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

Section	Weight	Objectives
Data Processing	28%	- Spark SQL - Structured Streaming - ETL Pipelines - Data Transformation
Monitoring and Troubleshooting	16%	- Performance Optimization - Troubleshooting - Monitoring
Data Quality and Governance	12%	- Data Lineage - Data Quality - Governance
Databricks Lakehouse Platform	24%	- Data Management - Delta Lake - Lakehouse Architecture - Unity Catalog
Data Modeling and Storage	20%	- Storage Optimization - File Formats - Data Modeling

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Databricks Certified Data Engineer Professional Exam Sample Questions (Q81-Q86):

NEW QUESTION # 81
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM 160 total cores and only one Executor per VM.
Given an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
B. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
C. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
D. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
E. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor

Answer: E

NEW QUESTION # 82
Spill occurs as a result of executing various wide transformations. However, diagnosing spill requires one to proactively look for key indicators.
Where in the Spark UI are two of the primary indicators that a partition is spilling to disk?

A. Stage's detail screen and Query's detail screen
B. Query's detail screen and Job's detail screen
C. Stage's detail screen and Executor's log files
D. Driver's and Executor's log files
E. Executor's detail screen and Executor's log files

Answer: C

Explanation:
In the Spark UI, the Stage's detail screen provides key metrics about each stage of a job, including the amount of data that has been spilled to disk. If you see a high number in the "Spill (Memory)" or "Spill (Disk)" columns, it's an indication that a partition is spilling to disk.
The Executor's log files can also provide valuable information about spill. If a task is spilling a lot of data, you'll see messages in the logs like "Spilling UnsafeExternalSorter to disk" or "Task memory spill". These messages indicate that the task ran out of memory and had to spill data to disk.

NEW QUESTION # 83
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. 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;
D. 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;

Answer: C

Explanation:
The correct way to satisfy non-overlapping windows with an offset in Databricks SQL is to use the window function with three parameters: window duration, slide duration, and start offset.
In option A, the function call:
window(event_ts, '5 minutes', '2 minutes', '5 minutes')
creates 5-minute windows that slide every 5 minutes, with a 2-minute offset, which exactly matches the requirement (intervals like 00:02?0:07, 00:07?0:12, ...).

NEW QUESTION # 84
The business reporting tem requires that data for their dashboards be updated every hour. The total processing time for the pipeline that extracts transforms and load the data for their pipeline runs in 10 minutes.
Assuming normal operating conditions, which configuration will meet their service-level agreement requirements with the lowest cost?

A. Schedule a Structured Streaming job with a trigger interval of 60 minutes.
B. Schedule a jo to execute the pipeline once and hour on a dedicated interactive cluster.
C. Schedule a job to execute the pipeline once hour on a new job cluster.
D. Configure a job that executes every time new data lands in a given directory.

Answer: C

Explanation:
Scheduling a job to execute the data processing pipeline once an hour on a new job cluster is the most cost-effective solution given the scenario. Job clusters are ephemeral in nature; they are spun up just before the job execution and terminated upon completion, which means you only incur costs for the time the cluster is active. Since the total processing time is only 10 minutes, a new job cluster created for each hourly execution minimizes the running time and thus the cost, while also fulfilling the requirement for hourly data updates for the business reporting team's dashboards.

NEW QUESTION # 85
A company wants to implement Lakehouse Federation across multiple data sources but is concerned about data consistency and ensuring that all teams access the same authoritative version of their data. Which statement is applicable for Lakehouse Federations to maintain data consistency?

A. Federation provides read-only access that reflects the current state of source systems.
B. A separate data synchronization service must be deployed.
C. Federation implements change data capture (CDC) from all sources.
D. Federation creates local copies that must be manually refreshed.

Answer: A

Explanation:
Lakehouse Federation allows Databricks to query and manage external data sources through a single governance layer, without moving or copying data. The documentation specifies that
"Federated queries provide read-only access to data, reflecting the current state of the underlying source system." This ensures consistency across teams since all users access the same source of truth directly from the external system through Unity Catalog. Federation does not perform CDC replication or local caching; it queries live data on demand. Hence, option A accurately represents how Lakehouse Federation maintains consistency across federated sources.

NEW QUESTION # 86
......

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