Microsoft Certified: Azure Data Engineer Associate DP-203 Full Course Free

You have an Azure subscription that contains an Azure Synapse Analytics workspace named workspace1. Workspace1 contains a dedicated SQL pool named SQL Pool and an Apache Spark pool named sparkpool. Sparkpool1 contains a DataFrame named pyspark.df.

You need to write the contents of pyspark_df to a tabte in SQLPooM by using a PySpark notebook.

How should you complete the code? To answer, select the appropriate options in the answer area.

NOTE: Each correct selection is worth one point.

You are designing an Azure Synapse solution that will provide a query interface for the data stored in an Azure Storage account. The storage account is only accessible from a virtual network.

You need to recommend an authentication mechanism to ensure that the solution can access the source data.

What should you recommend?

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 scenario, you will NOT be able to return to it. As a result, these questions will not appear in the review screen.

You have an Azure Storage account that contains 100 GB of files. The files contain text and numerical values. 75% of the rows contain description data that has an average length of 1.1 MB.

You plan to copy the data from the storage account to an enterprise data warehouse in Azure Synapse Analytics.

You need to prepare the files to ensure that the data copies quickly.

Solution: You convert the files to compressed delimited text files.

Does this meet the goal?

You are designing an Azure Data Lake Storage solution that will transform raw JSON files for use in an analytical workload.

You need to recommend a format for the transformed files. The solution must meet the following requirements:

Contain information about the data types of each column in the files.

Support querying a subset of columns in the files.

Support read-heavy analytical workloads.

Minimize the file size.

What should you recommend?