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Microsoft DP-100 Exam
- Topic 1: Define And Prepare The Development Environment/ Select Development Environment
- Topic 2: Assess The Deployment Environment Constraints/ Select The Development Environment Analyze And Recommend Tools That Meet System Requirements/ Set Up Development Environment Create An Azure Data Science Environment/ Configure Data Science Work Environments
- Topic 3: Transform Data Into Usable Datasets/ Develop Data Structures/ Perform Exploratory Data Analysis (Eda)
- Topic 4: Review Visual Analytics Data To Discover Patterns And Determine Next Steps/ Design A Data Sampling Strategy
- Topic 5: Design The Data Preparation Flow/ Identify Anomalies, Outliers, And Other Data Inconsistencies
- Topic 6: Resolve Anomalies, Outliers, And Other Data Inconsistencies/ Standardize Data Formats/ Perform Feature Extraction Algorithms On Numerical Data/ Perform Feature Extraction Algorithms On Non-Numerical Data
- Topic 7: Select An Algorithmic Approach/ Consider Data Preparation Steps That Are Specific To The Selected Algorithms
- Topic 8: Determine Appropriate Performance Metrics/ Implement Appropriate Algorithms
- Topic 9: Determine Ideal Split Based On The Nature Of The Data/ Determine Number Of Splits/ Identify Data Imbalances
- Topic 10: Determine Relative Size Of Splits/ Resample A Dataset To Impose Balance/ Adjust Performance Metric To Resolve Imbalances
Free Microsoft DP-100 Exam Actual Questions
The questions for DP-100 were last updated On Apr. 08, 2024
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 use Azure Machine Learning designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Join Data module.
Does the solution meet the goal?
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 use Azure Machine Learning designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Apply Transformation module.
Does the solution meet the goal?
You create an Azure Machine Learning workspace. You use Azure Machine Learning designer to create a pipeline within the workspace. You need to submit a pipeline run from the designer.
What should you do first?
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 use Azure Machine Learning designer to load the following datasets into an experiment:
You need to create a dataset that has the same columns and header row as the input datasets and contains all rows from both input datasets.
Solution: Use the Apply Transformation module.
Does the solution meet the goal?
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?
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