Exam DP-700: Microsoft Fabric Data Engineer Associate Certification Bootcamp

Instructor: Ashraf Ghonaim · Language: English or Arabic · Suggested duration: 4 days · Level: Advanced Certification Track (Level 300)

Official exam: DP-700 (Implementing Data Engineering Solutions Using Microsoft Fabric)

Objectives

• Master every technical objective tested on the official Microsoft DP-700: Fabric Data Engineer Associate certification exam.
• Configure and secure Fabric data engineering workspaces, Spark pools, environments, CI/CD pipelines, and OneLake governance.
• Design and build both batch (Pipelines, Copy Jobs, Mirroring, Shortcuts, PySpark, T-SQL) and real-time streaming (Eventstreams, Eventhouse, KQL, Activator) ingestion and transformation patterns.
• Monitor, debug, and optimize Spark jobs, Data Warehouses, Pipelines, and Eventhouses for enterprise scale and SLA reliability.

Outline and contents (Mapped to Official Microsoft DP-700 Exam Domains)

1. Domain 1: Implement and Manage an Analytics Solution (30–35%)
   • Configuring Fabric workspace settings: Spark starter/custom pools, high concurrency, runtime versions, custom Environments, and external Python/jar libraries
   • Implementing lifecycle management for engineering items: Git integration, version control, Deployment Pipelines, Variable Libraries, and automating deployments
   • Configuring security and governance: Workspace/item roles, OneLake RBAC, Row-Level Security (RLS), Column-Level Security (CLS), Dynamic Data Masking, and Private Links / Managed Private Endpoints
   • Orchestrating data workflows: designing metadata-driven Data Pipelines, Notebook DAGs (notebookutils.notebook.runMultiple), scheduling, and event triggers
2. Domain 2: Ingest and Transform Data (30–35%)
   • Designing and implementing batch ingestion: Data Pipelines, Copy Jobs (full & incremental), Dataflows Gen2, OneLake Shortcuts, and Near-Real-Time Database Mirroring (Azure SQL, Snowflake, Cosmos DB)
   • Designing and implementing streaming ingestion: Real-Time Hub, Eventstreams (sources, windowing transformations, derived streams, destinations), and Spark Structured Streaming
   • Transforming batch data at scale using PySpark (DataFrames, Spark SQL, Delta Lake MERGE/Upserts, Change Data Feed), T-SQL (COPY INTO, Stored Procedures, Window Functions), and KQL
   • Transforming streaming data using Eventstream processors, KQL update policies, and materialized views in Eventhouse
   • Handling data engineering edge cases: deduplication, missing/late-arriving data, schema drift/evolution, and surrogate key generation
3. Domain 3: Monitor and Optimize an Analytics Solution (30–35%)
   • Monitoring data ingestion and orchestration: Monitoring Hub, Pipeline run diagnostics, retry logic, error handling, and alerting
   • Monitoring and tuning Apache Spark: Spark UI, diagnosing data skew, shuffle spill, broadcast joins, partitioning vs. Liquid Clustering, and Native Execution Engine
   • Monitoring and tuning Fabric Data Warehouse: Query Insights DMVs, query history, locking/blocking analysis, and table statistics
   • Monitoring and tuning Eventhouse / KQL: query performance, caching policies, retention policies, and streaming ingestion health
   • Optimizing Delta Lake table storage: OPTIMIZE, V-Order, VACUUM, and file size management
4. DP-700 Exam Cram & Multi-Engine Code Clinic (Led by Ashraf Ghonaim)
   • Intensive syntax and pattern drills across PySpark, T-SQL, and KQL (Kusto Query Language)
   • Troubleshooting scenarios for Spark out-of-memory errors, pipeline failures, and streaming watermarks
   • Full-length timed DP-700 mock exam simulation and instructor review

Target audience

Data engineers, cloud architects, ETL developers, and BI engineers seeking to earn the Microsoft Certified: Fabric Data Engineer Associate (DP-700) credential.

Expected outcomes

Participants gain deep command over PySpark, T-SQL, KQL, Pipelines, and Real-Time Intelligence in Microsoft Fabric and leave fully prepared to pass the DP-700 exam.