A data engineer is managing a data pipeline in Databricks, where multiple Delta tables are used for various transformations. The team wants to track how data flows through the pipeline, including identifying dependencies between Delta tables, notebooks, jobs, and dashboards. The data engineer is utilizing the Unity Catalog lineage feature to monitor this process.
How does Unity Catalog's data lineage feature support the visualization of relationships between Delta tables, notebooks, jobs, and dashboards?

• A.Unity Catalog lineage visualizes dependencies between Delta tables, notebooks, and jobs, but does not provide column-level tracing or relationships with dashboards.
• B.Unity Catalog lineage only supports visualizing relationships at the table level and does not extend to notebooks, jobs, or dashboards.
• C.Unity Catalog lineage provides an interactive graph that tracks dependencies between tables and notebooks but excludes any job-related dependencies or dashboard visualizations.
• D.Unity Catalog provides an interactive graph that visualizes the dependencies between Delta tables, notebooks, jobs, and dashboards, while also supporting column-level tracking of data transformations.

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A data engineer wants to create a new table containing the names of customers that live in France.
They have written the following command:

A senior data engineer mentions that it is organization policy to include a table property indicating that the new table includes personally identifiable information (PII).
Which of the following lines of code fills in the above blank to successfully complete the task?

• A.There is no way to indicate whether a table contains PII.
• B."COMMENT PII"
• C.TBLPROPERTIES PII
• D.COMMENT "Contains PII"
• E.PII

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A data engineer has joined an existing project and they see the following query in the project repository:
CREATE STREAMING LIVE TABLE loyal_customers AS
SELECT customer_id -
FROM STREAM(LIVE.customers)
WHERE loyalty_level = 'high';
Which of the following describes why the STREAM function is included in the query?

• A.The STREAM function is not needed and will cause an error.
• B.The table being created is a live table.
• C.The customers table is a streaming live table.
• D.The customers table is a reference to a Structured Streaming query on a PySpark DataFrame.
• E.The data in the customers table has been updated since its last run.

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A data engineer wants to create a data entity from a couple of tables. The data entity must be used by other data engineers in other sessions. It also must be saved to a physical location.
Which of the following data entities should the data engineer create?

• A.Database
• B.Function
• C.View
• D.Temporary view
• E.Table

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A data engineer is cleaning a Bronze table. The requirement is to eliminate rows where either the customer_email field or the customer_phone field is NULL. The cleaning must be performed in one operation using a single method call.
Which PySpark approach supports filtering multiple columns for NULL values in one call?

• A.df.filter(col("customer_email").isNotNull() & col("customer_phone").isNotNull())
• B.df.dropna(subset=["customer_email", "customer_phone"])
• C.df.na.drop(how="all")
• D.df.where("customer_email IS NOT NULL").where("customer_phone IS NOT NULL")

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