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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineer, while designing a Pandas UDF to process financial time-series data with complex calculations that require maintaining state across rows within each stock symbol group, must ensure the function is efficient and scalable. Which approach will solve the problem with minimum overhead while preserving data integrity?

A) Use a grouped_agg Pandas UDF that processes each stock symbol group independently, maintaining state through intermediate aggregation results that get passed between successive UDF calls via broadcast variables.
B) Use a SCALAR Pandas UDF that processes the entire dataset at once, implementing custom partitioning logic within the UDF to group by stock symbol and maintain state using global variables shared across all executor processes.
C) Use applyInPandas() on a Spark DataFrame that receives all rows for each stock symbol as a Pandas DataFrame, allowing processing within each group while maintaining state variables local to each group's processing function.
D) Use a SCALAR_ITER Pandas UDF with iterator-based processing, implementing state management through persistent storage (Delta tables) that gets updated after each batch to maintain continuity across iterator chunks.

2. A Delta Lake table in the Lakehouse named customer_parsams is used in churn prediction by the machine learning team. The table contains information about customers derived from a number of upstream sources. Currently, the data engineering team populates this table nightly by overwriting the table with the current valid values derived from upstream data sources.
Immediately after each update succeeds, the data engineer team would like to determine the difference between the new version and the previous of the table. Given the current implementation, which method can be used?

A) Parse the Delta Lake transaction log to identify all newly written data files.
B) Execute a query to calculate the difference between the new version and the previous version using Delta Lake's built-in versioning and time travel functionality.
C) Parse the Spark event logs to identify those rows that were updated, inserted, or deleted.
D) Execute DESCRIBE HISTORY customer_churn_params to obtain the full operation metrics for the update, including a log of all records that have been added or modified.

3. A junior member of the data engineering team is exploring the language interoperability of Databricks notebooks. The intended outcome of the below code is to register a view of all sales that occurred in countries on the continent of Africa that appear in the geo_lookup table.
Before executing the code, running SHOW TABLES on the current database indicates the database contains only two tables: geo_lookup and sales.

Which statement correctly describes the outcome of executing these command cells in order in an interactive notebook?

A) Both commands will fail. No new variables, tables, or views will be created.
B) Both commands will succeed. Executing show tables will show that countries at and sales at have been registered as views.
C) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable containing a list of strings.
D) Cmd 1 will succeed. Cmd 2 will search all accessible databases for a table or view named countries af: if this entity exists, Cmd 2 will succeed.
E) Cmd 1 will succeed and Cmd 2 will fail, countries at will be a Python variable representing a PySpark DataFrame.

4. 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 read() function to read tasks_status table; enrich with employee table; store the result in a materialized view.
B) 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.
C) 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.
D) 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.

5. A security analytics pipeline must enrich billions of raw connection logs with geolocation data.
The join hinges on finding which IPv4 range each event's address falls into.
Table 1: network_events ( 5 billion rows)
event_id ip_int
42 3232235777
Table 2: ip_ranges ( 2 million rows)
start_ip_int end_ip_int country
3232235520 3232236031 US
The query is currently very slow:
SELECT n.event_id, n.ip_int, r.country
FROM network_events n
JOIN ip_ranges r
ON n.ip_int BETWEEN r.start_ip_int AND r.end_ip_int;
Which change will most dramatically accelerate the query while preserving its logic?

A) Add a broadcast hint: /*+ BROADCAST(r) */ for ip_ranges.
B) Add a range-join hint /*+ RANGE_JOIN(r, 65536) */.
C) Increase spark.sql.shuffle.partitions from 200 to 10000.
D) Force a sort-merge join with /*+ MERGE(r) */.

Solutions: