DP-700 prerequisites: what you need first
DP-700 has no formal prerequisite: you can book it without holding any other certification. Microsoft does describe what a candidate should be able to do: you should have subject matter expertise with data loading patterns, data architectures and orchestration processes, and be skilled at manipulating and transforming data with SQL, PySpark and Kusto Query Language (KQL). That audience profile is the real prerequisite.
What is required and what is expected
| Item | Status |
|---|---|
| Another certification | Not required |
| SQL | Expected; you should write joins, aggregates and window functions |
| PySpark | Expected; DataFrames, joins, aggregations, structured streaming basics |
| KQL | Expected; filters, summarize, bin, joins |
| Fabric experience | Expected; ideally real projects with lakehouses, warehouses and pipelines |
The three languages
SQL. Used in warehouses and SQL analytics endpoints, and for security statements. You need joins, CTEs, aggregation, window functions and the security syntax for row-level security and masking.
PySpark. Used in notebooks for transformation. You do not need to be a Spark internals expert, but you should read and complete code that filters, joins, groups, deduplicates and writes Delta tables, and be aware of streaming reads and writes.
KQL. The one most candidates lack. It is used in Eventhouses and for windowing in Real-Time Intelligence. A few hours with where, summarize, bin, project and join covers most of what you need.
Fabric items you should have used
| Item | Why |
|---|---|
| Lakehouse | Delta tables, Files, shortcuts, table maintenance |
| Warehouse | T-SQL, security, statistics |
| Data pipeline | Copy, notebooks, parameters, triggers |
| Notebook | Spark transformation |
| Dataflow Gen2 | Low-code transformation |
| Eventstream and Eventhouse | Streaming and KQL |
| Deployment pipeline and Git | Lifecycle management |
Where you might be coming from
| Background | Gap to close first |
|---|---|
| Data warehouse developer with T-SQL | PySpark, Delta, KQL, streaming |
| Spark or Databricks engineer | Fabric workspace security, pipelines, KQL, deployment pipelines |
| Power BI analyst | Spark and SQL; consider DP-600 first |
| Azure data engineer | Fabric-specific items and OneLake |
If you already hold Databricks skills, compare the two paths in DP-700 vs the Databricks Data Engineer Associate.
What you do not need
You do not need AZ-900 or any fundamentals exam first, and no specific prior Microsoft credential. A free trial or a small capacity is enough to practise on.
Where to go next
If you have the language skills above, go straight to the study plan. If not, spend a couple of weeks on the weakest language before starting it, and read is DP-700 hard? to calibrate.