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Microsoft DP-100 certification exam is a valuable credential for data scientists and machine learning engineers who want to demonstrate their proficiency in designing and implementing data science solutions on Azure. DP-100 exam covers a wide range of topics related to data science and machine learning and requires candidates to have a deep understanding of Azure data services. To prepare for the exam, candidates can take advantage of various resources provided by Microsoft, including online training courses, study guides, and practice exams.
The DP-100 Exam is a hands-on practical exam that requires the candidate to demonstrate their ability to design and implement data science solutions on Azure. DP-100 exam includes various tasks, such as creating machine learning models, building data pipelines, and deploying models to production environments. The tasks are designed to simulate real-world scenarios that data scientists and engineers face in their jobs.
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The Microsoft DP-100 Exam covers a range of topics related to data science, including data ingestion, transformation, and storage. Candidates will be tested on their ability to design and implement solutions using Azure tools and services, such as Azure Machine Learning, Azure Cognitive Services, and Azure Data Factory. They will also be tested on their ability to work with big data technologies such as Hadoop and Spark.
Microsoft Designing and Implementing a Data Science Solution on Azure Sample Questions (Q77-Q82):
NEW QUESTION # 77
You use Azure Machine Learning Studio to build a machine learning experiment.
You need to divide data into two distinct datasets.
Which module should you use?
Answer: B
Explanation:
Explanation
Partition and Sample with the Stratified split option outputs multiple datasets, partitioned using the rules you specified.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/partition-and-sample
NEW QUESTION # 78
You need to configure the Permutation Feature Importance module for the model framing requirements.
What should you do? To answer, select the appropriate options in the dialog box in the answer area-NOTE:
Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 79
You create an Azure Machine Learning compute target named ComputeOne by using the STANDARD_D1 virtual machine image.
You define a Python variable named was that references the Azure Machine Learning workspace. You run the following Python 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/python/api/azureml-core/azureml.core.compute.computetarget
NEW QUESTION # 80
You are developing a machine learning, experiment by using Azure. The following images show the input and output of a machine learning experiment:
Use the drop-down menus to select the answer choice that answers each question based on the information presented in the graphic.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
NEW QUESTION # 81
The finance team asks you to train a model using data in an Azure Storage blob container named finance-data.
You need to register the container as a datastore in an Azure Machine Learning workspace and ensure that an error will be raised if the container does not exist.
How should you complete the code? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.
Answer:
Explanation:
Reference:
https://docs.microsoft.com/en-us/python/api/azureml-core/azureml.core.datastore.datastore
NEW QUESTION # 82
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