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

Section	Weight	Objectives
Topic 1: Data Ingestion	15-20%	- Batch ingestion methods 1. DBR autoloader 2. Integration with external systems 3. Spark APIs for ingestion - Streaming ingestion 1. Kafka integration 2. Structured streaming fundamentals
Topic 2: Data Warehouse and Lakehouse Architecture	15-20%	- Lakehouse architecture principles 1. Data governance fundamentals 2. Bronze, silver, gold data layers 3. Differences between data lake, data warehouse, and lakehouse
Topic 3: Pipeline Development and Orchestration	10-15%	- Databricks workflows 1. Task dependencies and orchestration 2. Monitoring and alerting 3. Jobs and job scheduling
Topic 4: Data Processing with Spark	25-30%	- Python and SQL for data engineering 1. Spark APIs in Python 2. Built-in and user-defined functions 3. Performance optimization techniques - Spark DataFrames and Spark SQL 1. Window functions 2. DataFrame operations and transformations 3. Spark SQL queries and functions
Topic 5: Delta Lake	20-25%	- Delta Lake operations 1. Merge, update, delete operations 2. Schema evolution and enforcement 3. Delta Live Tables - Delta Lake fundamentals 1. ACID transactions 2. Optimize and Z-order 3. Time travel and data versioning

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q39-Q44):

NEW QUESTION # 39
Given the following error traceback (from display(df.select(3* " heartrate " ))) which shows AnalysisException: cannot resolve ' heartrateheartrateheartrate ' , which statement describes the error being raised?

A. There is a type error because a column object cannot be multiplied.
B. There is no column in the table named heartrateheartrateheartrate.
C. There is a type error because a DataFrame object cannot be multiplied.
D. There is a syntax error because the heartrate column is not correctly identified as a column.

Answer: B

Explanation:
* Exact extract: "select() expects column names or Column expressions." References: PySpark DataFrame select; Column expressions and col().

NEW QUESTION # 40
A data engineering team is setting up deployment automation. To deploy workspace assets remotely using the Databricks CLI command, they must configure it with proper authentication.
Which authentication approach will provide the highest level of security ?

A. Use a service principal and its Personal Access Token.
B. Use a shared user account and its OAuth client secret.
C. Use a service principal ID and its OAuth client secret.
D. Use a service principal with OAuth token federation.

Answer: D

Explanation:
The most secure and enterprise-recommended authentication method for Databricks automation is OAuth token federation with service principals .
This configuration allows service principals (non-human identities) to authenticate using temporary OAuth access tokens from a trusted identity provider (such as Azure AD or AWS IAM federation). These tokens are short-lived and scoped, significantly reducing credential exposure risks.
By contrast, static client secrets (B) or PATs (C) are long-lived and require periodic manual rotation, increasing security vulnerability. Shared user accounts (D) violate least-privilege and auditability principles.
Therefore, A provides the strongest, most compliant authentication model for automated CLI and CI/CD workflows.

NEW QUESTION # 41
A DLT pipeline includes the following streaming tables:
Raw_lot ingest raw device measurement data from a heart rate tracking device.
Bgm_stats incrementally computes user statistics based on BPM measurements from raw_lot.
How can the data engineer configure this pipeline to be able to retain manually deleted or updated records in the raw_iot table while recomputing the downstream table when a pipeline update is run?

A. Set the pipelines, reset, allowed property to false on raw_iot
B. Set the SkipChangeCommits flag to true raw_lot
C. Set the skipChangeCommits flag to true on bpm_stats
D. Set the pipelines, reset, allowed property to false on bpm_stats

Answer: A

Explanation:
In Databricks Lakehouse, to retain manually deleted or updated records in the raw_iot table while recomputing downstream tables when a pipeline update is run, the property pipelines.reset.allowed should be set to false. This property prevents the system from resetting the state of the table, which includes the removal of the history of changes, during a pipeline update. By keeping this property as false, any changes to the raw_iot table, including manual deletes or updates, are retained, and recomputation of downstream tables, such as bpm_stats, can occur with the full history of data changes intact.
:
Databricks documentation on DLT pipelines: https://docs.databricks.com/data-engineering/delta-live-tables
/delta-live-tables-overview.html

NEW QUESTION # 42
The Delta Live Tables Pipeline is configured to run in Development mode using the Triggered Pipeline Mode.
what is the expected outcome after clicking Start to update the pipeline?

A. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will be deployed for the update and terminated when the pipeline is stopped
B. All datasets will be updated at set intervals until the pipeline is shut down. The compute resources will persist after the pipeline is stopped to allow for additional development and testing
C. All datasets will be updated once and the pipeline will shut down. The compute resources will be terminated
D. All datasets will be updated continuously and the pipeline will not shut down. The compute resources will persist with the pipeline
E. All datasets will be updated once and the pipeline will shut down. The compute resources will persist to allow for additional development and testing

Answer: D

Explanation:
Explanation
The answer is All datasets will be updated once and the pipeline will shut down. The compute re-sources will persist to allow for additional testing.
DLT pipeline supports two modes Development and Production, you can switch between the two based on the stage of your development and deployment lifecycle.
Development and production modes
When you run your pipeline in development mode, the Delta Live Tables system:
*Reuses a cluster to avoid the overhead of restarts.
*Disables pipeline retries so you can immediately detect and fix errors.
In production mode, the Delta Live Tables system:
*Restarts the cluster for specific recoverable errors, including memory leaks and stale credentials.
*Retries execution in the event of specific errors, for example, a failure to start a cluster.
Use the buttons in the Pipelines UI to switch between develop-ment and production modes. By default, pipelines run in development mode.
Switching between development and production modes only controls cluster and pipeline execution behavior.
Storage locations must be configured as part of pipeline settings and are not affected when switching between modes.
Please review additional DLT concepts using below link
https://docs.databricks.com/data-engineering/delta-live-tables/delta-live-tables-concepts.html#delta-live-tables-c

NEW QUESTION # 43
The data architect has mandated that all tables in the Lakehouse should be configured as external Delta Lake tables.
Which approach will ensure that this requirement is met?

A. When the workspace is being configured, make sure that external cloud object storage has been mounted.
B. When tables are created, make sure that the external keyword is used in the create table statement.
C. Whenever a table is being created, make sure that the location keyword is used.
D. When configuring an external data warehouse for all table storage. leverage Databricks for all ELT.
E. Whenever a database is being created, make sure that the location keyword is used

Answer: C

Explanation:
This is the correct answer because it ensures that this requirement is met. The requirement is that all tables in the Lakehouse should be configured as external Delta Lake tables. An external table is a table that is stored outside of the default warehouse directory and whose metadata is not managed by Databricks. An external table can be created by using the location keyword to specify the path to an existing directory in a cloud storage system, such as DBFS or S3. By creating external tables, the data engineering team can avoid losing data if they drop or overwrite the table, as well as leverage existing data without moving or copying it. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "Create an external table" section.

NEW QUESTION # 44
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