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

Section	Objectives
Topic 1: Debugging and Deploying	- Debugging and Troubleshooting 1. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 2. Analyze errors and remediate failed job runs 3. Use Lakeflow Spark Declarative Pipelines event logs and Spark UI for debugging - Deploying CI/CD 1. Build and deploy Databricks resources using Databricks Asset Bundles 2. Integrate Git-based CI/CD workflows using Databricks Git Folders
Topic 2: 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
Topic 3: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 2. Ingest data from message buses and cloud storage 3. Build append-only pipelines for batch and streaming data using Delta
Topic 4: 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. Develop data quarantining processes for invalid data 2. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader
Topic 5: 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
Topic 6: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - Scalable Data Models 1. Design and implement scalable data models using Delta Lake 2. Optimize data layout using Liquid Clustering 3. Understand Liquid Clustering versus partitioning and Z-Ordering
Topic 7: Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Query Performance 1. Use Query Profile to identify performance bottlenecks 2. Identify inefficient joins and excessive data shuffling - Delta Optimization 1. Apply data skipping and file pruning techniques 2. Use Change Data Feed to address streaming table limitations and improve latency 3. Understand deletion vectors and liquid clustering
Topic 8: Monitoring and Alerting	- Monitoring 1. Use system tables for resource, cost, audit, and workload monitoring 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 4. Use Query Profiler and Spark UI to monitor workloads - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring
Topic 9: Developing Code for Data Processing using Python and SQL	- Using Python and Tools for Development 1. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 2. Manage and troubleshoot third-party library installations and dependencies 3. Develop User-Defined Functions using Pandas/Python UDFs - Building and Testing ETL Pipelines 1. Configure environments, dependencies, memory, and retry behavior 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 4. Develop unit and integration tests for data processing code 5. Use control flow operators in pipeline components 6. Compare streaming tables and materialized views 7. Use APPLY CHANGES APIs for change data capture 8. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines
Topic 10: Data Sharing and Federation	- Delta Sharing 1. Configure sharing with external platforms using the open sharing protocol 2. Share live Lakehouse data with external computing platforms 3. Configure Databricks-to-Databricks Sharing - Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance

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Databricks Certified Data Engineer Professional Sample Questions (Q196-Q201):

NEW QUESTION # 196
Why are Pandas UDFs often preferred over traditional PySpark UDFs in performance-critical applications involving large datasets?

A. They minimize memory usage by streaming each row individually through a lightweight Python wrapper, avoiding batch processing overhead.
B. They leverage Apache Arrow to enable vectorized operations between the JVM and Python runtimes, reducing serialization costs and improving computational efficiency.
C. They allow row-level execution of functions in Python with native Spark optimization, removing the need for columnar execution.
D. They eliminate the JVM-Python boundary by bypassing serialization entirely, thereby avoiding data conversion overhead.

Answer: B

Explanation:
Pandas UDFs use Apache Arrow to transfer data between the JVM and Python in a columnar, vectorized format. This significantly reduces serialization overhead and enables efficient batch processing, resulting in much better performance than traditional row-by-row PySpark UDFs on large datasets.

NEW QUESTION # 197
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 an extremely long-running job for which completion must be guaranteed, which cluster configuration will be able to guarantee completion of the job in light of one or more VM failures?

A. - Total VMs: 1
- 400 GB per Executor
- 160 Cores/Executor
B. - Total VMs: 2
- 200 GB per Executor
- 80 Cores / Executor
C. - Total VMs: 8
- 50 GB per Executor
- 20 Cores / Executor
D. - Total VMs: 4
- 100 GB per Executor
- 40 Cores / Executor
E. - Total VMs: 16
- 25 GB per Executor
- 10 Cores / Executor

Answer: E

NEW QUESTION # 198
A Structured Streaming job deployed to production has been experiencing delays during peak hours of the day. At present, during normal execution, each microbatch of data is processed in less than 3 seconds. During peak hours of the day, execution time for each microbatch becomes very inconsistent, sometimes exceeding 30 seconds. The streaming write is currently configured with a trigger interval of 10 seconds.
Holding all other variables constant and assuming records need to be processed in less than 10 seconds, which adjustment will meet the requirement?

