Exam DP-600 topic 1 question 12 discussion

I think : No yes no To display summary its describe() or sumarry()

Answer is No, Yes, Yes The code is displaying a summary of the data frame using the visualizations. Point 3 is a technicality and I think to some extent depends on your interpretation of the question. I am going with Yes.

My guess: No, Yes, Yes. The third point depends on how you interpret summary in the statement, it’s not a textual or statistical summary like .describe(), so the term "summary" is only applicable if regarded as a visual summary of the dataframe contents(which is what Quickvisualize does)

No, Yes, Yes IMO the report itself is a summary of the dataframe. It is a bad question though, you could interpret it either way.

So, My final answer would be No, Yes, No from powerbiclient import QuickVisualize, get_dataset_config # Create a Power BI visualization from a Spark DataFrame PBI_visualize = QuickVisualize(get_dataset_config(df)) # Display the visualization in the notebook PBI_visualize To emebed a power bi report in notebook, you need to import these libraries and then authenticate from powerbiclient import Report, models from IPython.display import display # Provide your Power BI report ID and workspace ID workspace_id = "your_workspace_id" report_id = "your_report_id" # Create an embedded report object report = Report(group_id=workspace_id, report_id=report_id) # Display the report inside the notebook display(report) For summary, you can either use describe() or summary()

No, Yes, No if it is a typo dateframe or yes if not dataframe

I think it's no, yes, no

No-->yes-->yes

No, No, Yes 1) The code will display the contents of a dataframe, not an existing report. 2) The code does not create a new PBI report. Only after you click 'save' is a new report made. 3) The result of this code is a newly rendered block called 'Quick summary' with PBI visualisations. So if Microsoft calls it a summary, it is a summary. Reference: https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-visualization

The provided answer is correct (No Yes Yes). Because with this code, we do get a Quick summary in the visualization. Please check the documentation and visualization image on this link: https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-visualization

No, Yes, Yes

The answer is No Yes Yes. See the documentation link, contains the exact code that is in the question https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-visualization

Creates a PBI report? Not so sure, it shows a visualization like a PBI report, does not save it as such until you hit save.

The code embeds an existing Power BI report: No: Typically, embedding an existing Power BI report involves using Power BI REST API or embedding URLs, not Spark DataFrame operations within a notebook. The provided code snippet seems more focused on data manipulation and visual creation within the notebook itself, rather than embedding an external report. The code creates a Power BI report: Yes: When working with Spark DataFrames in a Fabric notebook, you can generate visuals that can be published to Power BI. This involves converting DataFrames to a format that Power BI can consume and using integration features provided by Fabric to create reports based on this data. The code displays a summary of a DataFrame: Yes: If the code snippet includes functions like display(df), df.show(), or similar methods, it is intended to display a summary or the contents of the DataFrame. These functions are commonly used to provide a quick overview of the data within the notebook environment.

No yes no The third one is a little bit confused though. Anyway, the QuickVisualize is using the DataFrame, but it doesn't directly display a summary of the DataFrame itself You'd need to use another method like describe() or summary()

IMHO, No -> Yes -> Yes. Link is here: https://learn.microsoft.com/en-us/power-bi/create-reports/jupyter-quick-report#create-and-render-a-quick-visualize-instance It is not embedded, it is an actual report. The 3rd one - is philosophical, but it summarizes the info from the data frame, so it makes sense to have "Yes" there

No, Yes and According to https://learn.microsoft.com/en-us/fabric/data-engineering/notebook-visualization#displaydf-summary-view the third should be No