DP-700 - 6-Week Practice Plan¶

This plan assumes 1-2 hours of theory + 1-2 hours of hands-on lab per day.

Lab setup¶

• Activate the Microsoft Fabric Free Trial (60-day F64 trial capacity)
• Create a workspace; set up a sample dataset (NYC Taxi, World Bank, etc.)
• Connect the Capacity Metrics App
• Have access to a free Cosmos DB / Snowflake / SQL DB account if you want to try Mirroring

Week 1 - Fabric Architecture and Workspaces¶

Reading¶

• README.md full read
• fact-sheet.md skim
• notes/01-fabric-architecture-and-workspaces.md
• Microsoft Learn: "Get started with Microsoft Fabric"

Hands-on¶

• Create a workspace; assign trial capacity
• Explore the workspace: create a Lakehouse, Warehouse, Notebook
• Navigate the Monitoring Hub

Self-check¶

• Can I describe how OneLake relates to Lakehouses and Warehouses?
• Can I explain capacity vs workspace vs items?

Week 2 - Ingestion Patterns¶

Reading¶

• notes/02-ingestion-pipelines-eventstream.md

Hands-on¶

• Build a Pipeline: Copy Data from public CSV → Lakehouse Files → Notebook → Lakehouse Table
• Build a Dataflow Gen2 to clean and reshape some data into a Warehouse
• Configure an Eventstream from sample data → Lakehouse + KQL DB
• (If you have a free Cosmos DB) Set up Mirroring; observe the replication

Self-check¶

• Can I pick the right ingestion pattern for a given prompt?
• Do I know Dataflow Gen2 vs Notebook tradeoffs?

Week 3 - Lakehouse and Warehouse¶

Reading¶

• notes/03-lakehouse-warehouse-notebooks.md

Hands-on¶

• Notebook: read CSV, transform, MERGE upsert into Delta table
• OPTIMIZE + VORDER + ZORDER on the table
• Time-travel a table back 1 day
• In a Warehouse: cross-Lakehouse query with 3-part naming
• Build a Power BI report on top using Direct Lake mode
• Compare query latency: Import vs DirectQuery vs Direct Lake

Self-check¶

• Can I write a basic PySpark transform end-to-end?
• Do I know when to use Lakehouse vs Warehouse?

Week 4 - Security, Governance, Deployment¶

Reading¶

• notes/04-security-governance-deployment.md

Hands-on¶

• Create a Dev / Test / Prod workspace pair; set up a Deployment Pipeline
• Add a deployment rule to swap a data source between environments
• Connect the Dev workspace to a Git repo (Azure DevOps free or GitHub free)
• Commit changes from the workspace UI; PR to main
• Apply sensitivity labels to a Lakehouse and a semantic model

Self-check¶

• Can I promote items Dev → Prod with parameter swaps?
• Do I know the difference between workspace roles and item permissions?

Week 5 - Monitoring, Optimization, Real-time¶

Reading¶

• notes/05-monitoring-optimization.md
• Microsoft Learn: Real-Time Intelligence in Fabric

Hands-on¶

• Connect Capacity Metrics App; review yesterday's CU consumption
• Run a slow Spark job; use Spark UI to diagnose
• Build a Reflex item that triggers on a stream condition
• Configure pipeline failure alerts via email

Self-check¶

• Can I find which item is consuming the most CUs?
• Can I diagnose a slow Direct Lake query? (V-Order, OPTIMIZE, partition)

Week 6 - Practice and Review¶

Reading¶

• Re-read fact-sheet.md cover to cover
• Work through scenarios.md

Practice exams¶

• Microsoft Learn DP-700 Practice Assessment (free, official)
• resources/practice-questions/ - DP-700 questions if added
• One paid exam vendor (Whizlabs / MeasureUp - if you can wait, MeasureUp is the official partner)
• Score 80%+ before scheduling

Schedule the exam¶

• Book through Pearson VUE or Microsoft Learn

Stop signals (you're ready)¶

• You can build an end-to-end Fabric pipeline (ingest → transform → BI) without notes
• You can pick the right ingestion pattern for any prompt
• You can troubleshoot a slow Spark job using Spark UI
• You can promote items Dev → Prod with parameter swaps
• You score 80%+ on practice exams twice in a row