---
title: "DP-900 Study Guide — Microsoft Azure Data Fundamentals"
description: "An interactive study guide built on 7 memory techniques to help you pass the Microsoft Azure Data Fundamentals exam."
url: "https://lucidlabs.com.au/insights/dp-900"
---

# Master the DP-900

An interactive study guide built on 7 memory techniques to help you pass the Microsoft Azure Data Fundamentals exam.

Core 25-30%Relational 20-25%Non-Relational 15-20%Analytics 25-30%

What it covers

Core data concepts (structured/semi/unstructured), relational databases (Azure SQL), non-relational (Cosmos DB), analytics workloads (Synapse, Power BI), and data governance (Purview).

Ideal for

IT professionals new to data, business analysts, project managers, and anyone wanting a foundational understanding of Azure data services.

Aspire to this if

You're starting a career in data, moving from infrastructure to analytics, or want to validate your Azure data knowledge before pursuing associate-level certifications.

Section 1 / Spatial Memory

## The Map

Tap any component to see what you need to know for the exam.

**🗄️ Relational Data**

Azure SQL · PostgreSQL · MySQL

**📄 Non-Relational Data**

Cosmos DB · Blob · Table

**📁 File & Streaming Data**

Data Lake · Event Hubs

**🏭 Azure Data Factory**

ETL/ELT Orchestration

**⚡ Azure Stream Analytics**

Real-time Processing

**🔥 Azure Databricks**

Apache Spark Analytics

**🗄️ Azure SQL Family**

SQL DB · Managed Instance · VM

**🌐 Azure Cosmos DB**

Multi-model · Global Distribution

**🏔️ Azure Data Lake**

Storage Gen2 · Hierarchical

**🔬 Azure Synapse Analytics**

Unified Analytics · SQL + Spark + Pipelines

**📊 Power BI**

Visualisation & Dashboards

**🔍 Microsoft Purview**

Data Governance & Cataloguing

Section 2 / Narrative Memory

## The Story

Follow the narrative to build a mental model of how everything connects.

🌊

### The Data Ocean

Data exists everywhere — in databases, files, streams, and APIs. It comes in three forms: structured (tables with fixed schemas), semi-structured (JSON, XML with flexible tags), and unstructured (images, videos, documents). Understanding these forms is the first step.

**Exam Intel**Three data classifications: Structured (fixed schema, tables), Semi-structured (tags/keys but flexible — JSON, XML), Unstructured (no schema — images, video, audio). Know which Azure services handle each.

🏗️

### The Foundations

Before data can flow, it needs a home. Relational databases store structured data in normalised tables with ACID guarantees. Non-relational databases offer flexibility — documents, key-value pairs, graphs, and column families each solve different problems.

**Exam Intel**ACID = Atomicity (all or nothing), Consistency (valid state), Isolation (concurrent safety), Durability (committed = permanent). Normalisation reduces redundancy. Denormalisation improves read performance.

🗄️

### The SQL Fortress

Azure SQL stands as a fortress for relational data. Three tiers serve different needs: SQL Database for cloud-native apps, Managed Instance for lift-and-shift migrations with near-full SQL Server compatibility, and SQL Server on VMs for those who need complete control.

**Exam Intel**Azure SQL Database = PaaS (Microsoft manages infra). Managed Instance = PaaS with 99% SQL Server compat. SQL VM = IaaS (you manage OS + SQL). Choose PaaS first, VM only when you need OS access or unsupported features.

🌐

### The Global Network

Azure Cosmos DB spans the globe. Its multi-model engine speaks many languages — NoSQL, MongoDB, Cassandra, Gremlin, and Table. With single-digit millisecond reads and five tuneable consistency levels, it trades between performance and data freshness.

**Exam Intel**Cosmos DB 5 consistency levels (strongest to weakest): Strong → Bounded Staleness → Session (default) → Consistent Prefix → Eventual. Request Units (RUs) = throughput currency. APIs determine data model.

🏭

### The Pipeline Factory

Data rarely stays where it was born. Azure Data Factory builds pipelines that extract data from 90+ sources, transform it in mapping data flows, and load it into analytical stores. Triggers schedule the work — on a timer, in tumbling windows, or when events fire.

**Exam Intel**ETL = Extract, Transform, Load (transform before storing). ELT = Extract, Load, Transform (load raw, transform in place — preferred for big data). Data Factory handles both. Mapping data flows = code-free visual transforms.

⚡

### The Stream

Some data can't wait for batch processing. Event Hubs ingests millions of events per second, and Stream Analytics processes them in real-time using windowing functions — tumbling windows for fixed intervals, sliding windows for overlapping events.

