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The Microsoft DP-100 exam is created for the individuals who are involved in implementing machine learning techniques. The candidates for this certification test should have an in-depth knowledge of identifying and preparing the development environment, applying scientific data and rigor exploration techniques to acquire actionable insights along with communicating results to the stakeholders. They should also possess the skills in preparing data for modeling and developing models. In addition, they should also be able to apply machine learning techniques to train, deploy and evaluate models to build any AI solutions, which satisfy the business objectives.
The DP-100 Certification Exam is a comprehensive assessment of the candidate's knowledge and skills in designing and implementing data science solutions on Azure. DP-100 exam covers various topics, including data exploration and preparation, machine learning modeling, model deployment, and monitoring. DP-100 exam also tests the candidate's understanding of Azure technologies, such as Azure Machine Learning, Azure Databricks, and Azure Data Factory.
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Microsoft DP-100 is a certification exam that validates one’s ability to design and implement data science solutions on Azure. DP-100 exam is designed for professionals who want to enhance their skills in data science and become certified in Azure data science solutions. DP-100 Exam covers a wide range of topics, including data exploration, data preparation, modeling, and deployment.
NEW QUESTION # 293
You register the following versions of a model.
You use the Azure ML Python SDK to run a training experiment. You use a variable named run to reference the experiment run.
After the run has been submitted and completed, you run the following code:
For each of the following statements, select Yes if the statement is true. Otherwise, select No.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-deploy-and-where
NEW QUESTION # 294
You create machine learning models by using Azure Machine Learning.
You plan to train and score models by using a variety of compute contexts. You also plan to create a new compute resource in Azure Machine Learning studio.
You need to select the appropriate compute types.
Which compute types should you select? To answer, drag the appropriate compute types to the correct requirements. Each compute type may be used once, more than once, or not at all. You may need to drag the split bar between panes or scroll to view content.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 295
You are developing a deep learning model by using TensorFlow. You plan to run the model training workload on an Azure Machine Learning Compute Instance.
You must use CUDA-based model training.
You need to provision the Compute Instance.
Which two virtual machines sizes can you use? To answer, select the appropriate virtual machine sizes in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Explanation:
CUDA is a parallel computing platform and programming model developed by Nvidia for general computing on its own GPUs (graphics processing units). CUDA enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
Reference:
https://www.infoworld.com/article/3299703/what-is-cuda-parallel-programming-for-gpus.html
NEW QUESTION # 296
You have the following code. The code prepares an experiment to run a script:
The experiment must be run on local computer using the default environment.
You need to add code to start the experiment and run the script.
Which code segment should you use?
Answer: D
Explanation:
The experiment class submit method submits an experiment and return the active created run.
Syntax: submit(config, tags=None, **kwargs)
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.experiment.experiment
NEW QUESTION # 297
You use Azure Machine Learning designer to create a real-time service endpoint.
You have a single Azure Machine Learning service compute resource.
You train the model and prepare the real-time pipeline for deployment.
You need to publish the inference pipeline as a web service.
Which compute type should you use?
Answer: A
Explanation:
Azure Kubernetes Service (AKS) can be used real-time inference.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/concept-compute-target
NEW QUESTION # 298
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