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

Section	Weight	Objectives
Debugging and Deploying	10%	- Deployment using bundles, CLI, and APIs - CI/CD and DevOps practices - Troubleshooting pipelines and errors
Data Governance	7%	- Data lineage and metadata tracking - Policy enforcement - Unity Catalog management
Data Sharing and Federation	5%	- Cross-workspace and cross-cloud access - Unity Catalog data sharing
Data Modelling	6%	- Schema design and management - Delta Lake table design - Medallion Architecture implementation
Data Ingestion & Acquisition	7%	- Connecting to diverse data sources - Schema inference and evolution - Auto Loader and streaming ingestion
Cost & Performance Optimisation	13%	- Storage optimization (partitioning, Z-order, indexing) - Query optimization and caching - Cluster configuration and scaling
Data Transformation, Cleansing, and Quality	10%	- Handling missing or inconsistent data - Standardization and normalization - Data validation and quality checks
Ensuring Data Security and Compliance	10%	- Data encryption and masking - Compliance standards implementation - Access control and permissions
Developing Code for Data Processing using Python and SQL	22%	- Data transformation and aggregation - Batch and incremental processing logic - Integration with Databricks APIs and tools
Monitoring and Alerting	10%	- Performance and health monitoring - Pipeline observability and logging - Setting up alerts and notifications

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q20-Q25):

NEW QUESTION # 20
A data engineer has ingested data from an external source into a PySpark DataFrame raw_df. They need to
briefly make this data available in SQL for a data analyst to perform a quality assurance check on the data.
Which of the following commands should the data engineer run to make this data available in SQL for only
the remainder of the Spark session?

A. raw_df.saveAsTable("raw_df")
B. raw_df.write.save("raw_df")
C. There is no way to share data between PySpark and SQL
D. raw_df.createOrReplaceTempView("raw_df")
E. raw_df.createTable("raw_df")

Answer: D

NEW QUESTION # 21
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. A separate data synchronization service must be deployed.
B. Federation implements change data capture (CDC) from all sources.
C. Federation creates local copies that must be manually refreshed.
D. Federation provides read-only access that reflects the current state of source systems.

Answer: D

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
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 # 22
A data engineer is using Lakeflow Spark Declarative Pipelines Expectations to track the data quality of incoming sensor data. Periodically, sensors send bad readings that are out of range, and the team is currently flagging those rows with a warning and writing them to the silver table along with the good data. They have been given a new requirement: the bad rows need to be quarantined in a separate quarantine table and no longer included in the silver table.
This is the existing code for the silver table:
@dlt.table
@dlt.expect( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
Which code will satisfy the requirements?

A. @dlt.table
@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
@dlt.table
@dlt.expect( " invalid_sensor_reading " , " reading < 120 " )
def quarantine_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
B. @dlt.table
@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
C. @dlt.table
@dlt.expect( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
@dlt.table
@dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )
def quarantine_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
D. @dlt.table
@dlt.expect_or_drop( " valid_sensor_reading " , " reading < 120 " )
def silver_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )
@dlt.table
@dlt.expect( " invalid_sensor_reading " , " reading > = 120 " )
def quarantine_sensor_readings():
return spark.readStream.table( " bronze_sensor_readings " )

Answer: D

Explanation:
Databricks documents that expect retains invalid records in the target dataset, while expect_or_drop drops invalid records before writing to the target. Therefore, the silver table must use expect_or_drop so bad records are excluded from silver. ( Databricks Documentation ) Databricks also documents a quarantine pattern in which invalid records are separated for downstream processing, but the fully documented pattern uses an intermediate quarantine dataset with an is_quarantined flag and then derives valid and invalid paths from it. None of the listed options exactly matches the official quarantine pattern. As written, option B is the closest intended answer because it at least creates a separate quarantine table and removes invalid rows from silver, but strictly speaking, the documented quarantine implementation is more explicit than any option shown here. ( Databricks Documentation )
======

NEW QUESTION # 23
A small company based in the United States has recently contracted a consulting firm in India to implement several new data engineering pipelines to power artificial intelligence applications. All the company's data is stored in regional cloud storage in the United States.
The workspace administrator at the company is uncertain about where the Databricks workspace used by the contractors should be deployed.
Assuming that all data governance considerations are accounted for, which statement accurately informs this decision?

A. Databricks workspaces do not rely on any regional infrastructure; as such, the decision should be made based upon what is most convenient for the workspace administrator.
B. Databricks runs HDFS on cloud volume storage; as such, cloud virtual machines must be deployed in the region where the data is stored.
C. Cross-region reads and writes can incur significant costs and latency; whenever possible, compute should be deployed in the same region the data is stored.
D. Databricks notebooks send all executable code from the user's browser to virtual machines over the open internet; whenever possible, choosing a workspace region near the end users is the most secure.
E. Databricks leverages user workstations as the driver during interactive development; as such, users should always use a workspace deployed in a region they are physically near.

Answer: C

Explanation:
This is the correct answer because it accurately informs this decision. The decision is about where the Databricks workspace used by the contractors should be deployed. The contractors are based in India, while all the company's data is stored in regional cloud storage in the United States. When choosing a region for deploying a Databricks workspace, one of the important factors to consider is the proximity to the data sources and sinks. Cross-region reads and writes can incur significant costs and latency due to network bandwidth and data transfer fees. Therefore, whenever possible, compute should be deployed in the same region the data is stored to optimize performance and reduce costs. Verified Reference: [Databricks Certified Data Engineer Professional], under "Databricks Workspace" section; Databricks Documentation, under "Choose a region" section.

NEW QUESTION # 24
The Databricks CLI is use to trigger a run of an existing job by passing the job_id parameter. The response that the job run request has been submitted successfully includes a filed run_id.
Which statement describes what the number alongside this field represents?

A. The number of times the job definition has been run in the workspace.
B. The job_id and number of times the job has been are concatenated and returned.
C. The globally unique ID of the newly triggered run.
D. The job_id is returned in this field.

Answer: C

Explanation:
When triggering a job run using the Databricks CLI, the run_id field in the response represents a globally unique identifier for that particular run of the job. This run_id is distinct from the job_id. While the job_id identifies the job definition and is constant across all runs of that job, the run_id is unique to each execution and is used to track and query the status of that specific job run within the Databricks environment. This distinction allows users to manage and reference individual executions of a job directly.

NEW QUESTION # 25
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