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

Section	Objectives
Topic 1: Cost & Performance Optimisation	- Cost Optimization 1. Understand how Unity Catalog managed tables reduce operational overhead - Delta Optimization 1. Use Change Data Feed to address streaming table limitations and improve latency 2. Understand deletion vectors and liquid clustering 3. Apply data skipping and file pruning techniques - Query Performance 1. Identify inefficient joins and excessive data shuffling 2. Use Query Profile to identify performance bottlenecks
Topic 2: Data Ingestion & Acquisition	- Design and implement data ingestion pipelines 1. Ingest data from message buses and cloud storage 2. Ingest Delta Lake, Parquet, ORC, Avro, JSON, CSV, XML, Text, and Binary data 3. Build append-only pipelines for batch and streaming data using Delta
Topic 3: Data Governance	- Unity Catalog Permissions 1. Understand the Unity Catalog permission inheritance model - Metadata and Discoverability 1. Create and maintain descriptions and metadata for enterprise data
Topic 4: Monitoring and Alerting	- Alerting 1. Use SQL Alerts for data quality monitoring 2. Configure Lakeflow Jobs notifications for job status and performance issues - Monitoring 1. Use Databricks REST APIs and CLI for monitoring jobs and pipelines 2. Use Query Profiler and Spark UI to monitor workloads 3. Use system tables for resource, cost, audit, and workload monitoring 4. Use Lakeflow Spark Declarative Pipelines event logs for monitoring
Topic 5: Data Modelling	- Dimensional Modelling 1. Design dimensional models for analytical workloads - 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
Topic 6: Ensuring Data Security and Compliance	- Data Security 1. Use row filters and column masks for sensitive data 2. Apply anonymization and pseudonymization techniques 3. Use ACLs to secure workspace objects and enforce least privilege - Compliance 1. Develop data purging solutions according to data retention policies 2. Implement pipelines that detect and mask personally identifiable information
Topic 7: 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
Topic 8: 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
Topic 9: 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
Topic 10: 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. Develop User-Defined Functions using Pandas/Python UDFs 3. Manage and troubleshoot third-party library installations and dependencies - Building and Testing ETL Pipelines 1. Develop unit and integration tests for data processing code 2. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 3. Configure environments, dependencies, memory, and retry behavior 4. Compare streaming tables and materialized views 5. Use control flow operators in pipeline components 6. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 7. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 8. Use APPLY CHANGES APIs for change data capture

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Databricks Certified Data Engineer Professional Sample Questions (Q102-Q107):

NEW QUESTION # 102
A facilities-monitoring team is building a near-real-time PowerBI dashboard off the Delta table device_readings:
Columns:
device_id (STRING, unique sensor ID)
event_ts (TIMESTAMP, ingestion timestamp UTC)
temperature_c (DOUBLE, temperature in °C)
Requirement:
For each sensor, generate one row per non-overlapping 5-minute
interval, offset by 2 minutes (e.g., 00:02-00:07, 00:07-00:12, ...).
Each row must include interval start, interval end, and average
temperature in that slice.
Downstream BI tools (e.g., Power BI) must use the interval timestamps
to plot time-series bars.

A. SELECT device_id,
window.start AS bucket_start,
window.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM device_readings
GROUP BY device_id, window(event_ts, '5 minutes', '5 minutes', '2 minutes') ORDER BY device_id, bucket_start;
B. WITH buckets AS (
SELECT device_id,
window(event_ts, '5 minutes', '2 minutes', '5 minutes') AS win,
temperature_c
FROM device_readings
)
SELECT device_id,
win.start AS bucket_start,
win.end AS bucket_end,
AVG(temperature_c) AS avg_temp_5m
FROM buckets
GROUP BY device_id, win
ORDER BY device_id, bucket_start;
C. SELECT device_id,
date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 2 MINUTES AS bucket_start, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) + INTERVAL 7 MINUTES AS bucket_end, AVG(temperature_c) AS avg_temp_5m FROM device_readings GROUP BY device_id, date_trunc('minute', event_ts - INTERVAL 2 MINUTES) ORDER BY device_id, bucket_start;
D. SELECT device_id,
event_ts,
AVG(temperature_c) OVER (
PARTITION BY device_id
ORDER BY event_ts
RANGE BETWEEN INTERVAL 5 MINUTES PRECEDING AND CURRENT ROW
) AS avg_temp_5m
FROM device_readings
WINDOW w AS (window(event_ts, '5 minutes', '2 minutes'));

Answer: B

Explanation:
The correct way to satisfy non-overlapping windows with an offset in Databricks SQL is to use the window function with three parameters: window duration, slide duration, and start offset.
In option A, the function call:
window(event_ts, '5 minutes', '2 minutes', '5 minutes')
creates 5-minute windows that slide every 5 minutes, with a 2-minute offset, which exactly matches the requirement (intervals like 00:02?0:07, 00:07?0:12, ...).

