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DP-600 Study Guide

Preview the DP-600 flashcard-note study format, then unlock Pro for the complete section-by-section guide.

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Section 1 Select, filter, and aggregate by using Visual Query Editor, SQL, KQL, and DAX Preview

Preview: Prepare data

Preview includes
  • 4 of 8 lesson topics
  • 1 overview segment
  • 3 core concepts
  • 2 exam tips

Lesson Topics

  • No-Code Warehouse Querying
  • Data Selection
  • Filtering
  • Aggregation

Overview

This objective covers building warehouse queries using no-code tools like the Visual Query Editor, SQL, KQL, and DAX. The core task involves selecting, filtering, and aggregating data based on the provided data sources and transformations.

Core Concepts

  • Visual Query Editor: A no-code canvas for constructing warehouse queries, supporting selection, joins, filters, and aggregations.
  • SQL: Suitable for relational selection, joins, grouping, and creating reusable database objects within warehouses and SQL analytics endpoints.
  • KQL: Optimized for fast exploration of event, log, and time-series data in Eventhouse and KQL databases.

Exam Tips

  • Prioritize the no-code approach with Visual Query Editor whenever possible, especially for initial prototyping and simpler queries.
  • When SQL is required, focus on relational selection, joins, and aggregations – the core strengths of SQL in the warehouse environment.
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Section 2 Implement workspace, item, row, column, object, and file access controls Pro
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Section 3 Configure Git integration, PBIP projects, and deployment pipelines Pro
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Section 4 Choose storage modes and implement star-schema relationships Pro
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Section 5 Choose Fabric data stores and implement OneLake integration Pro
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Section 6 Create connections, discover data, and choose ingestion or access Pro
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Section 7 Create SQL views, functions, procedures, and enrichment logic Pro
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Section 8 Implement star schemas, denormalization, and aggregations Pro
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Section 9 Optimize models with Direct Lake, DAX tuning, and incremental refresh Pro
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Section 10 Write DAX with variables, iterators, table filters, windowing, and information functions Pro
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Section 11 Implement calculation groups, dynamic format strings, and field parameters Pro
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Section 12 Apply sensitivity labels and endorse analytics items Pro
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Section 13 Perform impact analysis and manage reusable semantic assets through XMLA Pro
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Section 14 Resolve duplicates, nulls, data types, joins, and filters Pro
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Section 15 Design composite models and large semantic models Pro
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