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New Microsoft Azure DP-100 Exam Practice Questions

Total 441 Questions

Last Updated On: 17-Mar-2025

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Question # 1

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.

B. Set the value of max_concurrent_trials to 4.

Question # 2

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)

A. run(mini_batch)
D. init()

Explanation:

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

Question # 3

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

C. Azure logic App

Question # 4

You create and register a model in an Azure Machine Learning workspace. You must use the Azure Machine Learning SDK to implement a batch inference pipeline that uses a ParallelRunStep to score input data using the model. You must specify a value for the ParallelRunConfig compute_target setting of the pipeline step. You need to create the compute target. Which class should you use?
A. BatchCompute
B. AdlaCompute
C. AmlCompute
D. Aks Compute

C. AmlCompute

Explanation:

Compute target to use for ParallelRunStep. This parameter may be specified as a compute target object or the string name of a compute target in the workspace.

The compute_target target is of AmlCompute or string.

Note: An Azure Machine Learning Compute (AmlCompute) is a managed-compute infrastructure that allows you to easily create a single or multi-node compute. The compute is created within your workspace region as a resource that can be shared with other users

[Reference:, https://docs.microsoft.com/en-us/python/api/azureml-contrib-pipeline-steps/azureml.contrib.pipeline.steps.parallelrunconfig, , https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.compute.amlcompute(class), , , ]

Question # 5

You use Azure Machine Learning to tram a model. You must use Baylean sampling to Tune hyperparaters. You need to select a learning_rate parameter distribution. Which two distributions can you use? Each correct answer presents a complete solution. NOTE Each correct selection is worth one point.
A. Normal
B. Uniform
C. Choice
D. LogUniform
E. QNormal

B. Uniform
C. Choice

Question # 6

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).

A. Increase Graphic Processing Units (GPUs).

Question # 7

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

B. No

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