NEW QUESTION # 103
A distributed team of data analysts share computing resources on an interactive cluster with autoscaling configured. In order to better manage costs and query throughput, the workspace administrator is hoping to evaluate whether cluster upscaling is caused by many concurrent users or resource-intensive queries.
In which location can one review the timeline for cluster resizing events?

A. Workspace audit logs
B. Ganglia
C. Executor's log file
D. Cluster Event Log
E. Driver's log file

Answer: D

Explanation:
The Cluster Event Log in Databricks will show the timeline for cluster resizing events, including details about when and why a cluster was resized (scaled up or down). This log would help the workspace administrator determine the causes of cluster scaling, whether due to many concurrent users submitting jobs or a few users running resource-intensive queries.

NEW QUESTION # 104
The data engineering team is migrating an enterprise system with thousands of tables and views into the Lakehouse. They plan to implement the target architecture using a series of bronze, silver, and gold tables. Bronze tables will almost exclusively be used by production data engineering workloads, while silver tables will be used to support both data engineering and machine learning workloads. Gold tables will largely serve business intelligence and reporting purposes. While personal identifying information (PII) exists in all tiers of data, pseudonymization and anonymization rules are in place for all data at the silver and gold levels.
The organization is interested in reducing security concerns while maximizing the ability to collaborate across diverse teams.
Which statement exemplifies best practices for implementing this system?

A. Because all tables must live in the same storage containers used for the database they're created in, organizations should be prepared to create between dozens and thousands of databases depending on their data isolation requirements.
B. Working in the default Databricks database provides the greatest security when working with managed tables, as these will be created in the DBFS root.
C. Isolating tables in separate databases based on data quality tiers allows for easy permissions management through database ACLs and allows physical separation of default storage locations for managed tables.
D. Because databases on Databricks are merely a logical construct, choices around database organization do not impact security or discoverability in the Lakehouse.
E. Storinq all production tables in a single database provides a unified view of all data assets available throughout the Lakehouse, simplifying discoverability by granting all users view privileges on this database.

Answer: C

Explanation:
This is the correct answer because it exemplifies best practices for implementing this system. By isolating tables in separate databases based on data quality tiers, such as bronze, silver, and gold, the data engineering team can achieve several benefits. First, they can easily manage permissions for different users and groups through database ACLs, which allow granting or revoking access to databases, tables, or views. Second, they can physically separate the default storage locations for managed tables in each database, which can improve performance and reduce costs. Third, they can provide a clear and consistent naming convention for the tables in each database, which can improve discoverability and usability.

NEW QUESTION # 105
The business intelligence team has a dashboard configured to track various summary metrics for retail stories. This includes total sales for the previous day alongside totals and averages for a variety of time periods. The fields required to populate this dashboard have the following schema:

For Demand forecasting, the Lakehouse contains a validated table of all itemized sales updated incrementally in near real-time. This table named products_per_order, includes the following fields:

Because reporting on long-term sales trends is less volatile, analysts using the new dashboard only require data to be refreshed once daily. Because the dashboard will be queried interactively by many users throughout a normal business day, it should return results quickly and reduce total compute associated with each materialization.
Which solution meets the expectations of the end users while controlling and limiting possible costs?

A. Populate the dashboard by configuring a nightly batch job to save the required values as a table overwritten with each update.
B. Use Structure Streaming to configure a live dashboard against the products_per_order table within a Databricks notebook.
C. Use the Delta Cache to persists the products_per_order table in memory to quickly the dashboard with each query.
D. Define a view against the products_per_order table and define the dashboard against this view.
E. Configure a webhook to execute an incremental read against products_per_order each time the dashboard is refreshed.

Answer: A

NEW QUESTION # 106
A data engineer has configured their Databricks Asset Bundle with multiple targets in databricks.yml and deployed it to the production workspace. Now, to validate the deployment, they need to invoke a job named my_project_job specifically within the prod target context.
Assuming the job is already deployed, they need to trigger its execution while ensuring the target- specific configuration is respected. Which command will trigger the job execution?

A. databricks bundle run my_project_job -t prod
B. databricks execute my_project_job -e prod
C. databricks run my_project_job -t prod
D. databricks job run my_project_job --env prod

Answer: A

Explanation:
Databricks Asset Bundles (DABs) enable declarative configuration and deployment of Databricks resources such as jobs, pipelines, and dashboards across multiple environments.
Once deployed, jobs can be executed in a specific target context using the databricks bundle run command, which ensures all environment-specific configurations from the bundle definition (such as parameters, cluster settings, and workspace URLs) are respected.
The -t flag specifies the target environment (e.g., dev, staging, or prod). This ensures that the execution runs with the correct configuration defined under that target in databricks.yml.
Other options (A, B, and C) are invalid because they reference deprecated or incorrect command syntax that doesn't integrate with bundle targets. Therefore, D is the correct and verified answer.

NEW QUESTION # 107
......

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