance module to select the correct metric to investigate the model's accuracy and replicate the findings. You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful. You are concerned that the model might not efficiently use compute resources in hyperparameter tuning. You also are concerned that the model might prevent an increase in the overall tuning time. Therefore, you need to implement an early stopping criterion on models that provides savings without terminating promising jobs. Testing You must produce multiple partitions of a dataset based on sampling using the Partition and Sample module in Azure Machine Learning Studio. You must create three equal partitions for cross-validation. You must also configure the cross-validation process so that the rows in the test and training datasets are divided evenly by properties that are near each city's main river. The data that identifies that a property is near a river is held in the column named NextToRiver. You want to complete this task before the data goes through the sampling process. When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent. Data visualization You need to provide the test results to the Fabrikam Residences team. You create data visualizations to aid in presenting the results. You must produce a Receiver Operating Characteristic (ROC) curve to conduct a diagnostic test evaluation of the model. You need to select appropriate methods for producing the ROC curve in Azure Machine Learning Studio to compare the Two-Class Decision Forest and the Two-Class Decision Jungle modules with one another. DRAG DROP You need to implement early stopping criteria as suited in the model training requirements. Which three code segments should you use to develop the solution? To answer, move the appropriate code segments from the list of code segments to the answer area and arrange them in the correct order. NOTE: More than one order of answer choices is correct. You will receive credit for any of the correct orders you select. 1. early_termination_poliy=TruncationSelectionPolicy(evaluation_interval=1,truncation_percentage=20,delay_evaluation=5) 2. import TruncationSelectionPolicy 3. from azureml.train.hyperdrive 4. import BanditPolicy 5. early_termination_policy=BanditPolicy(slack_factor=0.1,evaluation_interval=1,delay_evaluation=5)

1. from azureml.train.hyperdrive 2. import TruncationSelectionPolicy 3. early_termination_poliy=TruncationSelectionPolicy(evaluation_interval=1,truncation_percentage=20,delay_evaluation=5) You need to implement an early stopping criterion on models that provides savings without terminating promising jobs. Truncation selection cancels a given percentage of lowest performing runs at each evaluation interval. Runs are compared based on their performance on the primary metric and the lowest X% are terminated. Example: from azureml.train.hyperdrive import TruncationSelectionPolicy early_termination_policy = TruncationSelectionPolicy(evaluation_interval=1, truncation_percentage=20, delay_evaluation=5) Incorrect Answers: Bandit is a termination policy based on slack factor/slack amount and evaluation interval. The policy early terminates any runs where the primary metric is not within the specified slack factor / slack amount with respect to the best performing training run. Example: from azureml.train.hyperdrive import BanditPolicy early_termination_policy = BanditPolicy(slack_factor = 0.1, evaluation_interval=1, delay_evaluation=5

You are creating a machine learning model in Python. The provided dataset contains several numerical columns and one text column. •Biker •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. NOTE: Each correct selection is worth one point. Choose 1 from: import (pandas as df, numpy as df, scipy as df) Choose 1 from: dataset['ProductCategory'] (map(ProductCategoryMapping), reduce(ProductCategoryMapping), transpose(ProductCategoryMapping))

1. import scipy as df 2. map(ProductCategoryMapping)

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 Learning learning Studio. One class has a much smaller number of observations than the other classes in the training You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode. Does the solution meet the goal? A . Yes B . No

Answer: A

You are performing a filter based feature selection for a dataset 10 build a multi class classifies by using Azure Machine Learning Studio. The dataset contains categorical features that are highly correlated to the output label column. You need to select the appropriate feature scoring statistical method to identify the key predictors. Which method should you use? A . Chi-squared B . Spearman correlation C . Kendall correlation D . Person correlation

Answer: A

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? A . Fisher Linear Discriminant Analysis. B . Filter Based Feature Selection C . Synthetic Minority Oversampling Teachnique (SMOTE) D . Permutation Feature Importance

