Explanation
Box 1: PREDICT
Provide internal users with the ability to incorporate machine learning models loaded to the dedicated SQL pool.
The example below shows a sample query using prediction function. An additional column with name Score and data type float is created containing the prediction results. All the input data columns as well as output prediction columns are available to display with the select statement.
-- Query for ML predictions
SELECT d.*, p.Score
FROM PREDICT(MODEL = (SELECT Model FROM Models WHERE Id = 1),
DATA = dbo.mytable AS d, RUNTIME = ONNX) WITH (Score float) AS p;
Box 2: WITH
Reference:
https://docs.microsoft.com/en-us/azure/synapse-analytics/sql-data-warehouse/sql-data-warehouse-predict

Explanation
Users indicate that queries against the serverless SQL pool fail occasionally because the size of tempdb has been exceeded.
In the dedicated SQL pool resource, temporary tables offer a performance benefit because their results are written to local rather than remote storage.
Temporary tables in serverless SQL pool.
Temporary tables in serverless SQL pool are supported but their usage is limited. They can't be used in queries which target files.
For example, you can't join a temporary table with data from files in storage. The number of temporary tables is limited to 100, and their total size is limited to 100 MB.
Reference: https://docs.microsoft.com/en-us/azure/synapse-analytics/sql/develop-tables-temporary

Explanation
Box 1: matplotlib.pyplot
Create a scatter plot
Let's create a scatter plot to see if there's a correlation between age and weight.
Under Paste or type your script code here, enter this code:
import matplotlib.pyplot as plt
dataset.plot(kind='scatter', x='Age', y='Weight', color='red')
plt.show()
Box 2: chart.show()
Reference:
https://docs.microsoft.com/en-us/power-bi/connect-data/desktop-python-visuals#create-a-scatter-plot

Explanation
A Synapse Analytics dedicated SQL pool is named SQLDW.
Customer contact data in SQLDW and the Power BI dataset must be labeled as Sensitive. Records must be kept of any users that use the sensitive data.
A (not B): Enable sensitivity labels
Sensitivity labels must be enabled on the tenant before they can be used in both the service and in Desktop.
To enable sensitivity labels on the tenant, go to the Power BI Admin portal, open the Tenant settings pane, and find the Information protection section.
In the Information Protection section, perform the following steps:
* Open Allow users to apply sensitivity labels for Power BI content.
* Enable the toggle.
D (not C): When data protection is enabled on your tenant, sensitivity labels appear in the sensitivity column in the list view of dashboards, reports, datasets, and dataflows.
E: Power BI Tenant Discovery Setting include Make certified content discoverable.
Reference: https://docs.microsoft.com/en-us/power-bi/enterprise/service-security-enable-data-sensitivity-labels
https://docs.microsoft.com/en-us/power-bi/enterprise/service-security-apply-data-sensitivity-labels
https://support.nhs.net/knowledge-base/power-bi-guidance/

Explanation
Some users indicate that the visuals in Power BI reports are slow to render when making filter selections.
B: You can investigate a slow query in a Power BI report using DAX Studio, looking at the query plan and the server timings.
D: Use Power BI Desktop Performance Analyzer to optimize the report or model.
Reference: https://www.sqlbi.com/tv/analyzing-a-slow-report-query-in-dax-studio/
https://docs.microsoft.com/en-us/power-bi/guidance/report-performance-troubleshoot