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Microsoft DP-100 Sample Questions
Question # 1
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 acomplete solution.NOTE: Each correct selection is worth one point.
A. Bulk Insert SQL Query
B. AzCopy
C. Python script
D. Azure Storage Explorer
E. Bulk Copy Program (BCP)
Explanation:You can move data to and from Azure Blob storage using different technologies:Azure Storage-ExplorerAzCopyPythonSSIS
Question # 2
You are building a regression model tot estimating the number of calls during an event.You need to determine whether the feature values achieve the conditions to build aPoisson regression model.Which two conditions must the feature set contain? I ach correct answer presents part ofthe solution. NOTE: Each correct selection is worth one point.
A. The label data must be a negative value.
B. The label data can be positive or negative,
C. The label data must be a positive value
D. The label data must be non discrete.
E. The data must be whole numbers.
Explanation:Poisson regression is intended for use in regression models that are used to predictnumeric values, typically counts. Therefore, you should use this module to create yourregression model only if the values you are trying to predict fit the following conditions:The response variable has a Poisson distribution.Counts cannot be negative. The method will fail outright if you attempt to use itwith negative labels.A Poisson distribution is a discrete distribution; therefore, it is not meaningful touse this method with non-whole numbers.
Question # 3
You are performing a filter based feature selection for a dataset 10 build a multi classclassifies 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 keypredictors. Which method should you use?
A. Chi-squared
B. Spearman correlation
C. Kendall correlation
D. Person correlation
Explanation:Pearson’s correlation statistic, or Pearson’s correlation coefficient, is also known instatistical models as the r value. For any two variables, it returns a value that indicates thestrength of the correlationPearson’s correlation coefficient is the test statistics that measures the statisticalrelationship, or association, between two continuous variables. It is known as the bestmethod of measuring the association between variables of interest because it is based onthe method of covariance. It gives information about the magnitude of the association, orcorrelation, as well as the direction of the relationship.
Question # 4
Note: This question is part of a series of questions that present the same scenario. Eachquestion in the series contains a unique solution that might meet the stated goals. Somequestion 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
Explanation:Instead use the Synthetic Minority Oversampling Technique (SMOTE) sampling mode.Note: SMOTE is used to increase the number of underepresented cases in a dataset usedfor machine learning. SMOTE is a better way of increasing the number of rare cases thansimply duplicating existing cases.
Question # 5
You plan to use a Deep Learning Virtual Machine (DLVM) to train deep learning modelsusing Compute Unified Device Architecture (CUDA) computations.You need to configure the DLVM to support CUDA.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
Explanation:A Deep Learning Virtual Machine is a pre-configured environment for deep learning usingGPU instances.
Question # 6
Note: This question is part of a series of questions that present the same scenario. Eachquestion in the series contains a unique solution that might meet the stated goals. Somequestion 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 trainingYou 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
Explanation:SMOTE is used to increase the number of underepresented cases in a dataset used formachine learning. SMOTE is a better way of increasing the number of rare cases thansimply duplicating existing cases.
Question # 7
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
Explanation: Coefficient of determination, often referred to as R2, represents the predictive power of themodel as a value between 0 and 1. Zero means the model is random (explains nothing); 1means there is a perfect fit. However, caution should be used in interpreting R2 values, aslow values can be entirely normal and high values can be suspect.
Question # 8
You are building a recurrent neural network to perform a binary classification. You reviewthe training loss, validation loss, training accuracy, and validation accuracy for each training epoch.You need to analyze model performance.Which observation indicates that the classification model is over fitted?
A. The training loss .stays constant and the validation loss stays on a constant value andclose to the training loss value when training the model.
B. The training loss increases while the validation loss decreases when training the model.
C. The training loss decreases while the validation loss increases when training the model.
D. The training loss stays constant and the validation loss decreases when training the model.
Question # 9
Note: This question is part of a series of questions that present the same scenario. Eachquestion in the series contains a unique solution that might meet the stated goals. Somequestion sets might have more than one correct solution, while others might not have a correct solution.After you answer a question in this section, you will NOT be able to return to it. As a result,these questions will not appear in the review screen.You are creating a model to predict the price of a student’s artwork depending on thefollowing variables: the student’s length of education, degree type, and art form.You start by creating a linear regression model.You need to evaluate the linear regression model.Solution: Use the following metrics: Mean Absolute Error, Root Mean Absolute Error,Relative Absolute Error, Relative Squared Error, and the Coefficient of Determination.Does the solution meet the goal?
A. Yes
B. No
Explanation:The following metrics are reported for evaluating regression models. When you comparemodels, they are ranked by the metric you select for evaluation.Mean absolute error (MAE) measures how close the predictions are to the actualoutcomes; thus, a lower score is better.Root mean squared error (RMSE) creates a single value that summarizes the error in themodel. By squaring the difference, the metric disregards the difference between overprediction and under-prediction.Relative absolute error (RAE) is the relative absolute difference between expected andactual values; relative because the mean difference is divided by the arithmetic mean.Relative squared error (RSE) similarly normalizes the total squared error of the predictedvalues by dividing by the total squared error of the actual values.Mean Zero One Error (MZOE) indicates whether the prediction was correct or not. In otherwords: ZeroOneLoss(x,y) = 1 when x!=y; otherwise 0.Coefficient of determination, often referred to as R2, represents the predictive power of themodel as a value between 0 and 1. Zero means the model is random (explains nothing); 1means there is a perfect fit. However, caution should be used in interpreting R2 values, aslow values can be entirely normal and high values can be suspect.AUC.
Question # 10
You are creating a binary classification by using a two-class logistic regression model.You need to evaluate the model results for imbalance.Which evaluation metric should you use?
A. Relative Absolute Error
B. AUC Curve
C. Mean Absolute Error
D. Relative Squared Error
Explanation:One can inspect the true positive rate vs. the false positive rate in the Receiver OperatingCharacteristic (ROC) curve and the corresponding Area Under the Curve (AUC) value. Thecloser this curve is to the upper left corner, the better the classifier’s performance is (that ismaximizing the true positive rate while minimizing the false positive rate). Curves that areclose to the diagonal of the plot, result from classifiers that tend to make predictions thatare close to random guessing.
Question # 11
You are creating a machine learning model.You need to identify outliers in the data.Which two visualizations can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.NOTE: Each correct selection is worth one point.
A. box plot
B. scatter
C. random forest diagram
D. Venn diagram
E. ROC curve
Explanation:The box-plot algorithm can be used to display outliers.One other way to quickly identify Outliers visually is to create scatter plots.
Question # 12
Note: This question is part of a series of questions that present the same scenario. Eachquestion in the series contains a unique solution that might meet the stated goals. Somequestion 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
Explanation:Use the Entropy MDL binning mode which has a target column.
Question # 13
You create a binary classification model.You need to evaluate the model performance.Which two metrics can you use? Each correct answer presents a complete solution.NOTE: Each correct selection is worth one point.
A. relative absolute error
B. precision
C. accuracy
D. mean absolute error
E. coefficient of determination
Explanation:The evaluation metrics available for binary classification models are: Accuracy, Precision,Recall, F1 Score, and AUC.Note: A very natural question is: ‘Out of the individuals whom the model, how many wereclassified correctly (TP)?’This question can be answered by looking at the Precision of the model, which is theproportion of positives that are classified correctly.
Question # 14
You plan to use a Data Science Virtual Machine (DSVM) with the open source deeplearning frameworks Caffe2 and Theano. You need to select a pre configured DSVM tosupport 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)
Question # 15
You plan to build a team data science environment. Data for training models in machinelearning pipelines willbe 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 themachine learning pipelines and train models on their personal devices in bothconnected and disconnected network environments.Personal devices must support updating machine learning pipelines whenconnected to a network.You need to select a data science environment.Which environment should you use?
A. Azure Machine Learning Service
B. Azure Machine Learning Studio
C. Azure Databricks
D. Azure Kubernetes Service (AKS)
Explanation:The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft’s Azurecloud built specifically for doing data science. Caffe2 and Chainer are supported by DSVM.DSVM integrates with Azure Machine Learning.
Question # 16
You plan to deliver a hands-on workshop to several students. The workshop will focus on creating datavisualizations using Python. Each student will use a device that has internet access.Student devices are not configured for Python development. Students do not haveadministrator access toinstall software on their devices. Azure subscriptions are not available for students.You need to ensure that students can run Python-based data visualization code.Which Azure tool should you use?
A. Anaconda Data Science Platform
B. Azure BatchAl
C. Azure Notebooks
D. Azure Machine Learning Service
Question # 17
You are creating a new experiment in Azure Machine Learning Studio. You have a smalldataset that has missing values in many columns. The data does not require theapplication of predictors for each column. You plan to use the Clean Missing Data moduleto 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
Explanation:Replace using Probabilistic PCA: Compared to other options, such as Multiple Imputationusing Chained Equations (MICE), this option has the advantage of not requiring theapplication of predictors for each column. Instead, it approximates the covariance for thefull dataset. Therefore, it might offer better performance for datasets that have missingvalues in many columns.
Question # 18
You are developing deep learning models to analyze semi-structured, unstructured, andstructured data types.You have the following data available for model building:Video recordings of sporting eventsTranscripts of radio commentary about eventsLogs from related social media feeds captured during sporting eventsYou need to select an environment for creating the model.Which environment should you use?
A. Azure Cognitive Services
B. Azure Data Lake Analytics
C. Azure HDInsight with Spark MLib
D. Azure Machine Learning Studio
Explanation:Azure Cognitive Services expand on Microsoft’s evolving portfolio of machine learning APIsand enable developers to easily add cognitive features – such as emotion and videodetection; facial, speech, and vision recognition; and speech and language understanding– into their applications. The goal of Azure Cognitive Services is to help developers createapplications that can see, hear, speak, understand, and even begin to reason. The catalogof services within Azure Cognitive Services can be categorized into five main pillars -Vision, Speech, Language, Search, and Knowledge.
Question # 19
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?
A. Split Data
B. Load Trained Model
C. Assign Data to Clusters
D. Group Data into Bins
Explanation:The Group Data into Bins module supports multiple options for binning data. You cancustomize how the bin edges are set and how values are apportioned into the bins.
Question # 20
You are evaluating a completed binary classification machine learning model.You need to use the precision as the valuation metric.Which visualization should you use?
A. Binary classification confusion matrix
B. box plot
C. Gradient descent
D. coefficient of determination
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