Exam Name: Databricks Certified Data Engineer Professional Exam
Certification Provider: Databricks
Corresponding Certification: Databricks Certification
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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitoring and Alerting | 10% | - Track data lineage and metrics - Monitor pipeline performance and health - Set up alerts and notifications |
| Topic 2: Debugging and Deploying | 10% | - Troubleshoot and debug pipelines - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs |
| Topic 3: Cost & Performance Optimisation | 13% | - Improve query and pipeline performance - Apply cost management best practices - Optimize compute and storage resources |
| Topic 4: Data Modelling | 6% | - Design Medallion Architecture - Implement dimensional and relational models - Optimize table design and partitioning |
| Topic 5: Ensuring Data Security and Compliance | 10% | - Implement access control and permissions - Secure data at rest and in transit - Ensure data privacy and compliance |
| Topic 6: Data Transformation, Cleansing, and Quality | 10% | - Implement schema evolution and management - Enforce data quality standards - Apply data cleansing and validation rules |
| Topic 7: Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| Topic 8: Data Governance | 7% | - Use Unity Catalog for governance - Enforce data policies and standards - Manage data assets and metadata |
| Topic 9: Data Sharing and Federation | 5% | - Implement Lakehouse Federation - Manage cross-platform data access - Use Delta Sharing for secure data sharing |
| Topic 10: Developing Code for Data Processing using Python and SQL | 22% | - Use Databricks-specific libraries and APIs - Implement complex data processing logic - Write efficient and maintainable code |
Databricks Certified Data Engineer Professional Sample Questions:
A data governance team at a large enterprise is improving data discoverability across its organization. The team has hundreds of tables in their Databricks Lakehouse with thousands of columns that lack proper documentation. Many of these tables were created by different teams over several years, with missing context about column meanings and business logic. The data governance team needs to quickly generate comprehensive column descriptions for all existing tables to meet compliance requirements and improve data literacy across the organization. They want to leverage modern capabilities to automatically generate meaningful descriptions rather than manually documenting each column, which would take months to complete. Which approach should the team use in Databricks to automatically generate column comments and descriptions for existing tables?
- A. Navigate to the table in Databricks Catalog Explorer, select the table schema view, and use the AI Generate option which leverages artificial intelligence to automatically create meaningful column descriptions based on column names, data types, sample values, and data patterns.
- B. Use Delta Lake's DESCRIBE HISTORY command to analyze table evolution and infer column purposes from historical changes.
- C. Use the DESCRIBE TABLE command to extract existing schema information and manually write descriptions based on column names and data types.
- D. Write custom PySpark code using df.describe() and df.schema to programmatically generate basic statistical descriptions for each column.
Correct Answer: A 🗳️
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A data pipeline uses Structured Streaming to ingest data from kafka to Delta Lake. Data is being stored in a bronze table, and includes the Kafka_generated timesamp, key, and value. Three months after the pipeline is deployed the data engineering team has noticed some latency issued during certain times of the day.
A senior data engineer updates the Delta Table's schema and ingestion logic to include the current timestamp (as recoded by Apache Spark) as well the Kafka topic and partition. The team plans to use the additional metadata fields to diagnose the transient processing delays.
Which limitation will the team face while diagnosing this problem?
- A. New fields will not be computed for historic records.
- B. Spark cannot capture the topic partition fields from the kafka source.
- C. Updating the table schema requires a default value provided for each file added.
- D. New fields cannot be added to a production Delta table.
- E. Updating the table schema will invalidate the Delta transaction log metadata.
Correct Answer: A 🗳️
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A data team is implementing an append-only Delta Lake pipeline that processes both batch and streaming data. They want to ensure that schema changes in the source data are automatically incorporated without breaking the pipeline. Which configuration should the team use when writing data to the Delta table?
- A. ignoreChanges = false
- B. mergeSchema = true
- C. validateSchema = false
- D. overwriteSchema = true
Correct Answer: B 🗳️
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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. Use global Python variables to make expectations visible across DLT notebooks included in the same pipeline.
- B. Add data quality constraints to tables in this pipeline using an external job with access to pipeline configuration files.
- C. Maintain data quality rules in a Delta table outside of this pipeline's target schema, providing the schema name as a pipeline parameter.
- D. Maintain data quality rules in a separate Databricks notebook that each DLT notebook of file.
Correct Answer: C 🗳️
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A data engineer is building a streaming data pipeline to ingest JSON files from cloud storage into a Delta Lake table. The pipeline must process files incrementally, handle schema evolution automatically, ensure exactly-once processing, and minimize manual infrastructure management.
How should the data engineer fulfill these requirements?
- A. Use Lakeflow Spart Declarative Pipelines with Auto Loader and enabling schema inference with
"cloudFiles.schemaEvolutionMode"= "addNewColumns" - B. Use traditional Spark Structured Streaming with Auto Loader, manually configuring checkpoints location and enabling schema inference with "mergeSchema"= "true"
- C. Use Lakeflow Spark Declarative Pipelines with a static DataFrame read, merge schema with spark.conf.set ("spark.databricks.delta.schema.autoMerge.enabled", "true")
- D. Use Auto Loader in batch mode with a daily job to overwrite the Delta table.
Correct Answer: A 🗳️
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