**Exam Intel**Batch = process accumulated data at intervals. Streaming = process as it arrives. Windowing: Tumbling (fixed, non-overlapping), Hopping (fixed, overlapping), Sliding (event-triggered), Session (grouped by activity gap).

🏔️

### The Data Lake

Azure Data Lake Storage Gen2 is the vast reservoir where raw data accumulates. Built on Blob Storage with a hierarchical namespace, it stores petabytes of data in formats like Parquet and Avro, ready for Spark and Synapse to analyse.

**Exam Intel**Data Lake Gen2 = Blob Storage + hierarchical namespace. Blob tiers: Hot (frequent access), Cool (30+ days), Archive (180+ days, offline). Parquet = columnar (analytics). Avro = row-based (streaming). CSV/JSON = human-readable.

🔬

### The Analytics Engine

Azure Synapse Analytics is the great unifier — SQL pools for data warehousing, Spark pools for big data processing, and built-in pipelines for orchestration. Serverless SQL lets you query Data Lake files without provisioning anything.

**Exam Intel**Synapse dedicated SQL pool = MPP data warehouse (provisioned, pay for compute). Serverless SQL pool = pay-per-query on files. Spark pool = big data notebooks. Synapse Link = HTAP (analytical queries on operational data without ETL).

📊

### The Dashboard Gallery

Power BI transforms data into stories. Desktop for authoring, Service for sharing, Mobile for on-the-go consumption. Reports tell detailed multi-page stories; dashboards pin the most critical tiles on a single page for at-a-glance monitoring.

**Exam Intel**Power BI Dashboard vs Report: Dashboard = single page, pins from multiple reports, no filters. Report = multi-page, interactive filters, built from one dataset. Paginated reports = pixel-perfect, optimised for printing.

🔍

### The Catalogue

Microsoft Purview brings order to the data estate. It scans sources automatically, builds a Data Map of all assets, traces lineage from source to dashboard, and applies sensitivity labels for compliance — ensuring data is discoverable, trustworthy, and governed.

**Exam Intel**Purview Data Catalog = discover/search data. Data Map = automated metadata scanning. Data Lineage = source-to-report tracing. Data Estate Insights = usage and health dashboards. Sensitivity labels from Microsoft 365 apply to data assets.

Section 3 / Acronym Memory

## Mnemonic Wall

Memorable acronyms and phrases to anchor key exam concepts in your memory.

💎

ACID

**A**tomicity, **C**onsistency, **I**solation, **D**urability

Transaction guarantees. All-or-nothing, valid state, concurrent safety, committed = permanent.

📊

OLTP vs OLAP

**O**n**L**ine **T**ransaction **P**rocessing vs **A**nalytical

OLTP = many small writes (point of sale). OLAP = complex reads (data warehouse). Know which services serve which.

🗄️

SMA

**S**QL Database, **M**anaged Instance, Azure **V**M

"SMV" — Azure SQL family from most managed to least. PaaS → PaaS → IaaS.

🌐

SBSCE

**S**trong, **B**ounded Staleness, **S**ession, **C**onsistent Prefix, **E**ventual

Cosmos DB consistency levels from strongest to weakest. Session is the default.

📄

DKCG

**D**ocument, **K**ey-value, **C**olumn-family, **G**raph

Four types of non-relational data stores. Cosmos DB supports all four via different APIs.

🔥

THSS

**T**umbling, **H**opping, **S**liding, **S**ession

"THE windows HOP, SLIDE, and SESSION." Stream Analytics windowing functions.

🏔️

HCCA

**H**ot, **C**ool, **C**old, **A**rchive

Blob Storage access tiers. Hot = frequent. Cool = 30+ days. Cold = 90+ days. Archive = 180+ days, offline, must rehydrate.

📊

DRD

**D**ataset → **R**eport → **D**ashboard

Power BI building blocks in order. Dashboards pin tiles from reports. Reports built from datasets.

🔑

PK-FK

**P**rimary **K**ey — **F**oreign **K**ey

PK uniquely identifies a row. FK references another table's PK. This creates relationships in relational databases.

📦

ELT

**E**xtract, **L**oad, **T**ransform

Modern approach: load raw data first, transform in place. Preferred for big data. ETL transforms before loading.

🔬

DSP

**D**edicated pool, **S**erverless pool, Spark **P**ool

Synapse Analytics three compute options. Dedicated = provisioned MPP. Serverless = pay-per-query. Spark = big data.

