Free DP-100 Mock Exam – Practice Online Confidently

You are developing deep learning models to analyze semi-structured, unstructured, and structured data types. You have the following data available for model building: Video recordings of sporting events Transcripts of radio commentary about events Logs from related social media feeds captured during sporting events You need to select an environment for creating the model. Which environment should you use?

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 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 manage an Azure Machine learning workspace named workspace1. You must develop Python SDK v2 code to add a compute instance to workspace1. The code must import all required modules and call the constructor of the Compute instance class. You need to add the instantiated compute instance to workspace 1. What should you use?

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?