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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 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
Cost & Performance Optimization	- Optimize cost and performance 1. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 2. Understand Delta optimization techniques such as deletion vectors and liquid clustering 3. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 4. Apply Change Data Feed to address streaming table limitations and improve latency 5. Understand how and why Unity Catalog managed tables reduce operational overhead and maintenance burden
Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 2. Manage and troubleshoot external third-party library installations and dependencies, including PyPI packages, local wheels, and source archives 3. Develop User-Defined Functions using Pandas/Python UDF - 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. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 3. Explain the advantages and disadvantages of streaming tables compared to materialized views 4. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 5. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 6. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 7. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 8. Create and automate ETL workloads using Jobs through the UI, APIs, or CLI
Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Simplify data layout decisions and optimize query performance using liquid clustering 3. Design and implement scalable data models using Delta Lake to manage large datasets 4. Design dimensional models for analytical workloads with efficient querying and aggregation
Monitoring and Alerting	- 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 - Monitoring 1. Use Query Profile and Spark UI to monitor workloads 2. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 3. Use Databricks REST APIs and Databricks CLI to monitor jobs and pipelines 4. Use system tables for observability of resource utilization, cost, auditing, and workloads
Debugging and Deploying	- 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 - Debugging and Troubleshooting 1. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 3. Analyze errors and remediate failed job runs using job repairs and parameter overrides
Ensuring Data Security and Compliance	- Ensuring Compliance 1. Implement compliant batch and streaming pipelines that detect and mask PII 2. Develop data purging solutions that comply with data retention policies - 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
Data Transformation, Cleansing, and Quality	- Transform and validate data 1. Develop a quarantining process for bad data with Lakeflow Declarative Pipelines or Auto Loader in classic jobs 2. Write efficient Spark SQL and PySpark code for advanced transformations including window functions, joins, and aggregations
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
Data Sharing and Federation	- Share and federate data 1. Use Delta Sharing to share live data from the Lakehouse with any computing platform 2. Configure Lakehouse Federation with appropriate governance across supported source systems 3. Demonstrate secure Delta Sharing between Databricks deployments using Databricks-to-Databricks sharing or with external platforms using the open sharing protocol

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Databricks Certified Data Engineer Professional Sample Questions (Q176-Q181):

NEW QUESTION # 176
The data governance team is reviewing code used for deleting records for compliance with GDPR. They note the following logic is used to delete records from the Delta Lake table named users.

Assuming that user_id is a unique identifying key and that delete_requests contains all users that have requested deletion, which statement describes whether successfully executing the above logic guarantees that the records to be deleted are no longer accessible and why?

A. No; the Delta Lake delete command only provides ACID guarantees when combined with the merge into command.
B. No; files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files.
C. Yes; the Delta cache immediately updates to reflect the latest data files recorded to disk.
D. No; the Delta cache may return records from previous versions of the table until the cluster is restarted.
E. Yes; Delta Lake ACID guarantees provide assurance that the delete command succeeded fully and permanently purged these records.

Answer: B

Explanation:
The code uses the DELETE FROM command to delete records from the users table that match a condition based on a join with another table called delete_requests, which contains all users that have requested deletion. The DELETE FROM command deletes records from a Delta Lake table by creating a new version of the table that does not contain the deleted records. However, this does not guarantee that the records to be deleted are no longer accessible, because Delta Lake supports time travel, which allows querying previous versions of the table using a timestamp or version number. Therefore, files containing deleted records may still be accessible with time travel until a vacuum command is used to remove invalidated data files from physical storage.

NEW QUESTION # 177
An upstream system is emitting change data capture (CDC) logs that are being written to a cloud object storage directory. Each record in the log indicates the change type (insert, update, or delete) and the values for each field after the change. The source table has a primary key identified by the field pk_id.
For analytical purposes, only the most recent value for each record needs to be recorded in the target Delta Lake table in the Lakehouse. The Databricks job to ingest these records occurs once per hour, but each individual record may have changed multiple times over the course of an hour.
Which solution meets these requirements?

A. Iterate through an ordered set of changes to the table, applying each in turn to create the current state of the table, (insert, update, delete), timestamp of change, and the values.
B. Deduplicate records in each batch by pk_id and overwrite the target table.
C. Use Delta Lake's change data feed to automatically process CDC data from an external system, propagating all changes to all dependent tables in the Lakehouse.
D. Use MERGE INTO to insert, update, or delete the most recent entry for each pk_id into a table, then propagate all changes throughout the system.

Answer: C

NEW QUESTION # 178
A data engineer is designing a pipeline in Databricks that processes records from a Kafka stream where late-arriving data is common. Which approach should the data engineer use?

A. Use batch processing and overwrite the entire output table each time to ensure late data is incorporated correctly.
B. Use a watermark to specify the allowed lateness to accommodate records that arrive after their expected window, ensuring correct aggregation and state management.
C. Implement a custom solution using Databricks Jobs to periodically reprocess all historical data.
D. Use an Auto CDC pipeline with batch tables to simplify late data handling.

Answer: B

Explanation:
In Structured Streaming, event-time watermarks control how long the engine waits for late- arriving data before finalizing aggregations. By setting an appropriate watermark, Databricks can handle late data gracefully -- incorporating records that arrive within the defined window while discarding excessively delayed events.
This approach ensures accurate aggregations, minimizes state size, and prevents memory leaks.
Manual reprocessing (A) or overwriting entire datasets (B) is inefficient and costly, while Auto CDC (C) is used for change tracking in Delta tables, not for streaming event lateness.
Thus, using watermarking is the recommended and official approach for managing late data in streaming pipelines.

NEW QUESTION # 179
A data engineer needs to install the PyYAML Python package within an air-gapped Databricks environment. The workspace has no direct internet access to PyPI. The engineer has downloaded the .whl file locally and wants it available automatically on all new clusters. Which approach should the data engineer use?

A. Set up a private PyPI repository and install via pip index URL.
B. Upload the PyYAML .whl file to the user home directory and create a cluster-scoped init script to install it.
C. Upload the PyYAML .whl file to a Unity Catalog Volume, ensure it's allow-listed, and create a cluster-scoped init script that installs it from that path.
D. Add the .whl file to Databricks Git Repos and assume automatic installation.

Answer: C

Explanation:
For secure, air-gapped Databricks deployments, the recommended practice is to host dependency files such as .whl packages in Unity Catalog Volumes -- a managed storage layer governed by Unity Catalog.
Once stored in a volume, these files can be safely referenced from cluster-scoped init scripts, which automatically execute installation commands (e.g., pip install
/Volumes/catalog/schema/path/PyYAML.whl) during cluster startup.
This ensures consistent environment setup across clusters and compliance with data governance rules.
User directories (A) lack enterprise security controls; private repositories (C) are not viable in air- gapped setups; and Git repos (D) do not trigger package installation. Therefore, B is the correct and officially approved method.

NEW QUESTION # 180
Which REST API call can be used to review the notebooks configured to run as tasks in a multi- task job?

A. /jobs/runs/get-output
B. /jobs/runs/list
C. /jobs/get
D. /jobs/runs/get
E. /jobs/list

Answer: C

Explanation:
https://docs.databricks.com/api/workspace/jobs/getresponses/settings/tasks/notebook_task/noteb ook_path

NEW QUESTION # 181
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