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Databricks Databricks-Certified-Data-Engineer-Professional Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion & Acquisition | 7% | - Handle incremental and batch data loads - Use Auto Loader and structured streaming - Ingest data from diverse sources |
| Data Governance | 7% | - Use Unity Catalog for governance - Manage data assets and metadata - Enforce data policies and standards |
| Monitoring and Alerting | 10% | - Monitor pipeline performance and health - Track data lineage and metrics - Set up alerts and notifications |
| Ensuring Data Security and Compliance | 10% | - Secure data at rest and in transit - Ensure data privacy and compliance - Implement access control and permissions |
| Data Sharing and Federation | 5% | - Use Delta Sharing for secure data sharing - Implement Lakehouse Federation - Manage cross-platform data access |
| Developing Code for Data Processing using Python and SQL | 22% | - Write efficient and maintainable code - Use Databricks-specific libraries and APIs - Implement complex data processing logic |
| Cost & Performance Optimisation | 13% | - Apply cost management best practices - Optimize compute and storage resources - Improve query and pipeline performance |
| Data Transformation, Cleansing, and Quality | 10% | - Enforce data quality standards - Apply data cleansing and validation rules - Implement schema evolution and management |
| Debugging and Deploying | 10% | - Deploy using Asset Bundles, CLI, and APIs - Implement CI/CD and DevOps practices - Troubleshoot and debug pipelines |
| Data Modelling | 6% | - Optimize table design and partitioning - Implement dimensional and relational models - Design Medallion Architecture |
Databricks Certified Data Engineer Professional Sample Questions:
1. A Databricks job has been configured with 3 tasks, each of which is a Databricks notebook. Task A does not depend on other tasks. Tasks B and C run in parallel, with each having a serial dependency on task A.
If tasks A and B complete successfully but task C fails during a scheduled run, which statement describes the resulting state?
A) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; some operations in task C may have completed successfully.
B) Because all tasks are managed as a dependency graph, no changes will be committed to the Lakehouse until ail tasks have successfully been completed.
C) All logic expressed in the notebook associated with task A will have been successfully completed; tasks B and C will not commit any changes because of stage failure.
D) All logic expressed in the notebook associated with tasks A and B will have been successfully completed; any changes made in task C will be rolled back due to task failure.
E) Unless all tasks complete successfully, no changes will be committed to the Lakehouse; because task C failed, all commits will be rolled back automatically.
2. A data engineer is developing a Lakeflow Declarative Pipeline (LDP) using a Databricks notebook directly connected to their pipeline. After adding new table definitions and transformation logic in their notebook, they want to check for any syntax errors in the pipeline code without actually processing data or running the pipeline. How should the data engineer perform this syntax check?
A) Use the "Validate" option in the notebook to check for syntax errors.
B) Switch to a workspace file instead of a notebook to access validation and diagnostics tools.
C) Open the web terminal from the notebook and run a shell command to validate the pipeline code.
D) Disconnect the notebook from the pipeline and reconnect it to a compute cluster to access code validation features.
3. Which statement describes the default execution mode for Databricks Auto Loader?
A) Webhook trigger Databricks job to run anytime new data arrives in a source directory; new data automatically merged into target tables using rules inferred from the data.
B) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; new files are incrementally and impotently into the target Delta Lake table.
C) New files are identified by listing the input directory; the target table is materialized by directory querying all valid files in the source directory.
D) Cloud vendor-specific queue storage and notification services are configured to track newly arriving files; the target table is materialized by directly querying all valid files in the source directory.
E) New files are identified by listing the input directory; new files are incrementally and idempotently loaded into the target Delta Lake table.
4. Which statement describes a key benefit of an end-to-end test?
A) It makes it easier to automate your test suite
B) It provides testing coverage for all code paths and branches.
C) It closely simulates real world usage of your application.
D) It pinpoint errors in the building blocks of your application.
5. A large company seeks to implement a near real-time solution involving hundreds of pipelines with parallel updates of many tables with extremely high volume and high velocity data.
Which of the following solutions would you implement to achieve this requirement?
A) Store all tables in a single database to ensure that the Databricks Catalyst Metastore can load balance overall throughput.
B) Partition ingestion tables by a small time duration to allow for many data files to be written in parallel.
C) Use Databricks High Concurrency clusters, which leverage optimized cloud storage connections to maximize data throughput.
D) Isolate Delta Lake tables in their own storage containers to avoid API limits imposed by cloud vendors.
E) Configure Databricks to save all data to attached SSD volumes instead of object storage, increasing file I/O significantly.
Solutions:
| Question # 1 Answer: A | Question # 2 Answer: A | Question # 3 Answer: E | Question # 4 Answer: C | Question # 5 Answer: C |