📁

PJCA

**P**arquet, **J**SON, **C**SV, **A**vro

Key file formats. Parquet = columnar (analytics). JSON = semi-structured. CSV = flat text. Avro = row-based (streaming).

🌐

NMCGT

**N**oSQL, **M**ongoDB, **C**assandra, **G**remlin, **T**able

Cosmos DB's 5 APIs. NoSQL is the native/recommended API.

🛡️

AAA

**A**uthentication, **A**uthorisation, **A**uditing

Three pillars of data security. Who are you, what can you do, what did you do.

🔍

CMLD

**C**atalog, **M**ap, **L**ineage, la**D**

Microsoft Purview capabilities. Catalog (discover), Map (scan metadata), Lineage (trace flow), Labels (classify).

Section 4 / Contrast Memory

## Versus Arena

Side-by-side comparisons to sharpen your understanding of similar concepts.

vs

RelationalvsNon-Relational

Click to compare

#### Relational vs Non-Relational

| Aspect | Relational | Non-Relational |
| --- | --- | --- |
| Schema | Fixed (schema-on-write) | Flexible (schema-on-read) |
| Language | SQL | Varies (API-specific) |
| Scaling | Vertical (scale up) | Horizontal (scale out) |
| ACID | Full support | Varies by service |
| Best for | Structured data, transactions | Semi/unstructured, scale |
| Azure services | Azure SQL, PostgreSQL, MySQL | Cosmos DB, Table Storage |

Click to flip back

vs

BatchvsStreaming

Click to compare

#### Batch vs Streaming

| Aspect | Batch | Streaming |
| --- | --- | --- |
| Timing | Processes accumulated data | Processes as it arrives |
| Latency | Minutes to hours | Seconds to milliseconds |
| Volume | Large datasets | Individual events |
| Examples | Nightly ETL, monthly reports | IoT sensors, fraud detection |
| Azure services | Data Factory, Synapse | Stream Analytics, Event Hubs |
| Complexity | Simpler to implement | More complex, stateful |

Click to flip back

vs

SQL DBvsManaged InstancevsVM

Click to compare

#### SQL DB vs Managed Instance vs VM

| Aspect | SQL DB | Managed Instance | VM |
| --- | --- | --- | --- |
| Model | PaaS | PaaS | IaaS |
| Compatibility | Most features | ~99% SQL Server | 100% SQL Server |
| Management | Fully managed | Mostly managed | You manage |
| Use case | New cloud apps | Lift-and-shift | Full control needed |
| OS access | No | No | Yes |
| Cost | Lowest | Medium | Highest |

Click to flip back

vs

OLTPvsOLAP

Click to compare

#### OLTP vs OLAP

| Aspect | OLTP | OLAP |
| --- | --- | --- |
| Purpose | Day-to-day transactions | Analytics & reporting |
| Operations | Many small reads/writes | Complex queries, aggregations |
| Schema | Normalised (3NF) | Denormalised (star/snowflake) |
| Data | Current, operational | Historical, analytical |
| Examples | Order entry, banking | Data warehouse, BI |
| Azure services | Azure SQL, Cosmos DB | Synapse, Power BI |

Click to flip back

vs

Star SchemavsSnowflake Schema

Click to compare

#### Star Schema vs Snowflake Schema

| Aspect | Star Schema | Snowflake Schema |
| --- | --- | --- |
| Dimensions | Denormalised | Normalised (sub-dimensions) |
| Joins | Fewer (faster queries) | More (complex queries) |
| Storage | More redundancy | Less redundancy |
| Query speed | Faster | Slower |
| Complexity | Simpler | More complex |
| Best for | Most BI/analytics | When storage matters |

Click to flip back

vs

ETLvsELT

Click to compare

#### ETL vs ELT

| Aspect | ETL | ELT |
| --- | --- | --- |
| Transform where | In transit (staging) | In destination |
| Best for | Smaller datasets | Big data, cloud-native |
| Speed | Slower (transform first) | Faster load, transform later |
| Complexity | Transform engine needed | Leverage destination compute |
| Data quality | Clean before load | Raw data available |
| Azure example | Data Factory mappings | Synapse + Data Lake |

Click to flip back

vs

DashboardvsReport

Click to compare

#### Dashboard vs Report

| Aspect | Dashboard | Report |
| --- | --- | --- |
| Pages | Single page | Multi-page |
| Source | Pins from multiple reports | Single dataset |
| Interactivity | Click to navigate | Filters, slicers, drill |
| Where created | Power BI Service only | Power BI Desktop or Service |
| Use case | At-a-glance monitoring | Detailed analysis |
| Refresh | Real-time tiles possible | Scheduled or on-demand |