Answer: A

You plan to use a Data Science Virtual Machine (DSVM) with the open source deep learning frameworks Caffe2 and Theano. You need to select a pre configured DSVM to support the framework. What should you create? A . Data Science Virtual Machine for Linux (CentOS) B . Data Science Virtual Machine for Windows 2012 C . Data Science Virtual Machine for Windows 2016 D . Geo AI Data Science Virtual Machine with ArcGIS E . Data Science Virtual Machine for Linux (Ubuntu)

Answer: A

You are developing a data science workspace that uses an Azure Machine Learning service. You need to select a compote target to deploy the workspace. What should you use? A . Azure Data Lake Analytics B . Azure Databrick . C . Apache Spark for HDInsight. D . Azure Container Service

Answer: A Azure Data Lake Analytics: Azure Data Lake Analytics is an on-demand analytics job service that simplifies big data. Instead of deploying, configuring, and tuning hardware, you write queries to transform your data and extract valuable insights. The analytics service can handle jobs of any scale instantly by setting the dial for how much power you need. You only pay for your job when it is running, making it cost-effective. Azure Databrick: Azure Databricks is an Apache Spark-based analytics platform optimized for the Microsoft Azure cloud services platform. Designed with the founders of Apache Spark, Databricks is integrated with Azure to provide one-click setup, streamlined workflows, and an interactive workspace that enables collaboration between data scientists, data engineers, and business analysts. Apache Spark for HDInsight: Apache Spark is a parallel processing framework that supports in-memory processing to boost the performance of big-data analytic applications. Apache Spark in Azure HDInsight is the Microsoft implementation of Apache Spark in the cloud. HDInsight makes it easier to create and configure a Spark cluster in Azure. Spark clusters in HDInsight are compatible with Azure Storage and Azure Data Lake Storage. So you can use HDInsight Spark clusters to process your data stored in Azure. For the components and the versioning information, see Apache Hadoop components and versions in Azure HDInsight. Azure Container Service: Azure Container Service allows you to quickly deploy a production ready Kubernetes, DC/OS, or Docker Swarm cluster

You are a data scientist creating a linear regression model. You need to determine how closely the data fits the regression line. Which metric should you review? A . Coefficient of determination B . Recall C . Precision D . Mean absolute error E . Root Mean Square Error

Answer: A Explanation: Coefficient of determination, often referred to as R2, represents the predictive power of the model as a value between 0 and 1. Zero means the model is random (explains nothing); 1 means there is a perfect fit. However, caution should be used in interpreting R2 values, as low values can be entirely normal and high values can be suspect. coefficient of determination: assesses how well a model explains and predicts future outcomes. is the proportion of the variance in the dependent variable that is predictable from the independent variable recall: is the fraction of relevant instances that have been retrieved over the total amount of relevant instances precision: is the fraction of relevant instances among the retrieved instances mean absolute error: is a measure of difference between two continuous variables. Assume X and Y are variables of paired observations that express the same phenomenon. Examples of Y versus X include comparisons of predicted versus observed, subsequent time versus initial time, and one technique of measurement versus an alternative technique of measurement root mean square error: is a quadratic scoring rule which measures the average magnitude of the error

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 (MDI) binning mode. Does the solution meet the goal? A . Yes B . No

Answer: A Explanation: Entropy MDL binning mode: This method requires that you select the column you want to predict and the column or columns that you want to group into bins. It then makes a pass over the data and attempts to determine the number of bins that minimizes the entropy. In other words, it chooses a number of bins that allows the data column to best predict the target column. It then returns the bin number associated with each row of your data in a column named <colname> quantized. Binning or grouping data (sometimes called quantization) is an important tool in preparing numerical data for machine learning, and is useful in scenarios like these: A column of continuous numbers has too many unique values to model effectively, so you automatically or manually assign the values to groups, to create a smaller set of discrete ranges. For example, you could use entropy scores generated by Group Data into Bins to identify the optimal groupings of data values, and use those groups as features in your model.

