Free Microsoft DP-100 Exam Questions

You use the Azure Machine Learning SDK in a notebook to run an experiment using a script file in an experiment folder. The experiment fails. You need to troubleshoot the failed experiment. What are two possible ways to achieve this goal? Each correct answer presents a complete solution.

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.

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?

An organization creates and deploys a multi-class image classification deep learning model that uses a set of labeled photographs. The software engineering team reports there is a heavy inferencing load for the prediction web services during the summer. The production web service for the model fails to meet demand despite having a fully-utilized compute cluster where the web service is deployed. You need to improve performance of the image classification web service with minimal downtime and minimal administrative effort. What should you advise the IT Operations team to do?

You are using Azure Machine Learning to monitor a trained and deployed model. You implement Event Grid to respond to Azure Machine Learning events. Model performance has degraded due to model input data changes. You need to trigger a remediation ML pipeline based on an Azure Machine Learning event. Which event should you use?