Chapter 2 of 3 · Microsoft DP-600

Prepare Data

Get data into Fabric, choose a store, build a star schema, and query it with the visual editor, SQL, KQL and DAX.

  • 7topics
  • 35 minfast track
  • 2diagrams and exhibits
  • 45–50%of the exam
Start with Data Connections and Data Discovery

Topics in this chapter

Take them in order, or jump to the one you need.

  1. 1Data Connections and Data DiscoveryCreate Fabric data connections with cloud, on-premises and virtual network gateways, and discover data with the OneLake catalog…5 min
  2. 2Ingest or Access Data and Choose a StoreShortcuts, mirroring, pipelines, Dataflow Gen2 and notebooks; choosing a lakehouse, warehouse or Eventhouse; OneLake integration…Diagram5 min
  3. 3Views, Functions, Procedures and EnrichmentCreate T-SQL views, inline table-valued functions and stored procedures in a Fabric warehouse, and enrich data by adding new…5 min
  4. 4Star Schema, Denormalize, Aggregate and JoinBuild a star schema in a Fabric lakehouse or warehouse: fact and dimension tables, surrogate keys, denormalizing, aggregating…Diagram5 min
  5. 5Clean, Convert and Filter DataFind and fix duplicates, missing data and nulls, convert data types and filter rows in Fabric with T-SQL, PySpark and Power Query…5 min
  6. 6Query with the Visual Query Editor and SQLSelect, filter and aggregate data in a Fabric warehouse with the no-code Visual query editor and with T-SQL, including HAVING and…5 min
  7. 7Query with KQL and DAXSelect, filter and aggregate data with KQL in an Eventhouse and with DAX queries in DAX query view: where, summarize, EVALUATE…5 min

About this chapter

What this chapter covers

Prepare data is 45 to 50% of the Microsoft DP-600 exam, the largest domain by far. It has three skill groups: getting data, transforming data, and querying and analyzing data.

Getting data covers connections, discovery in the OneLake catalog and Real-Time hub, choosing between ingesting and accessing data in place, choosing a data store, and OneLake integration for Eventhouse and semantic models. Transforming covers views, functions and stored procedures, star schemas, denormalizing, aggregating, joining and cleaning data. Querying covers the Visual query editor, SQL, KQL and DAX.

Questions are scenarios and code: you choose the store, tool or transformation, complete a T-SQL, KQL, PySpark, Power Query M or DAX statement, or predict what a query returns.

How the topics fit together

Connect and discoverIngest or shortcutLakehouse, warehouseor EventhouseTransformstar schemaQuerySQL, KQL, DAX

Data arrives through a connection, lands in or is linked to a store, and is shaped into clean dimension and fact tables. Analysts then query those tables in the language that suits the store.

Exam tip

This domain rewards knowing which engine fits which store. T-SQL writes to a warehouse while a lakehouse's SQL analytics endpoint is read-only, Spark writes to a lakehouse, and KQL queries an Eventhouse.

How to use these pages

Each topic page has the same shape, so you can skim for what you need.

  • What it is: the idea in plain words.
  • How it works: the mechanism, with tables and code examples.
  • What to remember for the exam: the facts to memorise and how questions are phrased.
  • Common mistakes: the classic traps.
  • Check yourself: three questions to test your understanding.

Ready for more practice?

The full Microsoft DP-600 question bank has 648 questions, with timed practice exams and an explanation for every answer.

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