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

Section	Objectives
Data Transformation, Cleansing, and Quality	- Data Quality 1. Apply data quality controls using Lakeflow Spark Declarative Pipelines or Auto Loader 2. Develop data quarantining processes for invalid data - Advanced Data Transformation 1. Apply window functions, joins, and aggregations to large datasets 2. Write efficient Spark SQL and PySpark transformations
Ensuring Data Security and Compliance	- Data Security 1. Apply anonymization and pseudonymization techniques 2. Use ACLs to secure workspace objects and enforce least privilege 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
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
Debugging and Deploying	- Debugging and Troubleshooting 1. Analyze errors and remediate failed job runs 2. Use Spark UI, cluster logs, system tables, and query profiles for diagnostics 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
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. Use Change Data Feed to address streaming table limitations and improve latency 2. Apply data skipping and file pruning techniques 3. Understand deletion vectors and liquid clustering
Data Modelling	- Scalable Data Models 1. Design and implement scalable data models using Delta Lake 2. Understand Liquid Clustering versus partitioning and Z-Ordering 3. Optimize data layout using Liquid Clustering - Dimensional Modelling 1. Design dimensional models for analytical workloads
Developing Code for Data Processing using Python and SQL	- Building and Testing ETL Pipelines 1. Use control flow operators in pipeline components 2. Compare streaming tables and materialized views 3. Create and automate ETL workloads using Jobs through UI, APIs, and CLI 4. Develop unit and integration tests for data processing code 5. Build production-ready batch and streaming pipelines using Lakeflow Spark Declarative Pipelines and Auto Loader 6. Compare Spark Structured Streaming and Lakeflow Spark Declarative Pipelines 7. Use APPLY CHANGES APIs for change data capture 8. Configure environments, dependencies, memory, and retry behavior - Using Python and Tools for Development 1. Manage and troubleshoot third-party library installations and dependencies 2. Design and implement scalable Python project structures optimized for Databricks Asset Bundles 3. Develop User-Defined Functions using Pandas/Python UDFs
Data Sharing and Federation	- Lakehouse Federation 1. Configure Lakehouse Federation with appropriate governance - Delta Sharing 1. Configure sharing with external platforms using the open sharing protocol 2. Configure Databricks-to-Databricks Sharing 3. Share live Lakehouse data with external computing platforms
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
Monitoring and Alerting	- Monitoring 1. Use Query Profiler and Spark UI to monitor workloads 2. Use Lakeflow Spark Declarative Pipelines event logs for monitoring 3. Use system tables for resource, cost, audit, and workload monitoring 4. Use Databricks REST APIs and CLI for monitoring jobs and pipelines - Alerting 1. Configure Lakeflow Jobs notifications for job status and performance issues 2. Use SQL Alerts for data quality monitoring

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

NEW QUESTION # 102
A data engineer is testing a collection of mathematical functions, one of which calculates the area under a curve as described by another function.
assert(myIntegrate(lambda x: x*x, 0, 3) [0] == 9)
Which kind of the test does the above line exemplify?

A. Integration
B. End-to-end
C. Manual
D. Unit
E. functional

Answer: D

Explanation:
A unit test is designed to verify the correctness of a small, isolated piece of code, typically a single function. Testing a mathematical function that calculates the area under a curve is an example of a unit test because it is testing a specific, individual function to ensure it operates as expected.

NEW QUESTION # 103
A data engineer is using Structured Streaming to read in transaction data from a bronze Delta table. It was discovered that the data has quality issues where sometimes the transaction value is negative, and when that occurs, the rows need to be routed to a separate quarantine table. They have low latency requirements for the good data since it is used by downstream systems, but the bad data will only be analyzed periodically and has no production dependencies. The quarantine job needs to be implemented so that it cannot affect the production processes that depend on the good data, and the cost of the job needs to be minimized. How should the quarantine process be implemented in order to satisfy these requirements?

A. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing. Both should run as separate streams on the same cluster to minimize cost.
B. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. Inside a foreachBatch function, the dataframe should be filtered so that records with a transaction value greater than or equal to 0 are written to the good data table and records with a transaction value less than 0 are written to a quarantine table. Try/Catch can be added around the writes in the foreachBatch function so that the stream can't fail.
C. The existing streaming job for the good data should be updated to incorporate the quarantining of the bad data. A new boolean column called "quarantine" should be added to the dataframe, and its value should be set to true if the transaction value is less than 0 and false if the transaction value is greater than or equal to 0. Processing and storing all the data together will save costs.
D. The streaming job for the good data needs to be modified to filter out records with a transaction value less than 0 before writing, and should not share compute with other processes. The streaming job for the quarantine data needs to filter out records with a transaction value greater than or equal to 0 before writing, and should be implemented on a separate small cluster and only run once a day to minimize cost.

Answer: D

Explanation:
Separating the quarantine processing from the low-latency production stream ensures the good- data pipeline cannot be slowed down or destabilized by quarantine writes or failures. Filtering out negative values in the production stream preserves latency and reliability for downstream systems. Running the quarantine stream on a small, independent cluster on a daily schedule minimizes cost while still capturing the bad records for periodic analysis, and it fully isolates quarantine operations from production dependencies.

NEW QUESTION # 104
A team of data engineer are adding tables to a DLT pipeline that contain repetitive expectations for many of the same data quality checks.
One member of the team suggests reusing these data quality rules across all tables defined for this pipeline.
What approach would allow them to do this?

A. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
B. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
C. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
D. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.

Answer: B

Explanation:
Maintaining data quality rules in a centralized Delta table allows for the reuse of these rules across multiple DLT (Delta Live Tables) pipelines. By storing these rules outside the pipeline's target schema and referencing the schema name as a pipeline parameter, the team can apply the same set of data quality checks to different tables within the pipeline. This approach ensures consistency in data quality validations and reduces redundancy in code by not having to replicate the same rules in each DLT notebook or file.

NEW QUESTION # 105
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 a job with at least one wide transformation, which of the following cluster configurations will result in maximum performance?

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

Answer: C

Explanation:
https://docs.databricks.com/en/clusters/cluster-config-best-practices.html

NEW QUESTION # 106
A data engineering team needs to implement a tagging system for their tables as part of an automated ETL process, and needs to apply tags programmatically to tables in Unity Catalog.
Which SQL command adds tags to a table programmatically?

A. ALTER TABLE table_name SET TAGS ('key1' = 'value1', 'key2' = 'value2');
B. SET TAGS FOR table_name AS ('key1' = 'value1', 'key2' = 'value2');
C. COMMENT ON TABLE table_name TAGS ('key1' = 'value1', 'key2' = 'value2');
D. APPLY TAGS ON table_name VALUES ('key1' = 'value1', 'key2' = 'value2');

Answer: A

Explanation:
Unity Catalog supports programmatic tagging through the ALTER TABLE statement. Using SET TAGS allows tags to be added or updated directly on a table as part of automated ETL workflows, making it the correct and supported SQL command for managing table tags.

NEW QUESTION # 107
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