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

Section	Objectives
Topic 1: Data Modeling	- Design and optimize data models 1. Design and implement scalable data models using Delta Lake to manage large datasets 2. Identify the benefits of liquid clustering over partitioning and Z-Ordering 3. Simplify data layout decisions and optimize query performance using liquid clustering 4. Design dimensional models for analytical workloads with efficient querying and aggregation
Topic 2: Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Understand Delta optimization techniques such as deletion vectors and liquid clustering 3. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
Topic 3: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 2. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 3. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 4. Create pipeline components using control flow operators such as if/else and foreach 5. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 6. Explain the advantages and disadvantages of streaming tables compared to materialized views 7. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 8. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI - Using Python and Tools for Development 1. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 2. Develop User-Defined Functions using Pandas/Python UDF 3. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration
Topic 4: Data Governance	- Govern enterprise data 1. Demonstrate understanding of the Unity Catalog permission inheritance model 2. Create and add descriptions and metadata to enterprise data to improve discoverability
Topic 5: Debugging and Deploying	- Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs using job repairs and parameter overrides 2. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 3. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Configure and integrate Git-based CI/CD workflows using Databricks Git folders for notebook and code deployment
Topic 6: Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations 2. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs
Topic 7: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest formats including Delta Lake, Parquet, ORC, AVRO, JSON, CSV, XML, text, and binary data from sources such as message buses and cloud storage 2. Create an append-only data pipeline capable of handling both batch and streaming data using Delta
Topic 8: Monitoring and Alerting	- Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use system tables for observability of resource utilization, cost, auditing, and workloads 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 4. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines - Alerting 1. Use SQL Alerts to monitor data quality 2. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues
Topic 9: Ensuring Data Security and Compliance	- Applying Data Security Mechanisms 1. Use ACLs to secure workspace objects and enforce the principle of least privilege 2. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization 3. Use row filters and column masks to protect sensitive table data - Ensuring Compliance 1. Develop data purging solutions that comply with data retention policies 2. Implement compliant batch and streaming pipelines that detect and mask PII
Topic 10: Data Sharing and Federation	- Share and federate data 1. Configure Lakehouse Federation with appropriate governance across supported source systems 2. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 3. Use Delta Sharing to share live data from the Lakehouse with any computing platform

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Databricks Certified Data Engineer Professional Sample Questions (Q103-Q108):

NEW QUESTION # 103
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 at the catalog level applies to existing schemas and tables but not to those created in the future.
B. Granting SELECT on a catalog automatically applies SELECT to all current and future schemas, tables, and views within that catalog.
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. Privileges granted at the schema level override any catalog-level privileges and prevent access unless explicitly revoked.

Answer: C

Explanation:
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 # 104
A data engineer is creating a data ingestion pipeline to understand where customers are taking their rented bicycles during use. The engineer noticed that, over time, data being transmitted from the bicycle sensors fail to include key details like latitude and longitude. Downstream analysts need both the clean records and the quarantined records available for separate processing.
The data engineer already has this code:
import dlt
from pyspark.sql.functions import expr
rules = {
"valid_lat": "(lat IS NOT NULL)",
"valid_long": "(long IS NOT NULL)"
}
quarantine_rules = "NOT({})".format(" AND ".join(rules.values()))
@dlt.view
def raw_trips_data():
return spark.readStream.table("ride_and_go.telemetry.trips")
How should the data engineer meet the requirements to capture good and bad data?

A. @dlt.view
@dlt.expect_or_drop("lat_long_present", "(lat IS NOT NULL AND long IS NOT NULL)") def trips_data_quarantine():
return spark.readStream.table("ride_and_go.telemetry.trips")
B. @dlt.table(name="trips_data_quarantine")
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.filter(expr(quarantine_rules))
)
C. @dlt.table
@dlt.expect_all_or_drop(rules)
def trips_data_quarantine():
return spark.readStream.table("raw_trips_data")
D. @dlt.table(partition_cols=["is_quarantined", ])
@dlt.expect_all(rules)
def trips_data_quarantine():
return (
spark.readStream.table("raw_trips_data")
.withColumn("is_quarantined", expr(quarantine_rules))
)

Answer: B

Explanation:
The requirement is that both valid (good) and invalid (bad) records must be captured and available separately for downstream processing. Invalid records should not simply be dropped; they must be quarantined in a dedicated table.
In Databricks Lakeflow Declarative Pipelines (DLT), this is achieved by creating separate output tables:
One table for valid records (Silver table) that pass the expectations.
Another quarantine table that explicitly captures records failing the expectations.
Option A correctly implements this by:
Declaring a DLT table trips_data_quarantine.
Using .filter(expr(quarantine_rules)) to isolate invalid records (records where latitude or longitude is NULL).
This ensures analysts can query both good records (from the main Silver pipeline table) and bad records (from the quarantine table).

NEW QUESTION # 105
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 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.
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 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.

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.

NEW QUESTION # 106
What is a method of installing a Python package scoped at the notebook level to all nodes in the currently active cluster?

A. Use &sh install in a notebook cell
B. Use &Pip install in a notebook cell
C. Run source env/bin/activate in a notebook setup script
D. Install libraries from PyPi using the cluster UI

Answer: D

Explanation:
Installing a Python package scoped at the notebook level to all nodes in the currently active cluster in Databricks can be achieved by using the Libraries tab in the cluster UI. This interface allows you to install libraries across all nodes in the cluster. While the %pip command in a notebook cell would only affect the driver node, using the cluster UI ensures that the package is installed on all nodes.

NEW QUESTION # 107
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 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.
B. 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.
C. Because access to production data will always be verified using passthrough credentials it is safe to mount data to any Databricks development environment.
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.

NEW QUESTION # 108
......

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