1. Data Ingestion and Preparation
The “Prepare and process data” section of exam DP-750 focuses on a data engineer’s ability to ingest, clean, transform, and prepare data for analytics and AI workloads within Azure Databricks. Candidates should understand how Azure Databricks integrates with cloud storage systems such as Azure Data Lake Storage Gen2 and external data sources including relational databases, APIs, streaming systems, and flat files. Knowledge of batch and streaming ingestion techniques is important. Candidates are expected to understand common ingestion tools and frameworks including Auto Loader, structured streaming, and Delta Lake. Data engineers should also know how to handle schema inference, schema evolution, corrupt records, and incremental data loading patterns efficiently within enterprise environments.
2. Data Transformation and Spark Processing
A major focus area is transforming and processing data using Apache Spark within Azure Databricks. Candidates must understand how to use Spark DataFrames, Spark SQL, notebooks, and PySpark for scalable distributed processing. The exam expects knowledge of filtering, aggregating, joining, sorting, window functions, and handling nested or semi-structured data such as JSON and Parquet. Candidates should understand how Spark executes transformations across distributed clusters and how partitioning affects performance. Important optimization concepts include caching, skew handling, shuffle management, spill behavior, and query optimization. Knowledge of Delta Lake operations such as MERGE, OPTIMIZE, VACUUM, and Z-ORDER is also important for maintaining performant and reliable lakehouse tables.
3. Pipeline Development and Data Quality
Candidates are expected to understand how to build reliable and reusable data pipelines within Azure Databricks. This includes orchestrating notebooks, configuring workflows, and developing scalable ETL and ELT solutions. The exam covers incremental processing patterns, watermarking strategies, and change data capture approaches for handling large enterprise datasets efficiently. Data quality is another important topic. Candidates should understand validation techniques, schema enforcement, expectations, and error handling approaches to ensure trustworthy and consistent data processing. Knowledge of medallion architecture principles, including Bronze, Silver, and Gold layers, is important for organizing and refining data through progressive transformation stages.
4. Streaming, Integration, and Production Readiness
The exam also focuses on real-time and production-ready data processing capabilities. Candidates should understand structured streaming concepts including triggers, checkpoints, exactly-once processing, and stateful operations. Integration with external systems such as Power BI, SQL warehouses, machine learning workloads, and cloud-native services is also important. Candidates should understand how to monitor and troubleshoot pipelines using Spark UI, logs, and query history. Knowledge of workload optimization, scalability, fault tolerance, and operational best practices is expected. Overall, this section of DP-750 validates that a candidate can prepare, process, optimize, and manage enterprise-scale data pipelines within Azure Databricks using secure, scalable, and governed engineering practices.
Links
Microsoft Certified: Azure Databricks Data Engineer Associate – Certifications | Microsoft Learn