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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Monitoring and Alerting | 10% | - Set up alerts and notifications - Track data lineage and metrics - Monitor pipeline performance and health |
| Ensuring Data Security and Compliance | 10% | - Ensure data privacy and compliance - Implement access control and permissions - Secure data at rest and in transit |
| Data Ingestion & Acquisition | 7% | - Ingest data from diverse sources - Use Auto Loader and structured streaming - Handle incremental and batch data loads |
| 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 |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Data Governance | 7% | - Manage data assets and metadata - Use Unity Catalog for governance - Enforce data policies and standards |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Manage cross-platform data access - Implement Lakehouse Federation |
| Data Transformation, Cleansing, and Quality | 10% | - Apply data cleansing and validation rules - Enforce data quality standards - Implement schema evolution and management |
| Debugging and Deploying | 10% | - Implement CI/CD and DevOps practices - Deploy using Asset Bundles, CLI, and APIs - Troubleshoot and debug pipelines |
| Data Modelling | 6% | - Implement dimensional and relational models - Design Medallion Architecture - Optimize table design and partitioning |
Databricks Certified Data Engineer Professional Sample Questions:
1. Which statement characterizes the general programming model used by Spark Structured Streaming?
A) Structured Streaming leverages the parallel processing of GPUs to achieve highly parallel data throughput.
B) Structured Streaming uses specialized hardware and I/O streams to achieve sub-second latency for data transfer.
C) Structured Streaming relies on a distributed network of nodes that hold incremental state values for cached stages.
D) Structured Streaming models new data arriving in a data stream as new rows appended to an unbounded table.
E) Structured Streaming is implemented as a messaging bus and is derived from Apache Kafka.
2. A data company uses Databricks Unity Catalog and has multiple enterprise data sources, including PostgreSQL, Snowflake, and SQL Server. The central data platform team wants to configure Lakehouse Federation so analysts can query external tables directly in Databricks using Databricks SQL, without duplicating data. Which steps are necessary to configure Lakehouse Federation in a secure and governed manner?
A) Use Partner Connect to create linked datasets, and apply table ACLs at the source system to govern access through Databricks.
B) Create external locations and storage credentials to connect to each database, then register foreign tables in Unity Catalog.
C) Mirror the external datasets into Delta Lake using Auto Loader, and govern them using Data Lineage and System Tables.
D) Configure connections and foreign catalog in Unity Catalog, then grant access to foreign catalogs, schemas, and tables using Unity Catalog permissions.
3. The DevOps team has configured a production workload as a collection of notebooks scheduled to run daily using the Jobs Ul. A new data engineering hire is onboarding to the team and has requested access to one of these notebooks to review the production logic. What are the maximum notebook permissions that can be granted to the user without allowing accidental changes to production code or data?
A) Can edit
B) Can run
C) Can manage
D) Can Read
4. A data engineer is working in an interactive notebook with many transformations before outputting the result from display(df.collect() ). The notebook includes wide transformations and a cross join.
The data engineer is getting the following error: "The spark driver has stopped unexpectedly and is restarting. Your notebook will be automatically reattached." Which action should the data engineer take?
A) Look at the compute metrics UI to see if the executors have higher than 90% memory utilization.
B) Rewrite their code to avoid putting memory pressure on the driver node.
C) Run the notebook on a single node cluster to keep driver from falling.
D) Check into the Spark UI to see how many jobs are assigned to each stage as they are employing fewer executors.
5. Which configuration parameter directly affects the size of a spark-partition upon ingestion of data into Spark?
A) spark.sql.files.openCostInBytes
B) spark.sql.adaptive.coalescePartitions.minPartitionNum
C) spark.sql.files.maxPartitionBytes
D) spark.sql.adaptive.advisoryPartitionSizeInBytes
E) spark.sql.autoBroadcastJoinThreshold
Solutions:
| Question # 1 Answer: D | Question # 2 Answer: D | Question # 3 Answer: D | Question # 4 Answer: B | Question # 5 Answer: C |
Sabina 
Michelle 
Adonis 
Stanford 
Ashbur 
Ada 
Webster 
Michell 
Beulah 
Christine 
Ira 
Myra 
Hilary 
Kelly 
Darren 
Lauren 
