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

Section	Objectives
Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Build append-only pipelines for batch and streaming data using Delta 3. Ingest data from message buses and cloud storage
Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Query Performance 1. Identify inefficient joins and excessive data shuffling 2. Use Query Profile to identify performance bottlenecks - Delta Optimization 1. Use Change Data Feed to address streaming table limitations and improve latency 2. Apply data skipping and file pruning techniques 3. Understand deletion vectors and liquid clustering
Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure Databricks-to-Databricks Sharing 2. Configure sharing with external platforms using the open sharing protocol 3. Share live Lakehouse data with external computing platforms
Data Governance	- Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data - Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model
Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Compare streaming tables and materialized views 2. Configure environments, dependencies, memory, and retry behavior 3. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 4. Use APPLY CHANGES APIs for change data capture 5. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 6. Develop unit and integration tests for data processing code 7. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 8. Use control flow operators in pipeline components - Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Develop User-Defined Functions using Pandas/Python UDFs
Data Modelling	- Scalable Data Models 1. Optimize data layout using Liquid Clustering 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Design and implement scalable data models using Delta Lake - Dimensional Modelling 1. Design dimensional models for analytical workloads
Monitoring and Alerting	- Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring - Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Query Profiler and Spark UI to monitor workloads 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
Debugging and Deploying	- Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging 3. Analyze errors and remediate failed job runs - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Data Transformation, Cleansing, and Quality	- Advanced Data Transformation 1. Write efficient Spark SQL and PySpark transformations 2. Apply window functions, joins, and aggregations to large datasets - Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data
Ensuring Data Security and Compliance	- Data Security 1. Use ACLs to secure workspace objects and enforce least privilege 2. Apply anonymization and pseudonymization techniques 3. Use row filters and column masks for sensitive data - Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information

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Databricks Certified Data Engineer Professional Sample Questions (Q249-Q254):

NEW QUESTION # 249
Each configuration below is identical to the extent that each cluster has 400 GB total of RAM, 160 total cores and only one Executor per VM.
Given a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

A. Total VMs: 2
200 GB per Executor
80 Cores / Executor
B. Total VMs: 1
400 GB per Executor
160 Cores / Executor
C. Total VMs: 8
50 GB per Executor
20 Cores / Executor
D. Total VMs: 4
100 GB per Executor
40 Cores/Executor

Answer: B

Explanation:
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 250
A platform engineer is creating catalogs and schemas for the development team to use.
The engineer has created an initial catalog, catalog_A, and initial schema, schema_A. The engineer has also granted USE CATALOG, USE SCHEMA, and CREATE TABLE to the development team so that the engineer can begin populating the schema with new tables.
Despite being owner of the catalog and schema, the engineer noticed that they do not have access to the underlying tables in Schema_A.
What explains the engineer's lack of access to the underlying tables?

A. The owner of the schema does not automatically have permission to tables within the schema, but can grant them to themselves at any point.
B. Users granted with USE CATALOG can modify the owner's permissions to downstream tables.
C. The platform engineer needs to execute a REFRESH statement as the table permissions did not automatically update for owners.
D. Permissions explicitly given by the table creator are the only way the Platform Engineer could access the underlying tables in their schema.

Answer: A

Explanation:
In Databricks, owning a catalog or schema does not automatically grant access to the tables within it. Table-level permissions are separate, so even the schema or catalog owner must be explicitly granted privileges on individual tables or use the ability to grant themselves access.

NEW QUESTION # 251
Two data engineers are working on the same Databricks notebook in separate branches. Both have edited the same section of code. When one tries to merge the other's branch into their own using the Databricks Git folders UI, a merge conflict occurs on that notebook file. The UI highlights the conflict and presents options for resolution. How should the data engineers resolve this merge conflict using Databricks Git folders?

A. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.
B. Use the Git CLI in the cluster's web terminal to force-push the conflicted merge (git push -force), overriding the remote branch with the local version and discarding changes.
C. Delete the conflicted notebook file via the Databricks workspace UI, commit the deletion, and recreate the notebook from scratch in a new commit to bypass the conflict entirely.
D. Use the Git folders UI to manually edit the notebook file, selecting the desired lines from both versions and removing the conflict markers, then mark the conflict as resolved.

Answer: D

Explanation:
In the Databricks Git folders integration, when merge conflicts arise in notebooks, the UI provides a visual diff editor that highlights conflicting code segments. Users can manually choose which changes to keep from each branch, edit directly in the notebook UI, and remove conflict markers.
After resolving, the engineer must mark the conflict as resolved, save, and commit the final version.
This process ensures that both contributors' valid code segments are merged correctly and version history is maintained.
Forcing a push (C) or deleting notebooks (B) introduces data loss or versioning issues. Aborting without review (A) violates collaborative best practices. Therefore, D is the only correct and Databricks-approved way to resolve notebook merge conflicts.

NEW QUESTION # 252
A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?

A. Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
B. Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
C. Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
D. Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.

Answer: C

Explanation:
Lakehouse Federation is configured by defining secure connections to external data sources and registering them as foreign catalogs in Unity Catalog. Access is then governed using Unity Catalog permissions at the catalog, schema, and table levels, enabling analysts to query external tables securely without data duplication.

NEW QUESTION # 253
In order to prevent accidental commits to production data, a senior data engineer has instituted a policy that all development work will reference clones of Delta Lake tables. After testing both deep and shallow clone, development tables are created using shallow clone. A few weeks after initial table creation, the cloned versions of several tables implemented as Type 1 Slowly Changing Dimension (SCD) stop working. The transaction logs for the source tables show that vacuum was run the day before.
Why are the cloned tables no longer working?

A. Running vacuum automatically invalidates any shallow clones of a table; deep clone should always be used when a cloned table will be repeatedly queried.
B. Because Type 1 changes overwrite existing records, Delta Lake cannot guarantee data consistency for cloned tables.
C. The metadata created by the clone operation is referencing data files that were purged as invalid by the vacuum command
D. Tables created with SHALLOW CLONE are automatically deleted after their default retention threshold of 7 days.
E. The data files compacted by vacuum are not tracked by the cloned metadata; running refresh on the cloned table will pull in recent changes.

Answer: C

Explanation:
In Delta Lake, a shallow clone creates a new table by copying the metadata of the source table without duplicating the data files. When the vacuum command is run on the source table, it removes old data files that are no longer needed to maintain the transactional log's integrity, potentially including files referenced by the shallow clone's metadata. If these files are purged, the shallow cloned tables will reference non-existent data files, causing them to stop working properly. This highlights the dependency of shallow clones on the source table's data files and the impact of data management operations like vacuum on these clones.

NEW QUESTION # 254
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