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DP-700 Fabric Engineer Hub

Exam DP-700: Implementing Data Engineering Solutions using Microsoft Fabric

Exam DP-700: Implementing Data Engineering Solutions using Microsoft Fabric

Source

Scheduled Appointment

  • Exam Date: Tuesday, September 29, 2026
  • Exam Time: 11:00 AM Eastern European Summer Time (EEST)
  • Delivery Mode: Pearson VUE OnVUE (Online Proctored)
  • Candidate Name: Panagiotis Koletsos
  • Microsoft Candidate ID (MCID): MS1101476093
  • Registration ID: 544891069
  • Order Number: 0080-9235-2037
  • Approved Accommodations: Receives 30 minutes of additional time (ESL Accommodation active)
  • Total Appointment Length: 120 Minutes
  • System Test: Pearson VUE OnVUE System Test
  • Reschedule / Cancellation Policy: Must be performed at least 24 hours prior to appointment time.

Overview

Exam DP-700 tests candidate ability to accomplish the technical tasks of designing, building, securing, monitoring, and optimizing robust data engineering pipelines in Microsoft Fabric.

As an associate data engineer, candidates are responsible for ingesting, loading, transforming, securing, monitoring, and optimizing analytics workloads across Fabric Lakehouses, Data Warehouses, Real-Time Intelligence (Eventstreams & KQL databases), Data Factory pipelines, and Dataflows Gen2.

Skills Measured (Objective Domain Breakdown)

Domain 1: Plan, Implement, and Manage Data Engineering Solutions (30–35%)

  • Plan and configure Fabric workspace and items:
    • Configure workspace settings, Spark compute pools, and custom environment libraries.
    • Choose between Lakehouse and Warehouse architectures for specific analytical workloads.
    • Implement and manage OneLake shortcuts (ADLS Gen2, Amazon S3, Google Cloud Storage, Dataverse).
    • Manage lifecycle management (Git integration, deployment pipelines, Airflow workspaces/jobs).
  • Plan and implement data security and governance:
    • Configure workspace roles (Admin, Member, Contributor, Viewer) and item permissions (Read, ReadAll, Write, Reshare).
    • Implement data masking, Row-Level Security (RLS), Column-Level Security (CLS), and Object-Level Security (OLS).
    • Apply Microsoft Purview Information Protection sensitivity labels and data discovery policies.
    • Configure managed private endpoints and secure data exfiltration controls.

Domain 2: Ingest and Transform Data (30–35%)

  • Design and implement data ingestion patterns:
    • Implement batch ingestion and Change Data Capture (CDC) pipelines using Data Factory Copy and Lookup activities.
    • Ingest and stage structured and unstructured data using Dataflows Gen2 with fast copy enabled.
    • Configure Real-Time Intelligence Eventstreams with routing, transformations, and destinations.
    • Implement database mirroring (Azure SQL, Azure Cosmos DB, Snowflake) into OneLake.
  • Transform data using Apache Spark and PySpark:
    • Design Medallion architecture (Bronze, Silver, Gold) across Lakehouses.
    • Transform data with PySpark DataFrames, Spark SQL, and Delta Lake APIs.
    • Implement PySpark Structured Streaming with sliding/tumbling windows and watermarking.
    • Optimize Delta tables with OPTIMIZE, V-ORDER, VACUUM, and liquid clustering.
  • Transform data using SQL in Fabric Warehouse & Lakehouse:
    • Author stored procedures, user-defined functions (UDFs), and views.
    • Implement Slowly Changing Dimensions (SCD Type 1 & Type 2) and surrogate keys using MERGE.
    • Execute cross-database queries between Lakehouse SQL analytics endpoints and Warehouses.
  • Transform data using KQL in Real-Time Intelligence:
    • Author Kusto Query Language (KQL) queries for time-series and log analytics.
    • Implement KQL update policies and materialized views for streaming data transformation.

Domain 3: Monitor and Optimize Data Engineering Solutions (30–35%)

  • Monitor Fabric items and workloads:
    • Track compute unit (CU) consumption and background vs. interactive operations using the Microsoft Fabric Capacity Metrics app.
    • Monitor Spark job execution, DAGs, and executor logs via Spark Application UI.
    • Monitor pipeline execution runs and Dataflow refresh history in the Monitoring Hub.
    • Inspect query execution plans and query insights in Fabric Warehouse.
  • Optimize performance and resolve issues:
    • Optimize Spark jobs by tuning partitions, broadcast joins, caching, and executor memory allocation.
    • Resolve pipeline failures, throttling alerts, and capacity burndown spikes.
    • Tune Lakehouse SQL analytics endpoints and Warehouse queries with statistics and columnstore indexing.

Study & Resource Links