Topic 3, Mix Questions Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are 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? A . Yes B . NO

Answer: A Explanation: Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values. Note: Multivariate imputation by chained equations (MICE), sometimes called "fully conditional specification" or "sequential regression multiple imputation" has emerged in the statistical literature as one principled method of addressing missing data. Creating multiple imputations, as opposed to single imputations, accounts for the statistical uncertainty in the imputations. In addition, the chained equations approach is very flexible and can handle variables of varying types (e.g., continuous or binary) as well as complexities such as bounds or survey skip patterns.

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? A . Set the threshold to 0.5 and retrain if weighted Kappa deviates +/- 5% from 0.45. B . Set the threshold to 0.05 and retrain if weighted Kappa deviates +/- 5% from 0.5. C . Set the threshold to 0.2 and retrain if weighted Kappa deviates +/- 5% from 0.6. D . Set the threshold to 0.75 and retrain if weighted Kappa deviates +/- 5% from 0.15.

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

You need to implement a model development strategy to determine a user's tendency to respond to an ad. Which technique should you use? A . Use a Relative Expression Split module to partition the data based on centroid distance. B . Use a Relative Expression Split module to partition the data based on distance travelled to the event. C . Use a Split Rows module to partition the data based on distance travelled to the event. D . Use a Split Rows module to partition the data based on centroid distance.

Answer: A Explanation: Split Data partitions the rows of a dataset into two distinct sets. The Relative Expression Split option in the Split Data module of Azure Machine Learning Studio is helpful when you need to divide a dataset into training and testing datasets using a numerical expression. Relative Expression Split: Use this option whenever you want to apply a condition to a number column. The number could be a date/time field, a column containing age or dollar amounts, or even a percentage. For example, you might want to divide your data set depending on the cost of the items, group people by age ranges, or separate data by a calendar date. Scenario: Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement. The distribution of features across training and production data are not consistent

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 contain 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: Use the last Observation Carried Forward (IOCF) method to impute the missing data points. Does the solution meet the goal? A . Yes B . No

Answer: B

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? A . Yes B . No

Answer: B

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 tin- 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 Principal Components Analysis (PCA) sampling mode. Does the solution meet the goal? A . Yes B . No

Answer: B

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? A . Apache Drill B . Tensorflow C . Rattle D . Weka

Answer: B

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 IXVM to support CUOA What should you implement? A . Intel Software Guard Extensions (Intel SGX) technology B . Solid State Drives (SSD) C . Graphic Processing Unit (GPU) D . Computer Processing Unit (CPU) speed increase by using overcloking E . High Random Access Memory (RAM) configuration

Answer: B

Note: This question is part of a series of questions that present the same scenario. Each question in the series contains a unique solution that might meet the stated goals. Some question sets might have more than one correct solution, while others might not have a correct solution. After you answer a question in this section, you will NOT be able to return to it. As a result, these questions will not appear in the review screen. You are a data scientist using Azure Machine Learning Studio. You need to normalize values to produce an output column into bins to predict a target column. Solution: Apply an Equal Width with Custom Start and Stop binning mode. Does the solution meet the goal? A . Yes B . No

Answer: B Explanation: Use the Entropy MDL binning mode which has a target column.

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: Remove the entire column that contains the missing data point. Does the solution meet the goal? A . Yes B . No

Answer: B Explanation: Use the Multiple Imputation by Chained Equations (MICE) method.

You need to select a feature extraction method. Which method should you use? A . Mutual information B . Mood's median test C . Kendall correlation D . Permutation Feature Importance

