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Databricks Certified Data Engineer Professional Sample Questions:

1. Review the following error traceback:

Which statement describes the error being raised?

A) The code executed was PvSoark but was executed in a Scala notebook.
B) There is a syntax error because the heartrate column is not correctly identified as a column.
C) There is no column in the table named heartrateheartrateheartrate
D) There is a type error because a column object cannot be multiplied.
E) There is a type error because a DataFrame object cannot be multiplied.

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

3. A company has a task management system that tracks the most recent status of tasks. The system takes task events as input and processes events in near real-time using Lakeflow Declarative Pipelines. A new task event is ingested into the system when a task is created or the task status is changed. Lakeflow Declarative Pipelines provides a streaming table (tasks_status) for BI users to query.
The table represents the latest status of all tasks and includes 5 columns:
task_id (unique for each task)
task_name
task_owner
task_status
task_event_time
The table enables three properties: deletion vectors, row tracking, and change data feed (CDF).
A data engineer is asked to create a new Lakeflow Declarative Pipeline to enrich the tasks_status table in near real-time by adding one additional column representing task_owner's department, which can be looked up from a static dimension table (employee).
How should this enrichment be implemented?

A) Create a new Lakeflow Declarative Pipeline: use the readStream() function with the option skipChangeCommits to read the tasks_status table; enrich with the employee table; store the result in a new streaming table.
B) Create a new Lakeflow Declarative Pipeline: use the read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
C) Create a new Lakeflow Declarative Pipeline: use the readStream() function to read tasks_status table; enrich with the employee table; store the result in a new streaming table.
D) Create a new Lakeflow Declarative Pipeline: use readStream() function with option readChangeFeed to read tasks_status table CDF; enrich with the employee table; create a new streaming table as the result table and use apply_changes() function to process the changes from the enriched CDF.

4. The view updates represents an incremental batch of all newly ingested data to be inserted or updated in the customers table.
The following logic is used to process these records.

Which statement describes this implementation?

A) The customers table is implemented as a Type 1 table; old values are overwritten by new values and no history is maintained.
B) The customers table is implemented as a Type 2 table; old values are maintained but marked as no longer current and new values are inserted.
C) The customers table is implemented as a Type 2 table; old values are overwritten and new customers are appended.
D) The customers table is implemented as a Type 0 table; all writes are append only with no changes to existing values.
E) The customers table is implemented as a Type 3 table; old values are maintained as a new column alongside the current value.

5. A data engineer needs to productionize a new Spark application written by teammate. This application has numerous external dependencies, including libraries, and requires custom environment variables and Spark configuration parameters to be set. Which two methods will help the data engineer accomplish the task? (Choose two.)

A) Install libraries on DBFS
B) Use compute policies to set system properties, environment variables, and Spark configuration parameters.
C) Add libraries to compute policies
D) Create init scripts on DBFS.
E) Use secrets in init scripts to store configuration data

Solutions: