JITA Learning · Microsoft · Field Guide
DP-700 · Implementing Data Engineering Solutions Using Microsoft Fabric
Build working judgment for Microsoft Fabric data engineering.
What you should expect
Build working judgment for Microsoft Fabric data engineering.
This Field Guide develops a working mental model of Microsoft Fabric data engineering. You will connect choose between pipelines, Dataflows Gen2, notebooks, Spark, KQL, and T-SQL for the engineering task, design ingestion and transformation across batch, streaming, and real-time workloads, build lakehouse and OneLake structures with appropriate shortcut and table choices, and configure workspace, domain, Spark, Airflow, and OneLake settings. You are not just memorizing feature names; you are learning what owns the requirement, what can fail, and how to verify the outcome.
What you will learn
- choose between pipelines, Dataflows Gen2, notebooks, Spark, KQL, and T-SQL for the engineering task
- design ingestion and transformation across batch, streaming, and real-time workloads
- build lakehouse and OneLake structures with appropriate shortcut and table choices
- configure workspace, domain, Spark, Airflow, and OneLake settings
- recognize why one technically plausible alternative loses under the stated requirement
- apply Changed Fact reasoning and verify the design or implementation through operational evidence
Decision skills you will practice
- identify the governing requirement
- compare the nearest plausible alternative
- apply Changed Fact reasoning
- trace prerequisites, dependencies, and failure paths
- verify configured state, effective state, runtime outcome, and negative-path evidence
Who this is for
data engineers building ingestion, transformation, orchestration, lakehouse, and real-time data solutions in Fabric
Use this when you need to learn the architecture, mechanics, dependencies, tradeoffs, failure paths, and verification logic behind the certification.
Decisions you will learn to distinguish
- pipeline vs notebook vs Dataflow Gen2
- batch/lakehouse path vs real-time intelligence path
- OneLake shortcut choice vs native table or query-acceleration requirement
Why JITA is different
Microsoft explains the platform. JITA teaches the decision.
Microsoft Learn explains the services, features, and documented behavior that support Microsoft Fabric data engineering. JITA connects those capabilities into decisions: what owns the requirement, what the nearest alternative is, what changes when one fact changes, and what evidence proves the implementation worked.
How to choose:
Use the Field Guide to build understanding. Use the Exam Preparation Guide to test whether that understanding survives exam-style decisions.