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

Section	Objectives
Data Modeling and Storage	- Delta Lake table design and optimization - Schema evolution and data partitioning strategies - Design scalable data lakehouse architectures
Production Pipelines and Orchestration	- Build and manage workflows using Databricks Jobs - Automate ETL pipelines and scheduling - Pipeline reliability and fault tolerance
Security, Governance, Monitoring, and Optimization	- Monitor and optimize Spark workloads - Implement Unity Catalog governance and access control - Cost optimization and performance tuning
Data Ingestion and Transformation	- Handle batch and streaming data pipelines - Transform and clean datasets using Spark SQL and DataFrame APIs - Ingest data using Apache Spark and Databricks

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q136-Q141):

NEW QUESTION # 136
Which of the following data workloads will utilize a Bronze table as its destination?

A. A job that aggregates cleaned data to create standard summary statistics
B. A job that queries aggregated data to publish key insights into a dashboard
C. A job that ingests raw data from a streaming source into the Lakehouse
D. A job that develops a feature set for a machine learning application
E. A job that enriches data by parsing its timestamps into a human-readable format

Answer: C

Explanation:
Explanation
The answer is A job that ingests raw data from a streaming source into the Lakehouse.
The ingested data from the raw streaming data source like Kafka is first stored in the Bronze layer as first destination before it is further optimized and stored in Silver.
Medallion Architecture - Databricks
Bronze Layer:
1. Raw copy of ingested data
2. Replaces traditional data lake
3. Provides efficient storage and querying of full, unprocessed history of data
4. No schema is applied at this layer
Exam focus: Please review the below image and understand the role of each layer(bronze, silver, gold) in medallion architecture, you will see varying questions targeting each layer and its purpose.
Sorry I had to add the watermark some people in Udemy are copying my content.
Purpose of each layer in medallion architecture

NEW QUESTION # 137
A Spark job is taking longer than expected. Using the Spark UI, a data engineer notes that the Min, Median, and Max Durations for tasks in a particular stage show the minimum and median time to complete a task as roughly the same, but the max duration for a task to be roughly 100 times as long as the minimum.
Which situation is causing increased duration of the overall job?

A. Task queueing resulting from improper thread pool assignment.
B. Spill resulting from attached volume storage being too small.
C. Network latency due to some cluster nodes being in different regions from the source data
D. Credential validation errors while pulling data from an external system.
E. Skew caused by more data being assigned to a subset of spark-partitions.

Answer: E

Explanation:
Explanation
This is the correct answer because skew is a common situation that causes increased duration of the overall job. Skew occurs when some partitions have more data than others, resulting in uneven distribution of work among tasks and executors. Skew can be caused by various factors, such as skewed data distribution, improper partitioning strategy, or join operations with skewed keys. Skew can lead to performance issues such as long-running tasks, wasted resources, or even task failures due to memory or disk spills. Verified References:
[Databricks Certified Data Engineer Professional], under "Performance Tuning" section; Databricks Documentation, under "Skew" section.

NEW QUESTION # 138
To identify the top users consuming compute resources, a data engineering team needs to monitor usage within their Databricks workspace for better resource utilization and cost control. The team decided to use Databricks system tables, available under the system catalog in Unity Catalog, to gain detailed visibility into workspace activity. Which SQL query should the team run from the system catalog to achieve this?

A. SELECT
sku_name,
usage_metadata.run_name AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
B. SELECT
sku_name,
identity_metadata.created_by AS user_email,
COUNT(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
C. SELECT
sku_name,
identity_metadata.created_by AS user_email,
SUM(usage_quantity * usage_unit) AS total_dbus
FROM system.billing.usage
GROUP BY user_email, sku_name
ORDER BY total_dbus DESC
LIMIT 10
D. SELECT
identity_metadata.run_as AS user_email,
SUM(usage_quantity) AS total_dbus
FROM system.billing.usage
GROUP BY user_email
ORDER BY total_dbus DESC
LIMIT 10

Answer: D

Explanation:
Databricks documents system.billing.usage as the correct system table for billable usage analysis, and it documents identity_metadata.run_as as the field that records who ran supported workloads such as jobs, notebooks, and Lakeflow Spark Declarative Pipelines. For "top users consuming compute resources," summing usage_quantity by identity_metadata.run_as is the correct conceptual approach. ( Databricks Documentation ) The other options are not aligned with the documented schema or metric usage. identity_metadata.created_by is not the general compute-consumer identity field for jobs and notebook workloads; it applies to specific products such as Databricks Apps and certain agent workloads. usage_quantity should be summed, not counted, and usage_unit is not something you multiply into DBUs in the way shown. usage_metadata.
run_name is not the documented user identity field for this purpose. As written, option C is the only option that matches the official identity model for user-attributed compute consumption. ( Databricks Documentation )
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NEW QUESTION # 139
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context. Assuming the job is already deployed, they need to trigger its execution while ensuring the target-specific configuration is respected.
Which command will trigger the job execution?

A. databricks run my_project_job -t prod
B. databricks bundle run my_project_job -t prod
C. databricks job run my_project_job --env prod
D. databricks execute my_project_job -e prod

Answer: B

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.
Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.
The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.
Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn't integrate with bundle targets. Therefore, D is the correct and verified answer.

NEW QUESTION # 140
The data engineering team noticed that one of the job normally finishes in 15 mins but gets stuck randomly when reading remote databases due to a network packet drop, which of the following steps can be used to improve the stability of the job?

A. Use Spark job time out setting in the Spark UI
B. Use Cluster timeout setting in the Job cluster UI
C. Use Jobs runs, active runs UI section to monitor and kill long running job
D. Modify the task, to include a timeout to kill the job if it runs more than 15 mins.
E. Use Databrick REST API to monitor long running jobs and issue a kill command

Answer: D

Explanation:
Explanation
The answer is, Modify the task, to include time out to kill the job if it runs more than 15 mins.
https://docs.microsoft.com/en-us/azure/databricks/data-engineering/jobs/jobs#timeout

NEW QUESTION # 141
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