Answer: C Explanation: In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's tau coefficient (after the Greek letter τ), is a statistic used to measure the ordinal association between two measured quantities. It is a supported method of the Azure Machine Learning Feature selection. Scenario: When you train a Linear Regression module using a property dataset that shows data for property prices for a large city, you need to determine the best features to use in a model. You can choose standard metrics provided to measure performance before and after the feature importance process completes. You must ensure that the distribution of the features across multiple training models is consistent. Mutual information: of two random variables is a measure of the mutual dependence between the two variables. More specifically, it quantifies the "amount of information" (in units such as shannons, commonly called bits) obtained about one random variable through observing the other random variable. The concept of mutual information is intricately linked to that of entropy of a random variable, a fundamental notion in information theory that quantifies the expected "amount of information" held in a random variable. Mood's median test: Mood's median test is a special case of Pearson's chi-squared test. It is a nonparametric test that tests the null hypothesis that the medians of the populations from which two or more samples are drawn are identical. The data in each sample are assigned to two groups, one consisting of data whose values are higher than the median value in the two groups combined, and the other consisting of data whose values are at the median or below. A Pearson's chi-squared test is then used to determine whether the observed frequencies in each sample differ from expected frequencies derived from a distribution combining the two groups. Kendall correlation: is a statistic used to measure the ordinal association between two measured quantities. A tau test is a non-parametric hypothesis test for statistical dependence based on the tau coefficient. Permutation Feature Importance: Permutation feature importance works by randomly changing the values of each feature column, one column at a time, and then evaluating the model. The rankings provided by permutation feature importance are often different from the ones you get from Filter Based Feature Selection, which calculates scores before a model is created. This is because permutation feature importance doesn't measure the association between a feature and a target value, but instead captures how much influence each feature has on predictions from the model.

You are conducting feature engineering to prepuce data for further analysis. The data includes seasonal patterns on inventory requirements. You need to select the appropriate method to conduct feature engineering on the data. Which method should you use? A . Exponential Smoothing (ETS) function. B . One Class Support Vector Machine module C . Time Series Anomaly Detection module D . Finite Impulse Response (FIR) Filter module.

Answer: D

You need to select an environment that will meet the business and data requirements. Which environment should you use? A . Azure HDInsight with Spark MLlib B . Azure Cognitive Services C . Azure Machine Learning Studio D . Microsoft Machine Learning Server

Answer: D Azure HDInsight with Spark MLlib: HDInsight Spark is an Azure-hosted offering of Apache Spark, a unified, open source, parallel data processing framework supporting in-memory processing to boost big data analytics Azure Cognitive Services: Learn how to build intelligent and supported algorithms into apps, websites, and bots to see, hear, speak, understand, and interpret your user needs Azure Machine Learning Studio: Microsoft Azure Machine Learning Studio is a collaborative, drag-and-drop tool you can use to build, test, and deploy predictive analytics solutions on your data. Machine Learning Studio publishes models as web services that can easily be consumed by custom apps or BI tools such as Excel. Microsoft ML Server: Microsoft Machine Learning Server 9.4.7 is enterprise software for data science, providing R and Python interpreters, base distributions of R and Python, additional high-performance libraries from Microsoft, and an operationalization capability for advanced deployment scenarios. Solutions that you develop in R or Python can be deployed as a web service for direct access or as an upstream component to other solutions.

HOTSPOT You need to replace the missing data in the AccessibilityToHighway columns. How should you configure the Clean Missing Data module? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Cleaning Mode: Choose 1 from (Replace using MICE, Replace with Mean, Replace with Median, Replace with Mode) Cols with all missing values: Choose 1 from (Propagate, Remove)

Box 1: Replace using MICE Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values. Scenario: 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. Box 2: Propagate Cols with all missing values indicate if columns of all missing values should be preserved in the output.

HOTSPOT You need to use the Python language to build a sampling strategy for the global penalty detection models. How should you complete the code segment? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Choose one of following groups: 1. import pytorch as deeplearninglib, import tensorflow as deeplearninglib, import cntk as deeplearninglib 2. train_smapler = deeplearninglib.DistributedSampler(penalty_video_dataset), train_smapler = deeplearninglib.log_uniform_candidate_sampler(penalty_video_dataset), train_smapler = deeplearninglib.WeightedRandowmSampler(penalty_video_dataset), train_smapler = deeplearninglib.all_candidate_sampler(penalty_video_dataset) 3. optimizer = deeplearninglib.optim.SGD(model.parameters().lr=0.01), optimizer = deeplearninglib.train.GradientDescentOptimizer(learning_rate = 0.1) 4. model = deeplearninglib.parallel.Distributed(DataParallel(Model), model = deeplearninglib.nn.parallel.DistributedDataParallelCPU(Model), model = deeplearninglib.keras.Model((, model = deeplearninglib.keras.Sequental([

