You are assigned to explore data by using Azure Synapse Analytics. You have decided to use Synapse Spark notebook to explore the data. You can read the data using Spark's Dataframe API or SQL API into Spark Cluster's memory. Data is displayed in a tabular format as an output. As the number of rows returned is very high, you want to visualize the data within Spark Notebook itself quickly without any additional line of code. How do you achieve this?

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C. By switching from Table view to Chart view in the cells output pane Within the output area of each Synapse Spark notebook cell, a toolbar allows users to switch from the default Table view to Chart view. This built-in visualization renders the DataFrame or SQL result as a configurable chart - bar, line, scatter, etc. - without requiring any additional code, library imports, or configuration, directly satisfying the stated requirement.

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A. By using a python library called Matplotlib meant for data visualization Matplotlib requires writing additional Python code to create plot objects and render them, which contradicts the requirement for no additional lines of code.

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B. By using a python library called Seaborn meant for data visualization Seaborn also requires writing additional Python code and depends on Matplotlib as a backend, violating the no-additional-code requirement.

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D. By clicking on Open Spark UI link available below the right side of the cell The Open Spark UI link launches the Apache Spark cluster monitoring and job execution interface, not a data visualization view of cell output.

You are assigned to explore data by using Azure Synapse Analytics. You have decided to use Synapse Spark notebook to explore the data. You can read the data using Spark's Dataframe API or SQL API…

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