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Databricks Certified Data Engineer Professional Sample Questions:

1. A data engineering workspace was automatically enabled for Unity Catalog, creating a workspace catalog. New team members report they can create tables in the default schema but cannot access table in other schemas within the same workspace catalog. Why are the new team members unable to access tables in other schemas?

A) Tables in other schemas require additional BROWSEprivileges that new users don't receive automatically
B) Workspace catalog permissions are not subject to inheritance rules.
C) Workspace users receive USE CATALOG and specific privileges on default schema only.
D) New users only receive CREATE TABLE privileges on the default schema.

2. What is the first line of a Databricks Python notebook when viewed in a text editor?

A) # Databricks notebook source
B) # MAGIC %python
C) %python
D) // Databricks notebook source
E) -- Databricks notebook source

3. A data engineer is analyzing a large, partitioned retail dataset in Databricks, where each row represents a sale made by a salesperson. The dataset contains millions of records with the following schema:
sales_df: [salesperson_id: string, region: string, sale_amount: double, sale_date: date] The data engineer needs to generate a DataFrame that ranks salespeople within each region based on their total cumulative sales, with the highest seller ranked as 1. If multiple salespeople have the same total sales, they should share the same rank.
The data engineer wants to implement this logic using a PySpark window function and the dense_rank () function.
Which code snippet will perform this ranking?

A)

B)

C)

D)

4. A facilities-monitoring team is building a near-real-time PowerBI dashboard off the Delta table device_readings:
Columns:
device_id (STRING, unique sensor ID)
event_ts (TIMESTAMP, ingestion timestamp UTC)
temperature_c (DOUBLE, temperature in °C)
Requirement:
For each sensor, generate one row per non-overlapping 5-minute
interval, offset by 2 minutes (e.g., 00:02-00:07, 00:07-00:12, ...).
Each row must include interval start, interval end, and average
temperature in that slice.
Downstream BI tools (e.g., Power BI) must use the interval timestamps
to plot time-series bars.

A) SELECT device_id,
date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
B) SELECT device_id,
event_ts,
AVG(temperature_c) OVER (
PARTITION BY device_id
ORDER BY event_ts
RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
) AS avg_temp_5m
FROM device_readings
WINDOW w AS (window(event_ts, '5 minutes', '2 minutes'));
C) SELECT device_id,
window.start AS bucket_start,
window.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM device_readings
GROUP BY device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes') ORDER BY device_id, bucket_start;
D) WITH buckets AS (
SELECT device_id,
window(event_ts, '5 minutes', '2 minutes', '5 minutes') AS win,
temperature_c
FROM device_readings
)
SELECT device_id,
win.start AS bucket_start,
win.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM buckets
GROUP BY device_id, win
ORDER BY device_id, bucket_start;

5. A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A) The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.
B) The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.
C) The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
D) The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.

Solutions: