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Databricks Certified Data Engineer Professional Sample Questions:

1. An upstream system has been configured to pass the date for a given batch of data to the Databricks Jobs API as a parameter. The notebook to be scheduled will use this parameter to load data with the following code:
df = spark.read.format("parquet").load(f"/mnt/source/(date)")
Which code block should be used to create the date Python variable used in the above code block?

A) import sys
date = sys.argv[1]
B) date = spark.conf.get("date")
C) dbutils.widgets.text("date", "null")
date = dbutils.widgets.get("date")
D) date = dbutils.notebooks.getParam("date")
E) input_dict = input()
date= input_dict["date"]

2. A data engineer is optimizing a MERGE operation on an 800GB UC-managed table that experiences frequent updates and deletions. Which two actions should the engineer prioritize to improve MERGE performance? (Choose two.)

A) Partition the table by date.
B) Use ZORDER on high-cardinality columns.
C) Enable deletion vectors on the table if not already enabled.
D) Overwrite the table instead of Merge.
E) Apply liquid clustering using the merge join keys.

3. A table named user_ltv is being used to create a view that will be used by data analysts on various teams. Users in the workspace are configured into groups, which are used for setting up data access using ACLs.
The user_ltv table has the following schema:
email STRING, age INT, ltv INT
The following view definition is executed:

An analyst who is not a member of the marketing group executes the following query:
SELECT * FROM email_ltv
Which statement describes the results returned by this query?

A) The email and ltv columns will be returned with the values in user itv.
B) Only the email and itv columns will be returned; the email column will contain all null values.
C) The email, age. and ltv columns will be returned with the values in user ltv.
D) Three columns will be returned, but one column will be named "redacted" and contain only null values.
E) Only the email and ltv columns will be returned; the email column will contain the string
"REDACTED" in each row.

4. To reduce storage and compute costs, the data engineering team has been tasked with curating a series of aggregate tables leveraged by business intelligence dashboards, customer-facing applications, production machine learning models, and ad hoc analytical queries.
The data engineering team has been made aware of new requirements from a customer-facing application, which is the only downstream workload they manage entirely. As a result, an aggregate table used by numerous teams across the organization will need to have a number of fields renamed, and additional fields will also be added.
Which of the solutions addresses the situation while minimally interrupting other teams in the organization without increasing the number of tables that need to be managed?

A) Replace the current table definition with a logical view defined with the query logic currently writing the aggregate table; create a new table to power the customer-facing application.
B) Configure a new table with all the requisite fields and new names and use this as the source for the customer-facing application; create a view that maintains the original data schema and table name by aliasing select fields from the new table.
C) Send all users notice that the schema for the table will be changing; include in the communication the logic necessary to revert the new table schema to match historic queries.
D) Add a table comment warning all users that the table schema and field names will be changing on a given date; overwrite the table in place to the specifications of the customer-facing application.
E) Create a new table with the required schema and new fields and use Delta Lake's deep clone functionality to sync up changes committed to one table to the corresponding table.

5. A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A) Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.
B) The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
C) The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
D) Users granted with USE CATALOG can modify the owner's permissions to downstream tables.

Solutions: