Free Microsoft DP-100 Exam Questions

You must use the Azure Machine Learning SDK to interact with data and experiments in the workspace. You need to configure the config.json file to connect to the workspace from the Python environment. Which two additional parameters must you add to the config.json file in order to connect to the workspace? Each correct answer presents part of the solution. NOTE: Each correct selection is worth one point.

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 create an Azure Machine Learning workspace. The workspace contains a dataset named sample.dataset, a compute instance, and a compute cluster. You must create a two-stage pipeline that will prepare data in the dataset and then train and register a model based on the prepared data. The first stage of the pipeline contains the following code:You need to identify the location containing the output of the first stage of the script that you can use as input for the second stage. Which storage location 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 create a model to forecast weather conditions based on historical data. You need to create a pipeline that runs a processing script to load data from a datastore and pass the processed data to a machine learning model training script. Solution: Run the following code:

You develop and train a machine learning model to predict fraudulent transactions for a hotel booking website. Traffic to the site varies considerably. The site experiences heavy traffic on Monday and Friday and much lower traffic on other days. Holidays are also high web traffic days. You need to deploy the model as an Azure Machine Learning real-time web service endpoint on compute that can dynamically scale up and down to support demand. Which deployment compute option should you use?