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

Section	Objectives
Topic 1: 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 2: Data Modeling	- Design and optimize data models 1. Simplify data layout decisions and optimize query performance using liquid clustering 2. Design dimensional models for analytical workloads with efficient querying and aggregation 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Identify the benefits of liquid clustering over partitioning and Z-Ordering
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. Create pipeline components using control flow operators such as if/else and foreach 2. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 3. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 4. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 5. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 6. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 7. Explain the advantages and disadvantages of streaming tables compared to materialized views 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: 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 6: Cost & Performance Optimization	- Optimize cost and performance 1. Apply Change Data Feed to address streaming table limitations and improve latency 2. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden 3. Understand Delta optimization techniques such as deletion vectors and liquid clustering 4. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 5. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning
Topic 7: Debugging and Deploying	- Debugging and Troubleshooting 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 2. Analyze errors and remediate failed job runs using job repairs and parameter overrides 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 8: Data Sharing and Federation	- Share and federate data 1. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol 2. Use Delta Sharing to share live data from the Lakehouse with any computing platform 3. Configure Lakehouse Federation with appropriate governance across supported source systems
Topic 9: Monitoring and Alerting	- Alerting 1. Use the Workflows UI and Jobs API to configure notifications for job status and performance issues 2. Use SQL Alerts to monitor data quality - Monitoring 1. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 2. Use Query Profile and Spark UI to monitor workloads 3. Use system tables for observability of resource utilization, cost, auditing, and workloads 4. Use Lakeflow Declarative Pipelines event logs to monitor pipelines
Topic 10: Ensuring Data Security and Compliance	- 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 - Applying Data Security Mechanisms 1. Use row filters and column masks to protect sensitive table data 2. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization 3. Use ACLs to secure workspace objects and enforce the principle of least privilege

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Databricks Certified Data Engineer Professional Sample Questions (Q247-Q252):

NEW QUESTION # 247
Which statement describes the default execution mode for Databricks Auto Loader?

A. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
B. Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
C. New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
D. New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
E. Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.

Answer: D

Explanation:
Databricks Auto Loader simplifies and automates the process of loading data into Delta Lake.
The default execution mode of the Auto Loader identifies new files by listing the input directory. It incrementally and idempotently loads these new files into the target Delta Lake table. This approach ensures that files are not missed and are processed exactly once, avoiding data duplication. The other options describe different mechanisms or integrations that are not part of the default behavior of the Auto Loader.

NEW QUESTION # 248
A data engineer is building a Lakeflow Declarative Pipelines pipeline to process healthcare claims data. A metadata JSON file defines data quality rules for multiple tables, including:
{
"claims": [
{"name": "valid_patient_id", "constraint": "patient_id IS NOT NULL"},
{"name": "non_negative_amount", "constraint": "claim_amount >= 0"}
]
}
The pipeline must dynamically apply these rules to the claims table without hardcoding the rules.
How should the data engineer achieve this?

A. Reference each expectation with @dlt.expect decorators in the table declaration.
B. Load the JSON metadata, loop through its entries, and apply expectations using dlt.expect_all.
C. Invoke an external API to validate records against the metadata rules.
D. Use a SQL CONSTRAINT block referencing the JSON file path.

Answer: B

Explanation:
Lakeflow Declarative Pipelines provide the expect_all method for programmatically applying multiple data quality expectations at once. The documentation explains that @dlt.expect_all accepts a dictionary of expectation names mapped to SQL constraints, allowing rules to be dynamically loaded from metadata such as JSON files. This ensures that pipelines remain maintainable and scalable without needing to hardcode individual @dlt.expect decorators. The event logs will track each expectation's pass and fail counts individually, making it auditable.

NEW QUESTION # 249
A data engineer wants to ingest a large collection of image files (JPEG and PNG) from cloud object storage into a Unity Catalog-managed table for analysis and visualization. Which two configurations and practices are recommended to incrementally ingest these images into the table? (Choose two.)

A. Use Auto Loader and set cloudFiles.format to "TEXT".
B. Use the pathGlobFilter option to select only image files (e.g., "*.jpg,*.png").
C. Move files to a volume and read with SQL editor.
D. Use Auto Loader and set cloudFiles.format to "BINARYFILE".
E. Use Auto Loader and set cloudFiles.format to "IMAGE".

Answer: B,D

Explanation:
Databricks Auto Loader supports ingestion of binary file formats using the cloudFiles.format option. For ingesting JPEG or PNG image files, the correct setting is "BINARYFILE", which loads the raw binary content and file metadata into a DataFrame. Additionally, when processing files from object storage, it is best practice to apply pathGlobFilter to limit ingestion to specific file types and reduce unnecessary scanning of non-image files. Options like "IMAGE" or "TEXT" are invalid, and using volumes with SQL editors does not provide incremental ingestion. Therefore, combining Auto Loader with cloudFiles.format="BINARYFILE" and pathGlobFilter ensures scalable, incremental ingestion of image data into Unity Catalog tables.

NEW QUESTION # 250
A data engineer manages a Unity Catalog table customer_data in schema finance that includes sensitive fields like ssn and credit_score. Intern Group should only see masked values, while Analyst Group should only access rows for their assigned region. The data engineer needs to restrict access based on user role and region without duplicating data. How should the data engineer enforce this security policy?

A. Use Unity Catalog's row filters based on the user roles and column masks based on the region.
B. Use Unity Catalog's row filters based on the region and column masks based on user roles.
C. Create views using current_user() and is_account_group_member() functions, and apply masking logic inside the SQL SELECT clause for each sensitive column.
D. Create dynamic views for each user role and manage access with ACLs.

Answer: B

Explanation:
Unity Catalog row filters can restrict which rows are visible based on attributes such as the user's assigned region, while column masks can dynamically obfuscate sensitive fields like ssn and credit_score based on user roles. This enforces fine-grained, role-and region-based access control directly at the table level without duplicating data or relying on custom views.

NEW QUESTION # 251
A workspace admin has created a new catalog called finance_data and wants to delegate permission management to a finance team lead without giving them full admin rights. Which privilege should be granted to the finance team lead?

A. Make the finance team lead a metastore admin.
B. ALL PRIVILEGES on the finance_data catalog.
C. MANAGE privilege on the finance_data catalog.
D. GRANT OPTION privilege on the finance_data catalog.

Answer: C

Explanation:
The MANAGE privilege in Unity Catalog provides the ability to grant and revoke privileges on the specified object (in this case, a catalog) without giving full administrative access or ownership.
This is the Databricks-recommended approach for delegating governance responsibilities while preserving the principle of least privilege.
By contrast, the ALL PRIVILEGES option grants excessive access (including read and write permissions), and metastore admin status provides global control over all catalogs--far exceeding the requirement. The MANAGE privilege enables the finance team lead to control access to objects within finance_data responsibly while limiting overall administrative exposure.

NEW QUESTION # 252
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