Review the Microsoft Fabric Data Engineer Associate (DP-700) scope, Fabric data-engineering decisions, ingestion, transformation, monitoring, and optimization traps before practicing in IT Mastery.
DP-700 is about building and operating data-engineering solutions in Microsoft Fabric. Use this cheat sheet to review when to use lakehouses, warehouses, pipelines, notebooks, Dataflows Gen2, semantic models, monitoring, and optimization controls.
Use this with practice. Review the Fabric decision points, then take the free DP-700 diagnostic or open the full IT Mastery practice bank.
| Field | Detail |
|---|---|
| Issuer | Microsoft |
| Certification | Microsoft Certified: Fabric Data Engineer Associate |
| Exam code | DP-700 |
| Product family | Microsoft Fabric |
| Exam time | 100 minutes |
| IT Mastery status | Live DP-700 practice available |
| Domain | What to know | Common trap |
|---|---|---|
| Implement and manage an analytics solution | Workspaces, lakehouses, warehouses, semantic models, permissions, deployment, and lifecycle | Treating Fabric assets as interchangeable just because they share OneLake |
| Ingest and transform data | Pipelines, Dataflows Gen2, notebooks, Spark, SQL, shortcuts, incremental loads, and orchestration | Choosing the flashiest tool rather than the simplest fit for data shape and team skill |
| Monitor and optimize an analytics solution | Capacity, refresh, query performance, data quality, lineage, monitoring, and troubleshooting | Scaling capacity before identifying the bottleneck |
| Distinction | How to decide |
|---|---|
| Lakehouse vs warehouse | Lakehouses fit open data, files, Spark, and flexible data engineering; warehouses fit SQL-first relational analytics and T-SQL workloads. |
| Pipeline vs Dataflows Gen2 | Pipelines orchestrate activities; Dataflows Gen2 transform and load data through a low-code Power Query experience. |
| Notebook vs SQL | Notebooks fit Spark, code-heavy transformation, ML, and file processing; SQL fits relational querying and warehouse patterns. |
| Shortcut vs copy | Shortcuts reference data without duplicating it; copy physically moves or materializes data. |
| Semantic model vs warehouse table | Semantic models define analytical relationships and measures; warehouse tables store relational data. |
| Capacity problem vs model problem | Capacity affects shared compute resources; model or query design affects how efficiently the workload uses them. |
| Incremental refresh vs full refresh | Incremental refresh reduces repeated processing for changing data ranges; full refresh reprocesses everything. |
Take the free DP-700 diagnostic and classify each miss as an asset-selection, transformation, security, monitoring, or optimization miss. Fabric questions often include several valid tools; the exam usually rewards the one that matches the workload boundary and operational evidence.
Move to mixed timed practice when you can explain why a pipeline, Dataflow, notebook, SQL query, model change, or capacity action is the right layer to change.