Box 1: import pytorch as deeplearninglib Box 2: ..DistributedSampler(Sampler)..DistributedSampler(Sampler): Sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with class:`torch.nn.parallel.DistributedDataParallel`. In such case, each process can pass a DistributedSampler instance as a DataLoader sampler, and load a subset of the original dataset that is exclusive to it. Scenario: Sampling must guarantee mutual and collective exclusively between local and global segmentation models that share the same features. Box 3: optimizer = deeplearninglib.train. GradientDescentOptimizer(learning_rate=0.10) Incorrect Answers: ..SGD.. Scenario: All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too slow. Box 4: .. nn.parallel.DistributedDataParallel.. DistributedSampler(Sampler): The sampler that restricts data loading to a subset of the dataset. It is especially useful in conjunction with :class:`torch.nn.parallel.DistributedDataParallel`.

HOTSPOT You need to configure the Permutation Feature Importance module for the model framing requirements. What should you do? To answer, select the appropriate options in the dialog box in the answer area-NOTE: Each correct selection is worth one point. Choose 1 from (0, 500) Choose 1 from (Regression - Root Mean Square Error, Regression - R-squared, Regression - Mean Zero One Error, Regression - Mean Absolute Error)

1. 500 2. Regression - R-squard

HOTSPOT You need to set up the Permutation Feature Importance module according to the model training requirements. Which properties should you select? To answer, select the appropriate options in the answer area. NOTE: Each correct selection is worth one point. Choose 1 of: Metric for measuring performance for classification (F-score, Precision, Recall, Accuracy) Choose 1 of Metric for measuring performance for regression: (Root of mean squared error, R-squared, Mean zero error, Mean absolute error)

1. Accuracy 2. R-squared Box 1: Accuracy Scenario: You want to configure hyperparameters in the model learning process to speed the learning phase by using hyperparameters. In addition, this configuration should cancel the lowest performing runs at each evaluation interval, thereby directing effort and resources towards models that are more likely to be successful. Box 2: R-Squared

Pearson correlation coefficient

A Pearson correlation is a number between -1 and 1 that indicates the extent to which two variables are linearly related, where 1 is total positive linear correlation, 0 is no linear correlation, and −1 is total negative linear correlation. The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.

Topic 1, Case Study 1 Overview You are a data scientist in a company that provides data science for professional sporting events. Models will be global and local market data to meet the following business goals: •Understand sentiment of mobile device users at sporting events based on audio from crowd reactions. •Access a user's tendency to respond to an advertisement. •Customize styles of ads served on mobile devices. •Use video to detect penalty events. Current environment Requirements • Media used for penalty event detection will be provided by consumer devices. Media may include images and videos captured during the sporting event and snared using social media. The images and videos will have varying sizes and formats. • The data available for model building comprises of seven years of sporting event media. The sporting event media includes: recorded videos, transcripts of radio commentary, and logs from related social media feeds feeds captured during the sporting events. •Crowd sentiment will include audio recordings submitted by event attendees in both mono and stereo Formats. Advertisements • Ad response models must be trained at the beginning of each event and applied during the sporting event. • Market segmentation nxxlels must optimize for similar ad resporr.r history. • Sampling must guarantee mutual and collective exclusivity local and global segmentation models that share the same features. • Local market segmentation models will be applied before determining a user's propensity to respond to an advertisement. • Data scientists must be able to detect model degradation and decay. • Ad response models must support non linear boundaries features. • The ad propensity model uses a cut threshold is 0.45 and retrains occur if weighted Kappa deviates from 0.1 +/-5%. • The ad propensity model uses cost factors shown in the following diagram: The ad propensity model uses proposed cost factors shown in the following diagram: Performance curves of current and proposed cost factor scenarios are shown in the following diagram: Penalty detection and sentiment Findings •Data scientists must build an intelligent solution by using multiple machine learning models for penalty event detection. •Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines. •Notebooks must be deployed to retrain by using Spark instances with dynamic worker allocation •Notebooks must execute with the same code on new Spark instances to recode only the source of the data. •Global penalty detection models must be trained by using dynamic runtime graph computation during training. • Local penalty detection models must be written by using BrainScript. • Experiments for local crowd sentiment models must combine local penalty detection data. • Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds. • All shared features for local models are continuous variables. • Shared features must use double precision. Subsequent layers must have aggregate running mean and standard deviation metrics Available. segments During the initial weeks in production, the following was observed: •Ad response rates declined. •Drops were not consistent across ad styles. •The distribution of features across training and production data are not consistent. Analysis shows that of the 100 numeric features on user location and behavior, the 47 features that come from location sources are being used as raw features. A suggested experiment to remedy the bias and variance issue is to engineer 10 linearly uncorrected features. Penalty detection and sentiment •Initial data discovery shows a wide range of densities of target states in training data used for crowd sentiment models. •All penalty detection models show inference phases using a Stochastic Gradient Descent (SGD) are running too stow. •Audio samples show that the length of a catch phrase varies between 25%-47%, depending on region. •The performance of the global penalty detection models show lower variance but higher bias when comparing training and validation sets. Before implementing any feature changes, you must confirm the bias and variance using all training and validation cases. You need to resolve the local machine learning pipeline performance issue. What should you do? A . Increase Graphic Processing Units (GPUs). B . Increase the learning rate. C . Increase the training iterations, D . Increase Central Processing Units (CPUs).

