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Cheat sheet

Azure DP-900 Cheat Sheet

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Core Data Concepts

  • 25-30% of exam
  • Data Types Storage Models Workloads Data Roles OLTP vs OLAP

Relational Data

  • 20-25% of exam
  • Relational Objects SQL Verbs Azure SQL Open Source SQL Picker

Non-relational Data

  • 15-20% of exam
  • Azure Storage Cosmos DB Cosmos APIs NoSQL Picker Blob vs Files

Analytics Workloads

  • 25-30% of exam
  • Analytics Pipeline Fabric Real-time Analytics Power BI Analytics Picker

Quick Facts

  • Exam DP-900
  • Credential Azure Data Fundamentals
  • Time 45 min
  • Pass 700/1000
  • Level Beginner
  • Blueprint Nov 1 2024
  • Page Oct 31 2025
  • Skill Match workload

Domain Weights CO

  • Core, SQL, NoSQL, Analytics
  • Core: 25-30 SQL: 20-25 NoSQL: 15-20 Analytics: 25-30

OLTP vs OLAP CO

OLTP

  • Current operations
  • Small writes
  • Normalized tables

OLAP

  • Historical analysis
  • Large scans
  • Aggregated facts
  • Transact vs analyze

Storage Picker CO

  • Fixed schema → Relational ( SQL )
  • Flexible JSON → Document ( NoSQL )
  • Simple lookup → Key-value
  • Deep relationships → Graph
  • Large files → Object
  • Timestamp events → Time series

Data Types CO

  • Structured Rows and columns
  • Semi-structured Tagged flexible shape
  • Unstructured No fixed model
  • JSON Document format
  • XML Tagged hierarchy
  • CSV Delimited rows
  • Parquet Columnar analytics files
  • Avro Schema-based serialization

Analytics Types CO

  • What, why, next, do
  • Descriptive: what Diagnostic: why Predictive: next Prescriptive: do

Structured vs Semi-structured CO

Structured

  • Fixed schema
  • Tables
  • SQL-friendly

Semi-structured

  • Flexible schema
  • JSON/XML
  • Document-friendly
  • Rigid vs flexible

Storage Models CO

  • Relational Tables plus relationships
  • Document JSON-like aggregates
  • Key-value Fast key lookup
  • Column-family Wide sparse rows
  • Graph Nodes and edges
  • Object Files and blobs
  • Time series Timestamped events
  • Search index Text relevance

Workloads CO

  • OLTP Current transactions
  • OLAP Historical analysis
  • Batch Scheduled chunks
  • Streaming Continuous events
  • ETL Transform before load
  • ELT Transform after load
  • Descriptive What happened
  • Predictive What may happen

Data Roles CO

  • DBA Operate databases
  • Data engineer Build pipelines
  • Data analyst Model and visualize
  • Data scientist Build ML models
  • Data steward Govern data quality
  • Business user Consume insights

SQL Options RE

  • DB -> MI -> VM
  • DB: modern app MI: migration VM: full control

SQL DB vs MI RE

SQL Database

  • Database scope
  • Cloud-native apps
  • Least admin

Managed Instance

  • Instance scope
  • Migration fit
  • SQL Agent
  • Database vs instance

SQL Picker RE

  • New cloud app → SQL Database ( PaaS )
  • Many small DBs → Elastic pool
  • Instance features → Managed Instance
  • Full OS control → SQL on VM ( IaaS )
  • Postgres engine → PostgreSQL
  • MySQL engine → MySQL

Relational Objects RE

  • Table Rows and columns
  • Primary key Unique row identity
  • Foreign key Table relationship
  • Index Faster lookup
  • View Saved query
  • Stored procedure Saved logic
  • Schema Object namespace
  • Normalization Reduce duplication

PaaS vs IaaS SQL RE

PaaS SQL

  • Managed patching
  • Less control
  • Built-in HA

SQL VM

  • OS control
  • Full compatibility
  • You manage
  • Managed vs control

SQL Verbs RE

  • SELECT Read rows
  • INSERT Add rows
  • UPDATE Change rows
  • DELETE Remove rows
  • JOIN Combine tables
  • WHERE Filter rows
  • GROUP BY Aggregate groups
  • ORDER BY Sort results

Azure SQL RE

  • SQL Database Managed database PaaS
  • Elastic pool Shared database resources
  • Managed Instance Managed instance PaaS
  • SQL on VM Full IaaS control
  • Hyperscale Large database tier
  • Serverless Auto-pause compute
  • Azure Arc SQL Hybrid SQL management
  • T-SQL SQL Server dialect

Open Source RE

  • PostgreSQL Managed PostgreSQL
  • MySQL Managed MySQL
  • Flexible server Managed OSS tier
  • Migration Engine compatibility matters
  • Single server Legacy deployment
  • OSS engines Vendor-neutral skills