A. Decrease the trigger interval to 5 seconds; triggering batches more frequently may prevent records from backing up and large batches from causing spill.
B. The trigger interval cannot be modified without modifying the checkpoint directory; to maintain the current stream state, increase the number of shuffle partitions to maximize parallelism.
C. Increase the trigger interval to 30 seconds; setting the trigger interval near the maximum execution time observed for each batch is always best practice to ensure no records are dropped.
D. Decrease the trigger interval to 5 seconds; triggering batches more frequently allows idle executors to begin processing the next batch while longer running tasks from previous batches finish.
E. Use the trigger once option and configure a Databricks job to execute the query every 10 seconds; this ensures all backlogged records are processed with each batch.

Answer: A

Explanation:
The adjustment that will meet the requirement of processing records in less than 10 seconds is to decrease the trigger interval to 5 seconds. This is because triggering batches more frequently may prevent records from backing up and large batches from causing spill. Spill is a phenomenon where the data in memory exceeds the available capacity and has to be written to disk, which can slow down the processing and increase the execution time. By reducing the trigger interval, the streaming query can process smaller batches of data more quickly and avoid spill. This can also improve the latency and throughput of the streaming job.

NEW QUESTION # 199
A data engineering team is collaborating on a Databricks project where each team member needs to develop and test code independently before merging changes into the main branch.
They want to avoid accidental overwrites or branch switching issues while ensuring that all work is version- controlled and can be integrated into their CI/CD pipeline.
How should the data engineer achieve collaboration?

A. Team members edit notebooks directly in the workspace's shared folder and periodically copy changes into a Git folder for version control.
B. Team members use the Databricks CLI to clone the Git repository and perform Git operations from a cluster's web terminal.
C. Each team member creates their own Databricks Git folder, mapped to the same remote Git repository, and works in their own development branch within their personal folder.
D. All team members work in the same Databricks Git folder and perform Git operations (pull, push, commit, branch switching) directly in that shared folder.

Answer: C

Explanation:
Using separate Databricks Git folders per user mapped to the same remote repository allows each team member to work independently on their own branch without interfering with others.
This prevents accidental overwrites or branch conflicts while ensuring all changes are version- controlled and easily integrated into CI/CD workflows.

NEW QUESTION # 200
A Delta Lake table representing metadata about content posts from users has the following schema:
user_id LONG, post_text STRING, post_id STRING, longitude FLOAT,
latitude FLOAT, post_time TIMESTAMP, date DATE
This table is partitioned by the date column. A query is run with the following filter:
longitude < 20 & longitude > -20
Which statement describes how data will be filtered?

A. The Delta Engine will scan the parquet file footers to identify each row that meets the filter criteria.
B. Statistics in the Delta Log will be used to identify data files that might include records in the filtered range.
C. The Delta Engine will use row-level statistics in the transaction log to identify the flies that meet the filter criteria.
D. Statistics in the Delta Log will be used to identify partitions that might Include files in the filtered range.
E. No file skipping will occur because the optimizer does not know the relationship between the partition column and the longitude.

Answer: B

Explanation:
This is the correct answer because it describes how data will be filtered when a query is run with the following filter: longitude < 20 & longitude > -20. The query is run on a Delta Lake table that has the following schema: user_id LONG, post_text STRING, post_id STRING, longitude FLOAT, latitude FLOAT, post_time TIMESTAMP, date DATE. This table is partitioned by the date column.
When a query is run on a partitioned Delta Lake table, Delta Lake uses statistics in the Delta Log to identify data files that might include records in the filtered range. The statistics include information such as min and max values for each column in each data file. By using these statistics, Delta Lake can skip reading data files that do not match the filter condition, which can improve query performance and reduce I/O costs.

NEW QUESTION # 201
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