Free Microsoft DP-100 Exam Questions

You create an Azure Machine Learning pipeline named pipeline 1 with two steps that contain Python scnpts. Data processed by the first step is passed to the second step. You must update the content of the downstream data source of pipeline 1 and run the pipeline again. You need to ensure the new run of pipeline 1 fully processes the updated content. Solution: Change the value of the compute.target parameter of the PythonScriptStep object in the two steps. Does the solution meet the goal'

You manage an Azure Machine Learning workspace. You plan to irain a natural language processing (NLP) tew classification model in multiple languages by using Azure Machine learning Python SDK v2. You need to configure the language of the text classification job by using automated machine learning. Which method of the TextClassifkationlob class should you use?

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 are creating a new experiment in Azure Machine Learning Studio. One class has a much smaller number of observations than the other classes in the training set. You need to select an appropriate data sampling strategy to compensate for the class imbalance. Solution: You use the Stratified split for the sampling mode. Does the solution meet the goal?