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

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

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Databricks Certified Professional Data Engineer Exam Sample Questions (Q189-Q194):

NEW QUESTION # 189
What are the different ways you can schedule a job in Databricks workspace?

A. Cron, File notification from Cloud object storage
B. Cron, On Demand runs
C. Once, Continuous
D. On-Demand runs, File notification from Cloud object storage
E. Continuous, Incremental

Answer: B

Explanation:
Explanation
The answer is, Cron, On-Demand runs
Supports running job immediately or using can be scheduled using CRON syntax

NEW QUESTION # 190
A data architect has designed a system in which two Structured Streaming jobs will concurrently write to a single bronze Delta table. Each job is subscribing to a different topic from an Apache Kafka source, but they will write data with the same schema. To keep the directory structure simple, a data engineer has decided to nest a checkpoint directory to be shared by both streams.
The proposed directory structure is displayed below:

Which statement describes whether this checkpoint directory structure is valid for the given scenario and why?

A. Yes; both of the streams can share a single checkpoint directory.
B. Yes; Delta Lake supports infinite concurrent writers.
C. No; each of the streams needs to have its own checkpoint directory.
D. No; Delta Lake manages streaming checkpoints in the transaction log.
E. No; only one stream can write to a Delta Lake table.

Answer: C

Explanation:
This is the correct answer because checkpointing is a critical feature of Structured Streaming that provides fault tolerance and recovery in case of failures. Checkpointing stores the current state and progress of a streaming query in a reliable storage system, such as DBFS or S3. Each streaming query must have its own checkpoint directory that is unique and exclusive to that query. If two streaming queries share the same checkpoint directory, they will interfere with each other and cause unexpected errors or data loss. Verified References: [Databricks Certified Data Engineer Professional], under "Structured Streaming" section; Databricks Documentation, under "Checkpointing" section.

NEW QUESTION # 191
The data engineer team is configuring environment for development testing, and production before beginning migration on a new data pipeline. The team requires extensive testing on both the code and data resulting from code execution, and the team want to develop and test against similar production data as possible.
A junior data engineer suggests that production data can be mounted to the development testing environments, allowing pre production code to execute against production data. Because all users have Admin privileges in the development environment, the junior data engineer has offered to configure permissions and mount this data for the team.
Which statement captures best practices for this situation?

A. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
B. Because delta Lake versions all data and supports time travel, it is not possible for user error or malicious actors to permanently delete production data, as such it is generally safe to mount production data anywhere.
C. All developer, testing and production code and data should exist in a single unified workspace; creating separate environments for testing and development further reduces risks.
D. In environments where interactive code will be executed, production data should only be accessible with read permissions; creating isolated databases for each environment further reduces risks.

Answer: D

Explanation:
The best practice in such scenarios is to ensure that production data is handled securely and with proper access controls. By granting only read access to production data in development and testing environments, it mitigates the risk of unintended data modification. Additionally, maintaining isolated databases for different environments helps to avoid accidental impacts on production data and systems.
:
Databricks best practices for securing data: https://docs.databricks.com/security/index.html

NEW QUESTION # 192
An upstream source writes Parquet data as hourly batches to directories named with the current date. A nightly batch job runs the following code to ingest all data from the previous day as indicated by the date variable:

Assume that the fields customer_id and order_id serve as a composite key to uniquely identify each order.
If the upstream system is known to occasionally produce duplicate entries for a single order hours apart, which statement is correct?

A. Each write to the orders table will only contain unique records, but newly written records may have duplicates already present in the target table.
B. Each write to the orders table will only contain unique records, and only those records without duplicates in the target table will be written.
C. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, these records will be overwritten.
D. Each write to the orders table will run deduplication over the union of new and existing records, ensuring no duplicate records are present.
E. Each write to the orders table will only contain unique records; if existing records with the same key are present in the target table, the operation will tail.

Answer: A

Explanation:
This is the correct answer because the code uses the dropDuplicates method to remove any duplicate records within each batch of data before writing to the orders table. However, this method does not check for duplicates across different batches or in the target table, so it is possible that newly written records may have duplicates already present in the target table. To avoid this, a better approach would be to use Delta Lake and perform an upsert operation using mergeInto. Verified Reference: [Databricks Certified Data Engineer Professional], under "Delta Lake" section; Databricks Documentation, under "DROP DUPLICATES" section.

NEW QUESTION # 193
A data engineering team is configuring access controls in Databricks Unity Catalog. They grant the SELECT privilege on the sales catalog to the analyst_group, expecting that members of this group will automatically have SELECT access to all current and future schemas, tables, and views within the catalog.
What describes the privilege inheritance behavior in Unity Catalog?

A. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.
B. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.
C. Privileges in Unity Catalog do not cascade; SELECT must be explicitly granted on each schema and table, even if granted at the catalog level.
D. Granting SELECT at the catalog level applies to existing schemas and tables but not to those created in the future.

Answer: C

Explanation:
Comprehensive and Detailed Explanation From Exact Extract of Databricks Data Engineer Documents:
In Unity Catalog, privileges are non-cascading-meaning that granting a privilege (like SELECT) on a catalog does not automatically grant the same privilege on contained objects (schemas, tables, or views). Each object type has its own independent access control hierarchy.
According to the Databricks access control documentation: "Privileges do not automatically cascade from catalog to schema or table levels." Administrators must explicitly grant privileges on each level if users need access across objects. This design ensures tighter governance and least-privilege enforcement. Therefore, option B correctly describes Unity Catalog's privilege model, while A and D incorrectly imply automatic inheritance.

NEW QUESTION # 194
......

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