Answer: A

You are creating a new experiment in Azure Machine Learning Studio. You have a small dataset that has missing values in many columns. The data does not require the application of predictors for each column. You plan to use the Clean Missing Data module to handle the missing data. You need to select a data cleaning method. Which method should you use? A . Synthetic Minority Oversampling Technique (SMOTE) B . Replace using MICE C . Replace using; Probabilistic PCA D . Normalization

Answer: A Synthetic Minority Oversampling Technique (SMOTE): is a powerful sampling method that goes beyond simple under or over sampling. This algorithm creates new instances of the minority class by creating convex combinations of neighboring instances. Replace using MICE: For each missing value, this option assigns a new value, which is calculated by using a method described in the statistical literature as "Multivariate Imputation using Chained Equations" or "Multiple Imputation by Chained Equations". With a multiple imputation method, each variable with missing data is modeled conditionally using the other variables in the data before filling in the missing values. In contrast, in a single imputation method (such as replacing a missing value with a column mean) a single pass is made over the data to determine the fill value. All imputation methods introduce some error or bias, but multiple imputation better simulates the process generating the data and the probability distribution of the data. Replace using; Probabilistic PCA: Replaces the missing values by using a linear model that analyzes the correlations between the columns and estimates a low-dimensional approximation of the data, from which the full data is reconstructed. The underlying dimensionality reduction is a probabilistic form of Principal Component Analysis (PCA), and it implements a variant of the model proposed in the Journal of the Royal Statistical Society, Series B 21(3), 611-622 by Tipping and Bishop. Compared to other options, such as Multiple Imputation using Chained Equations (MICE), this option has the advantage of not requiring the application of predictors for each column. Instead, it approximates the covariance for the full dataset. Therefore, it might offer better performance for datasets that have missing values in many columns. The key limitations of this method are that it expands categorical columns into numerical indicators and computes a dense covariance matrix of the resulting data. It also is not optimized for sparse representations. For these reasons, datasets with large numbers of columns and/or large categorical domains (tens of thousands) are not supported due to prohibitive space consumption. Normalization: Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information. Normalization is also required for some algorithms to model the data correctly. For example, assume your input dataset contains one column with values ranging from 0 to 1, and another column with values ranging from 10,000 to 100,000. The great difference in the scale of the numbers could cause problems when you attempt to combine the values as features during modeling. Normalization avoids these problems by creating new values that maintain the general distribution and ratios in the source data, while keeping values within a scale applied across all numeric columns used in the model.

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? A . Yes B . No

Answer: B

Analysis of Variance (ANOVA)

Statistical test that looks for significant differences between means on a particular measure. Researchers conduct an ANOVA when they are interested in determining whether two groups differ significantly on a particular measure or test.