Databricks Databricks-Certified-Data-Engineer-Professional Exam Overview:

Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:

Databricks Certified Data Engineer Professional Sample Questions:

1. A Delta Lake table representing metadata about content from user has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE Based on the above schema, which column is a good candidate for partitioning the Delta Table?

A) Date
B) Post_id
C) User_id
D) Post_time
E) latitude

2. A production workload incrementally applies updates from an external Change Data Capture feed to a Delta Lake table as an always-on Structured Stream job. When data was initially migrated for this table, OPTIMIZE was executed and most data files were resized to 1 GB. Auto Optimize and Auto Compaction were both turned on for the streaming production job. Recent review of data files shows that most data files are under 64 MB, although each partition in the table contains at least 1 GB of data and the total table size is over 10 TB.
Which of the following likely explains these smaller file sizes?

A) Databricks has autotuned to a smaller target file size based on the overall size of data in the table
B) Databricks has autotuned to a smaller target file size based on the amount of data in each partition
C) Z-order indices calculated on the table are preventing file compaction C Bloom filler indices calculated on the table are preventing file compaction
D) Databricks has autotuned to a smaller target file size to reduce duration of MERGE operations

3. A data engineer deploys a multi-task Databricks job that orchestrates three notebooks. One task intermittently fails with Exit Code 1 but succeeds on retry. The engineer needs to collect detailed logs for the failing attempts, including stdout/stderr and cluster lifecycle context, and share them with the platform team. What steps the data engineer needs to follow using built-in tools?

A) From the job run details page, export the job's logs or configure log delivery; then retrieve the compute driver logs and event logs from the compute details page to correlate stdout/stderr with cluster events.
B) Download worker logs directly from the Spark UI and ignore driver logs, as worker logs contain stdout/stderr for all tasks and cluster events.
C) Export the notebook run results to HTML; this bundle includes complete stdout, stderr, and cluster event history across all tasks.
D) Use the notebook interactive debugger to re-run the entire multi-task job, and capture step- through traces for the failing task.

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,
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'));
B) 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;
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. Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A) Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
B) Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
C) Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.
D) Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.

Solutions: