Core Data Concepts
25-30%of exam
Relational Data
20-25%of exam
Non-relational Data
15-20%of exam
Analytics Workloads
25-30%of exam
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
Core, SQL, NoSQL, Analytics
Core: 25-30SQL: 20-25NoSQL: 15-20Analytics: 25-30
OLTP vs OLAP
OLTP
- Current operations
- Small writes
- Normalized tables
OLAP
- Historical analysis
- Large scans
- Aggregated facts
Transact vs analyze
Storage Picker
- Fixed schema→Relational(SQL)
- Flexible JSON→Document(NoSQL)
- Simple lookup→Key-value
- Deep relationships→Graph
- Large files→Object
- Timestamp events→Time series
Data Types
- 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
What, why, next, do
Descriptive: whatDiagnostic: whyPredictive: nextPrescriptive: do
Structured vs Semi-structured
Structured
- Fixed schema
- Tables
- SQL-friendly
Semi-structured
- Flexible schema
- JSON/XML
- Document-friendly
Rigid vs flexible
Storage Models
- 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
- 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
- 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
DB -> MI -> VM
DB: modern appMI: migrationVM: full control
SQL DB vs MI
SQL Database
- Database scope
- Cloud-native apps
- Least admin
Managed Instance
- Instance scope
- Migration fit
- SQL Agent
Database vs instance
SQL Picker
- 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
- 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
PaaS SQL
- Managed patching
- Less control
- Built-in HA
SQL VM
- OS control
- Full compatibility
- You manage
Managed vs control
SQL Verbs
- 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
- 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
- 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
NoSQL, Mongo, Cassandra, Gremlin, Table
NoSQL: documentCassandra: wide-columnGremlin: graphTable: key-value
Blob vs Files
Blob
- Object API
- Massive scale
- Web access
Files
- SMB/NFS
- Mounted shares
- Lift-and-shift
Objects vs shares
NoSQL Picker
- 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
- 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
Cosmos DB
- Global distribution
- Multiple APIs
- RU model
Table Storage
- Key-value
- Simple scale
- Lower complexity
Global app vs simple
Cosmos DB
- 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
- 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
Ingest -> Store -> Process -> Serve -> Visualize
Ingest: ADFStore: lakeProcess: SparkVisualize: Power BI
Batch vs Streaming
Batch
- Data at rest
- Scheduled jobs
- High throughput
Streaming
- Data in motion
- Continuous queries
- Low latency
Later vs now
Analytics Picker
- Unified SaaS analytics→Fabric
- Pipelines only→Data Factory
- Spark notebooks→Databricks
- Stream queries→Stream Analytics
- Event ingestion→Event Hubs
- Dashboards→Power BI
Analytics Pipeline
- 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
Fabric
- End-to-end analytics
- OneLake
- Multiple workloads
Power BI
- Reports
- Dashboards
- Semantic models
Platform vs BI
Analytics Services
- 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
- 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
Buy on Amazon
Take courses on Udemy
Learning path on PluralsightSame family resources
Explore More Microsoft Azure Certifications
Continue into nearby exams from the same family. Each card keeps practice questions, study guides, flashcards, videos, and articles in one place.
More From This Family
Videos and articles for deeper review.
VideoAI-900 Exam Guide 2026: FREE Azure AI Fundamentals Study PlanVideoAZ-104 Study Plan 2026: Pass Azure Administrator ExamVideoAZ-900 Exam Guide 2026: Pass Azure Fundamentals Free (Complete Study Plan)VideoFREE MD-102 Exam Guide 2026: Pass Microsoft Endpoint Administrator (Intune, Autopilot, Windows 11)ArticleAI-900 Exam Guide 2026: FREE Azure AI Fundamentals Study Plan26 min readArticleAZ-104 Study Plan 2026: Pass Azure Administrator Exam17 min readArticleAZ-500 Exam Guide 2026: Azure Security Before Retirement11 min readArticleAZ-900 Exam Guide 2026: Pass Azure Fundamentals Free (Complete Study Plan)28 min read