Microsoft DP-100 Dumps

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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