Free Microsoft DP-100 Exam Questions

You use Azure Machine Learning Designer to load the following datasets into an experiment: Dataset1You 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 component. Does the solution meet the goal?

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.

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 plan to run a script as an experiment using a Script Run Configuration. The script uses modules from the scipy library as well as several Python packages that are not typically installed in a default conda environment. You plan to run the experiment on your local workstation for small datasets and scale out the experiment by running it on more powerful remote compute clusters for larger datasets. You need to ensure that the experiment runs successfully on local and remote compute with the least administrative effort. What should you do?