Databricks-Certified-Professional-Data-Engineer

The Databricks Certified Professional Data Engineer exam is designed to test your ability to design, build, and maintain data pipelines and solutions using Databricks platform. Here are the main points covered by the exam:

1. Databricks Fundamentals:

• Understanding Databricks Architecture: Knowledge of Databricks architecture, including clusters, workspaces, and key components like Delta Lake, MLflow, and Databricks SQL.
• Databricks Workspace Management: Managing Databricks workspaces, including access control, user management, and workspace settings.
• Databricks Cluster Management: Creating, configuring, and managing Databricks clusters with appropriate resources for different workloads.
• Data Exploration and Analysis: Using SQL and Python to analyze data in Databricks.
• Data Ingestion: Understanding various data ingestion methods, including file formats, connectors, and data sources.

2. Databricks Data Engineering:

• Data Pipelines: Designing, building, and optimizing data pipelines using Databricks tools and techniques.
• Data Transformation: Applying transformations to data using Spark SQL, Python UDFs, and other data manipulation techniques.
• Data Quality and Validation: Implementing data quality checks and validation mechanisms to ensure data integrity.
• Data Governance and Security: Understanding and implementing data governance policies and security measures within Databricks.
• Data Lineage and Tracking: Recognizing the importance of data lineage and tracking data transformations.

3. Delta Lake:

• Understanding Delta Lake: The core concepts of Delta Lake, including its advantages over traditional data lakes, ACID properties, and schema evolution.
• Delta Lake Operations: Using Delta Lake commands for data manipulation, such as append, merge, and update.
• Time Travel and Versioning: Understanding Delta Lake's time travel capabilities and using them for data recovery and auditing.

4. Advanced Databricks Concepts:

• Machine Learning on Databricks: Using Databricks for machine learning tasks like classification, regression, and clustering.
• Databricks SQL and DLT: Using Databricks SQL for data analysis and DLT for data modeling and pipelines.
• Data Visualization and Reporting: Using tools like Databricks SQL and other visualization libraries to generate reports and dashboards.
• Integration with Other Tools: Understanding how to integrate Databricks with other tools and services.

5. Performance Optimization and Troubleshooting:

• Performance Tuning: Identifying and resolving performance bottlenecks in data pipelines and queries.
• Troubleshooting Databricks Issues: Diagnosing and resolving common Databricks issues.
• Monitoring and Logging: Implementing monitoring and logging mechanisms to track data pipeline health.

Preparation Tips:

• Databricks Learning Path: Complete the official Databricks Learning Path for Data Engineers.
• Hands-on Practice: Work on Databricks projects and practice using all the features and concepts.
• Exam Study Guides: Use Databricks study guides and practice exams available online.
• Databricks Community: Engage with the Databricks community forum and resources for support and knowledge

Remember that the exam content and focus may vary depending on the specific exam version. It's crucial to refer to the official Databricks documentation and study guides for the latest exam information.