Microsoft DP-100 Exam Dumps

Designing and Implementing a Data Science Solution on Azure

Microsoft DP-100 Sample Questions

Question # 1

You train a model and register it in your Azure Machine Learning workspace. You are ready to deploy the model as a real-time web service. You deploy the model to an Azure Kubernetes Service (AKS) inference cluster, but the deployment fails because an error occurs when the service runs the entry script that is associated with the model deployment. You need to debug the error by iteratively modifying the code and reloading the service, without requiring a re-deployment of the service for each code update. What should you do?

A. Register a new version of the model and update the entry script to load the new version of the model from its registered path. 
B. Modify the AKS service deployment configuration to enable application insights and redeploy to AKS. 
C. Create an Azure Container Instances (ACI) web service deployment configuration and deploy the model on ACI. 
D. Add a breakpoint to the first line of the entry script and redeploy the service to AKS. 
E. Create a local web service deployment configuration and deploy the model to a local Docker container. 

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Question # 2

You create a multi-class image classification deep learning model that uses a set of labeled images. You create a script file named train.py that uses the PyTorch 1.3 framework to train the model. You must run the script by using an estimator. The code must not require any additional Python libraries to be installed in the environment for the estimator. The time required for model training must be minimized. You need to define the estimator that will be used to run the script. Which estimator type should you use?

A. TensorFlow 
B. PyTorch 
C. SKLearn 
D. Estimator 

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Question # 3

You use Azure Machine Learning designer to create a training pipeline for a regression model. You need to prepare the pipeline for deployment as an endpoint that generates predictions asynchronously for a dataset of input data values. What should you do?

A. Clone the training pipeline. 
B. Create a batch inference pipeline from the training pipeline. 
C. Create a real-time inference pipeline from the training pipeline. 
D. Replace the dataset in the training pipeline with an Enter Data Manually module. 

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Question # 4

You use the Azure Machine Learning Python SDK to define a pipeline to train a model. The data used to train the model is read from a folder in a datastore. You need to ensure the pipeline runs automatically whenever the data in the folder changes. What should you do?

A. Set the regenerate_outputs property of the pipeline to True 
B. Create a ScheduleRecurrance object with a Frequency of auto. Use the object to create a Schedule for the pipeline 
C. Create a PipelineParameter with a default value that references the location where the training data is stored 
D. Create a Schedule for the pipeline. Specify the datastore in the datastore property, and the folder containing the training data in the path_on_datascore property 

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Question # 5

You train and register a model in your Azure Machine Learning workspace. You must publish a pipeline that enables client applications to use the model for batch inferencing. You must use a pipeline with a single ParallelRunStep step that runs a Python inferencing script to get predictions from the input data. You need to create the inferencing script for the ParallelRunStep pipeline step. Which two functions should you include? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. run(mini_batch)D 
B. main() 
C. batch() 
D. init() 
E. score(mini_batch) 

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Question # 6

You have an Azure subscription named Sub1 that contains an Azure • a registered MLflow model named Model1 • an online endpoint named Endpoint1 Outbound network connectivity from Endpointl is blocked. You need to deploy ModeM to Endpointl. What should you do first?

A. In Workspacel. create a linked service. 
B. In Subl, create an Azure Machine Learning registry. 
C. In Workspacel. create a package. 
D. In Workspace! create a package. 
E. In Subl, create a private endpoint 

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Question # 7

You train and register an Azure Machine Learning model You plan to deploy the model to an online endpoint You need to ensure that applications will be able to use the authentication method with a non-expiring artifact to access the model. Solution: Create a managed online endpoint with the default authentication settings. Deploy the model to the online endpoint. Does the solution meet the goal?

A. Yes
 B. No 

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Question # 8

You create an Azure Machine Learning pipeline named pipeline1 with two steps that contain Python scripts. Data processed by the first step is passed to the second step. You must update the content of the downstream data source of pipeline1 and run the pipeline again You need to ensure the new run of pipeline1 fully processes the updated content. Solution: Set the allow_reuse parameter of the PythonScriptStep object of both steps to False Does the solution meet the goal? 

A. Yes 
B. No 

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Question # 9

You plan to use the Hyperdrive feature of Azure Machine Learning to determine the optimal hyperparameter values when training a model. You must use Hyperdrive to try combinations of the following hyperparameter values: • learning_rate: any value between 0.001 and 0.1 • batch_size: 16, 32, or 64 You need to configure the search space for the Hyperdrive experiment. Which two parameter expressions should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. a choice expression for learning_rate 
B. a uniform expression for learning_rate 
C. a normal expression for batch_size 
D. a choice expression for batch_size 
E. a uniform expression for batch_size 

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Question # 10

You are training machine learning models in Azure Machine Learning. You use Hyperdrive to tune the hyperparameters. In previous model training and tuning runs, many models showed similar performance. You need to select an early termination policy that meets the following requirements: • accounts for the performance of all previous runs when evaluating the current run • avoids comparing the current run with only the best performing run to date Which two early termination policies should you use? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. 

