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Microsoft DP-100 Exam Sample Questions

Question # 1

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)

B. main()

C. batch()

D. init()

E. score(mini_batch)

Answer

A. run(mini_batch)
D. init()

Explanation:

[Reference:, https://github.com/Azure/MachineLearningNotebooks/tree/master/how-to-use-azureml/machine-learningpipelines/parallel-run, , ]

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Question # 2

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. Microsoft Machine Learning Server

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Question # 3

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 have an Azure Machine Learning workspace. You connect to a terminal session from the Notebooks page in Azure Machine Learning studio. You plan to add a new Jupyter kernel that will be accessible from the same terminal session. You need to perform the task that must be completed before you can add the new kernel. Solution: Delete the Python 3.8 - AzureML kernel. Does the solution meet the goal?

A. Yes

B. No

Answer

B. No

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Question # 4

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. Increase Graphic Processing Units (GPUs).

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Question # 5

You are implementing hyperparameter tuning by using Bayesian sampling for an Azure ML Python SDK v2-based model training from a notebook. The notebook is in an Azure Machine Learning workspace. The notebook uses a training script that runs on a compute cluster with 20 nodes. The code implements Bandit termination policy with slack_factor set to 02 and a sweep job with max_concurrent_trials set to 10. You must increase effectiveness of the tuning process by improving sampling convergence. You need to select which sampling convergence to use. What should you select?

A. Set the value of slack. factor of earty. termination policy to 0.1.

B. Set the value of max_concurrent_trials to 4.

C. Set the value of slack_factor of eartyjermination policy to 0.9.

D. Set the value of max. concurrentjrials to 20.

Answer

B. Set the value of max_concurrent_trials to 4.

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