1. Pipeline Deployment and Orchestration
The “Deploy and maintain data pipelines and workloads” section of exam DP-750 focuses on a data engineer’s ability to operationalize, automate, and manage enterprise data solutions within Azure Databricks. Candidates should understand how to deploy notebooks, workflows, jobs, and pipelines into production environments using scalable and repeatable processes. The exam emphasizes orchestration techniques for coordinating multiple tasks, dependencies, and schedules across complex data engineering workloads. Candidates should understand job scheduling, task dependencies, retry policies, notifications, and parameterized workflows. Knowledge of Databricks Workflows, Delta Live Tables, and orchestration integration with external tools is also important for building reliable enterprise-grade solutions.
2. CI/CD, Version Control, and Environment Management
A major focus area is implementing DevOps practices and continuous integration and continuous deployment processes. Candidates should understand how to integrate Azure Databricks with Git-based repositories for source control, collaboration, and version management. This includes managing notebooks, configuration files, libraries, and deployment artifacts across development, test, and production environments. The exam also covers Databricks Asset Bundles and deployment automation techniques used to validate and promote workloads consistently between environments. Candidates should understand environment-specific configuration management, dependency handling, rollback strategies, and testing approaches to reduce deployment risks and improve operational stability.
3. Monitoring, Troubleshooting, and Performance Optimization
The exam expects candidates to understand how to monitor and maintain data pipelines and workloads after deployment. This includes configuring logging, audit monitoring, alerts, query history, and diagnostic settings using Azure Monitor, Log Analytics, and built-in Databricks monitoring tools. Candidates should understand how to use Spark UI to investigate job execution, identify bottlenecks, and troubleshoot issues related to skew, shuffle, spill, partitioning, or resource contention. Knowledge of workload optimization is important, including autoscaling, cluster sizing, caching, Photon acceleration, and query optimization techniques. Candidates should also understand how to improve reliability, scalability, fault tolerance, and cost efficiency for production workloads.
4. Reliability, Security, and Operational Governance
Candidates should understand operational best practices for maintaining secure and dependable data engineering solutions. This includes managing permissions, securing secrets using Azure Key Vault or managed identities, and applying governance policies through Unity Catalog. The exam also emphasizes designing resilient pipelines that support recovery from failures, incremental processing, checkpointing, and restartability. Candidates should understand service-level reliability concepts, operational governance, and lifecycle management for long-running workloads. Integration with streaming solutions, machine learning workloads, Power BI, and external systems is also important. Overall, this section of DP-750 validates that a candidate can deploy, monitor, secure, optimize, and maintain enterprise-scale Azure Databricks pipelines and workloads using modern DevOps and operational engineering practices.
Links
Microsoft Certified: Azure Databricks Data Engineer Associate – Certifications | Microsoft Learn