A. Bandit 
B. Median stopping 
C. Default 
D. Truncation selection 

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Question # 11

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?

A. attached Azure Databricks cluster
 B. Azure Container Instance (ACI) 
C. Azure Kubernetes Service (AKS) inference cluster 
D. Azure Machine Learning Compute Instance 
E. attached virtual machine in a different region 

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Question # 12

You manage an Azure AI Foundry project in your subscription. You deploy a gpt-4o model. You must test the model before you use it in an existing front-end application. You need to adjust the parameters to get more creative responses. Solution: Adjust Presence penalty. Does the solution meet the goal? 

A. Yes 
B. No 

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Question # 13

You manage an Azure Machine Learning workspace named workspace!. You plan to author custom pipeline components by using Azure Machine Learning Python SDK v2. You must transform the Python code into a YAML specification that can be processed by the pipeline service. You need to import the Python library that provides the transformation functionality. Which Python library should you import?

A. azure.ai ml.automl 
B. azure.ai.ml.entities 
C. sklearn 
D. mldesigner

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Question # 14

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?

A. Yes 
B. No 

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Question # 15

You train and publish a machine teaming model. You need to run a pipeline that retrains the model based on a trigger from an external system. What should you configure?

A. Azure Data Catalog 
B. Azure Batch 
C. Azure logic App 

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Question # 16

You manage an Azure Machine Learning workspace. You build a model for which you must configure a Responsible Al dashboard. Based on what you learn from the dashboard, you must perform the following activities: • Determine what must be done to get a desirable outcome from the model. • Identify the features that have the most direct effect on your outcome of interest. You need to select the components to use for the Responsible Al dashboard configuration. Which two components should you add? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

A. error analysis 
B. counterfactuals 
C. causal 
D. explanation 

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Question # 17

You manage an Azure Machine Learning workspace. You plan to import data from Azure Data Lake Storage Gen2. You need to build a URI that represents the storage location. Which protocol should you use?

A. abfss 
B. https 
C. adl 
D. wasbs 

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Question # 18

You have an Azure Machine Learning workspace. You plan to use the workspace to set up automated machine learning training for an image classification model. You need to choose the primary metric to optimize the model training. Which primary metric should you choose?

A. r2_score 
B. mean_absolute_error 
C. accuracy 
D. root_mean_squared_log_error 

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Question # 19

You are a data scientist working for a bank and have used Azure ML to train and register a machine learning model that predicts whether a customer is likely to repay a loan. You want to understand how your model is making selections and must be sure that the model does not violate government regulations such as denying loans based on where an applicant lives. You need to determine the extent to which each feature in the customer data is influencing predictions. What should you do?

A. Enable data drift monitoring for the model and its training dataset. 
B. Score the model against some test data with known label values and use the results to calculate aconfusion matrix. 
C. Use the Hyperdrive library to test the model with multiple hyperparameter values. 
D. Use the interpretability package to generate an explainer for the model. E. Add tags to the model registration indicating the names of the features in the training dataset. 

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Question # 20

You manage an Azure Machine Learning workspace. You must create and configure a compute cluster for a training job by using Python SDK v2. You need to create a persistent Azure Machine Learning compute resource, specifying the fewest possible properties. Which two properties should you define? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point. 

A. max_instances 
B. name 
C. type 
D. Min_instances 
E. size 

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Question # 21

You create a new Azure subscription. No resources are provisioned in the subscription. You need to create an Azure Machine Learning workspace. What are three possible ways to achieve this goal? Each correct answer presents a complete solution. NOTE: Each correct selection is worth one point. 

A. Run Python code that uses the Azure ML SDK library and calls the Workspace.create method with name, subscription_id, resource_group, and location parameters. 
B. Use an Azure Resource Management template that includes a Microsoft.MachineLearningServices/workspaces resource and its dependencies. 
C. Use the Azure Command Line Interface (CLI) with the Azure Machine Learning extension to call the azgroup create function with --name and --location parameters, and then the az ml workspace createfunction, specifying –w and –g parameters for the workspace name and resource group. 
D. Navigate to Azure Machine Learning studio and create a workspace. 
E. Run Python code that uses the Azure ML SDK library and calls the Workspace.get method with name,subscription_id, and resource_group parameters. 

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Question # 22

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)

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Question # 23

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.

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Question # 24

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

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Question # 25

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

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Question # 26

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

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Question # 27

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

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Question # 28

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

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Question # 29

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.

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Question # 30

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

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Question # 31

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

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Question # 32

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

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Question # 33

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

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Question # 34

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

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Question # 35

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)

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Question # 36

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)

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Question # 37

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

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