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

Section	Objectives
Topic 1: Data Governance	- Govern enterprise data 1. Create and add descriptions and metadata to enterprise data to improve discoverability 2. Demonstrate understanding of the Unity Catalog permission inheritance model
Topic 2: Debugging and Deploying	- Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs using job repairs and parameter overrides 2. Use Lakeflow Declarative Pipelines event logs and Spark UI to debug Lakeflow Declarative Pipelines and Spark pipelines 3. Identify diagnostic information using Spark UI, cluster logs, system tables, and query profiles to troubleshoot errors - 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 3: 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
Topic 4: Developing Code for Data Processing using Python and SQL	- Building and Testing an ETL Pipeline with Lakeflow Declarative Pipelines, SQL, and Apache Spark 1. Use APPLY CHANGES APIs to simplify CDC in Lakeflow Declarative Pipelines 2. Choose appropriate configurations for environments, dependencies, high-memory notebook tasks, and retry behavior 3. Compare Spark Structured Streaming and Lakeflow Declarative Pipelines to determine the optimal approach for scalable ETL pipelines 4. Create pipeline components using control flow operators such as if/else and foreach 5. Develop unit and integration tests using assertDataFrameEqual, assertSchemaEqual, DataFrame.transform, testing frameworks, and debugging tools 6. Explain the advantages and disadvantages of streaming tables compared to materialized views 7. Build and manage reliable, production-ready batch and streaming data pipelines using Lakeflow Declarative Pipelines and Auto Loader 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. Design and implement a scalable Python project structure optimized for Databricks Asset Bundles, enabling modular development, deployment automation, and CI/CD integration 3. Develop User-Defined Functions using Pandas/Python UDF
Topic 5: Data Modeling	- Design and optimize data models 1. Identify the benefits of liquid clustering over partitioning and Z-Ordering 2. Design and implement scalable data models using Delta Lake to manage large datasets 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 6: 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 system tables for observability of resource utilization, cost, auditing, and workloads 3. Use Lakeflow Declarative Pipelines event logs to monitor pipelines 4. Use Query Profile and Spark UI to monitor workloads
Topic 7: 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. Use row filters and column masks to protect sensitive table data 3. Apply anonymization and pseudonymization methods including hashing, tokenization, suppression, and generalization
Topic 8: 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
Topic 9: 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 10: Cost & Performance Optimization	- Optimize cost and performance 1. Use query profiling to identify bottlenecks such as inefficient joins and data shuffling 2. Understand Databricks query optimization techniques for large datasets, including data skipping and file pruning 3. Understand Delta optimization techniques such as deletion vectors and liquid clustering 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

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Databricks Certified Data Engineer Professional Sample Questions (Q56-Q61):

NEW QUESTION # 56
Which statement regarding stream-static joins and static Delta tables is correct?

A. The checkpoint directory will be used to track state information for the unique keys present in the join.
B. Stream-static joins cannot use static Delta tables because of consistency issues.
C. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch.
D. Each microbatch of a stream-static join will use the most recent version of the static Delta table as of the job's initialization.
E. The checkpoint directory will be used to track updates to the static Delta table.

Answer: C

Explanation:
This is the correct answer because stream-static joins are supported by Structured Streaming when one of the tables is a static Delta table. A static Delta table is a Delta table that is not updated by any concurrent writes, such as appends or merges, during the execution of a streaming query. In this case, each microbatch of a stream-static join will use the most recent version of the static Delta table as of each microbatch, which means it will reflect any changes made to the static Delta table before the start of each microbatch.

NEW QUESTION # 57
A data architect is designing a Databricks solution to efficiently process data for different business requirements. In which scenario should a data engineer use a materialized view compared to a streaming table?

A. Implementing a CDC (Change Data Capture) pipeline that needs to detect and respond to database changes within seconds.
B. Processing high-volume, continuous clickstream data from a website to monitor user behavior in real-time.
C. Precomputing complex aggregations and joins from multiple large tables to accelerate BI dashboard performance.
D. Ingesting data from Apache Kafka topics with sub-second processing requirements for immediate alerting.

Answer: C

Explanation:
Materialized views in Databricks are optimized for precomputing and caching results of complex SQL queries, joins, and aggregations. They store query outputs physically and automatically refresh on a schedule or incremental change basis, drastically improving BI dashboard performance and reducing compute costs.
Conversely, streaming tables are designed for real-time data ingestion and processing, enabling event-driven analytics and low-latency use cases.
Databricks documentation explicitly recommends materialized views for analytical workloads with periodic updates and streaming tables for continuously updating sources. Therefore, the correct choice is C, where complex aggregations from large tables benefit most from materialized precomputation for fast reporting.

NEW QUESTION # 58
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. 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.
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. 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.
D. Abort the merge, discard all local changes, and try the merge operation again without reviewing the conflicting code.

Answer: C

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 # 59
A junior data engineer has been asked to develop a streaming data pipeline with a grouped aggregation using DataFrame df. The pipeline needs to calculate the average humidity and average temperature for each non-overlapping five-minute interval. Events are recorded once per minute per device.
Streaming DataFrame df has the following schema:
"device_id INT, event_time TIMESTAMP, temp FLOAT, humidity FLOAT"
Code block:

Choose the response that correctly fills in the blank within the code block to complete this task.

A. lag("event_time", "10 minutes").alias("time")
B. window("event_time", "5 minutes").alias("time")
C. window("event_time", "10 minutes").alias("time")
D. "event_time"
E. to_interval("event_time", "5 minutes").alias("time")

Answer: B

Explanation:
This is the correct answer because the window function is used to group streaming data by time intervals. The window function takes two arguments: a time column and a window duration. The window duration specifies how long each window is, and must be a multiple of 1 second. In this case, the window duration is "5 minutes", which means each window will cover a non-overlapping five- minute interval. The window function also returns a struct column with two fields: start and end, which represent the start and end time of each window. The alias function is used to rename the struct column as "time".

NEW QUESTION # 60
A data engineer is optimizing a managed Delta table that suffers from data skew and frequently changing query filter columns. The engineer wants to avoid costly data rewrites when query patterns evolve. The table size is under 1 TB. How should the data engineer meet this requirement?

A. Apply Z-ordering, since it allows flexible reorganization of data layout without rewriting existing files and adapts easily to new filter columns.
B. Combine partitioning and Z-ordering to maximize flexibility and minimize maintenance as query patterns change.
C. Use Hive-style partitioning, as it provides efficient data skipping and is easy to change partition columns at any time.
D. Enable liquid clustering, as it efficiently handles data skew, allows clustering keys to be changed without rewriting existing data, and adapts to evolving query patterns.

Answer: D

Explanation:
Liquid clustering is designed for managed tables under 1TB with evolving query patterns. It efficiently addresses data skew, continuously optimizes data layout, and allows clustering keys to be changed without requiring full data rewrites, making it well suited for frequently changing filter columns while minimizing maintenance overhead.

NEW QUESTION # 61
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