Cosmos APIs NO

  • NoSQL, Mongo, Cassandra, Gremlin, Table
  • NoSQL: document Cassandra: wide-column Gremlin: graph Table: key-value

Blob vs Files NO

Blob

  • Object API
  • Massive scale
  • Web access

Files

  • SMB/NFS
  • Mounted shares
  • Lift-and-shift
  • Objects vs shares

NoSQL Picker NO

  • Unstructured objects → Blob
  • Lift SMB shares → Files
  • Simple messages → Queues
  • Key-value table → Tables
  • Global JSON app → Cosmos NoSQL
  • Graph traversal → Cosmos Gremlin

Azure Storage NO

  • Blob Object storage
  • Files SMB/NFS shares
  • Queues Simple messages
  • Tables Key-value NoSQL
  • Containers Blob grouping
  • Access tier Cost by access
  • Hot Frequent access
  • Archive Offline retrieval

Cosmos DB vs Tables NO

Cosmos DB

  • Global distribution
  • Multiple APIs
  • RU model

Table Storage

  • Key-value
  • Simple scale
  • Lower complexity
  • Global app vs simple

Cosmos DB NO

  • Cosmos DB Distributed NoSQL
  • Account Global entry point
  • Database Container namespace
  • Container Items plus throughput
  • Item JSON document
  • Partition key Scale and routing
  • RU Throughput currency
  • Consistency Replica read guarantee

Cosmos APIs NO

  • NoSQL Native document API
  • MongoDB Mongo-compatible API
  • Cassandra Wide-column API
  • Gremlin Graph API
  • Table Key-value API
  • PostgreSQL Distributed relational API

Analytics Pipeline AN

  • Ingest -> Store -> Process -> Serve -> Visualize
  • Ingest: ADF Store: lake Process: Spark Visualize: Power BI

Batch vs Streaming AN

Batch

  • Data at rest
  • Scheduled jobs
  • High throughput

Streaming

  • Data in motion
  • Continuous queries
  • Low latency
  • Later vs now

Analytics Picker AN

  • Unified SaaS analytics → Fabric
  • Pipelines only → Data Factory
  • Spark notebooks → Databricks
  • Stream queries → Stream Analytics
  • Event ingestion → Event Hubs
  • Dashboards → Power BI

Analytics Pipeline AN

  • Ingest Bring data in
  • Store Persist raw data
  • Process Clean and transform
  • Model Shape for analysis
  • Serve Expose curated data
  • Visualize Reports and dashboards
  • Data lake Raw analytical storage
  • Warehouse Structured BI store

Fabric vs Power BI AN

Fabric

  • End-to-end analytics
  • OneLake
  • Multiple workloads

Power BI

  • Reports
  • Dashboards
  • Semantic models
  • Platform vs BI

Analytics Services AN

  • Fabric SaaS analytics platform
  • OneLake Tenant data lake
  • Data Factory Pipeline orchestration
  • Databricks Spark analytics
  • Stream Analytics Real-time queries
  • Event Hubs Event ingestion
  • Synapse Analytics workspace
  • Power BI Business intelligence

Power BI AN

  • Desktop Report authoring
  • Service Cloud sharing
  • Semantic model Reusable data model
  • Dataset Model plus data
  • Dashboard Pinned visuals
  • Report Multi-page visuals
  • Gateway On-prem connectivity
  • DAX Model expressions

Common Traps

Official weights changed

  • NoSQL is 15-20 ≠ Analytics is 25-30

SQL service scope

  • SQL DB is database ≠ MI is instance

Control vs management

  • VM gives control ≠ PaaS reduces admin

Blob vs file shares

  • Blob uses objects ≠ Files supports SMB

Cosmos API choice

  • Gremlin is graph ≠ Table is key-value

OLTP vs warehouse

  • OLTP writes transactions ≠ Warehouse scans history

Batch vs streaming

  • Batch is scheduled ≠ Streaming is continuous

Power BI role

  • Power BI visualizes ≠ Fabric unifies analytics

Last Minute

  • 1. Weights: 25-30 / 20-25 / 15-20 / 25-30
  • 2. OLTP = transactions; OLAP = analysis
  • 3. Structured = tables; semi = JSON
  • 4. SQL DB = database PaaS
  • 5. MI = managed instance
  • 6. VM = OS control
  • 7. Blob = objects; Files = SMB
  • 8. Cosmos = global NoSQL
  • 9. Gremlin = graph API
  • 10. ADF = pipelines; Power BI = visuals
  • 11. Fabric = SaaS analytics
  • 12. Streaming = data in motion

Azure DP-900

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