Microsoft DP-100 Exam Dumps

Designing and Implementing a Data Science Solution on Azure

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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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