Click to flip back

vs

HotvsCoolvsColdvsArchive

Click to compare

#### Hot vs Cool vs Cold vs Archive

| Aspect | Hot | Cool | Cold | Archive |
| --- | --- | --- | --- | --- |
| Min retention | None | 30 days | 90 days | 180 days |
| Storage cost | Highest | Lower | Lower still | Lowest |
| Access cost | Lowest | Higher | Higher | Highest (rehydrate) |
| Availability | 99.9% | 99% | 99% | Offline |
| Use case | Active data | Short-term backup | Rarely accessed | Compliance, long-term |

Click to flip back

vs

Cosmos DB APIs Compared

Click to compare

#### Cosmos DB APIs Compared

| Aspect | Cosmos DB APIs Compared |
| --- | --- |
| NoSQL | JSON documents | New apps (recommended) |
| MongoDB | BSON documents | MongoDB migrations |
| Cassandra | Column-family | Cassandra workloads |
| Gremlin | Graph (vertices/edges) | Relationship-heavy data |
| Table | Key-value | Azure Table migrations |

Click to flip back

Section 5 / Grouping Memory

## Cheat Sheet

Organised reference grouped by exam domain — everything you need on one page.

### Core Data Concepts

25-30%

#### Data Classification

-   Structured: fixed schema, tables, rows/columns
-   Semi-structured: flexible tags (JSON, XML, YAML)
-   Unstructured: no schema (images, video, audio, docs)

#### Data Roles

-   Database Admin: backups, access, performance, security
-   Data Engineer: pipelines, integration, data prep
-   Data Analyst: reports, dashboards, visualisations

#### File Formats

-   CSV: plain text, comma-separated, human-readable
-   JSON: key-value pairs, semi-structured
-   Parquet: columnar, compressed, analytics-optimised
-   Avro: row-based, schema evolution, streaming

#### Processing Patterns

-   Batch: scheduled, large volumes, high latency OK
-   Streaming: real-time, event-driven, low latency
-   ETL: transform before load (traditional)
-   ELT: load raw, transform in place (modern/cloud)

### Relational Data

20-25%

#### Core Concepts

-   Tables, rows (records), columns (fields)
-   Primary Key: unique row identifier
-   Foreign Key: references another table's PK
-   Normalisation: reduce redundancy (1NF, 2NF, 3NF)
-   ACID transactions: all-or-nothing guarantees

#### SQL Commands

-   DDL: CREATE, ALTER, DROP (structure)
-   DML: SELECT, INSERT, UPDATE, DELETE (data)
-   DCL: GRANT, REVOKE (permissions)
-   Views: virtual tables from queries
-   Indexes: speed up queries, slow down writes

#### Azure SQL Family

-   SQL Database: PaaS, elastic pools, serverless
-   Managed Instance: near-full SQL Server compat
-   SQL VM: IaaS, full OS access, any SQL version
-   PostgreSQL/MySQL: open-source managed PaaS

#### Security

-   Azure AD authentication
-   Firewall rules (IP-based)
-   TDE: Transparent Data Encryption (at rest)
-   Always Encrypted: client-side encryption

### Non-Relational Data

15-20%

#### Data Store Types

-   Key-value: simple lookups (Redis, Table Storage)
-   Document: JSON docs (Cosmos DB NoSQL)
-   Column-family: wide columns (Cosmos DB Cassandra)
-   Graph: vertices + edges (Cosmos DB Gremlin)

#### Azure Cosmos DB

-   Global distribution with multi-region writes
-   5 APIs: NoSQL, MongoDB, Cassandra, Gremlin, Table
-   5 consistency levels: Strong → Eventual
-   Request Units (RUs) = throughput measure
-   Partition key determines data distribution

#### Azure Storage

-   Blob: unstructured (Hot/Cool/Archive tiers)
-   File: SMB/NFS file shares (lift-and-shift)
-   Queue: messaging between components
-   Table: key-value NoSQL (simple, cheap)

#### When to Use

-   Key-value: session state, caching, config
-   Document: content management, catalogues
-   Column-family: IoT telemetry, time series
-   Graph: social networks, fraud detection

### Analytics Workload

25-30%

#### Azure Synapse

-   Dedicated SQL pool: provisioned MPP warehouse
-   Serverless SQL pool: pay-per-query on files
-   Spark pool: big data notebooks
-   Synapse Link: HTAP (no-ETL analytics on ops data)

#### Power BI

-   Desktop: author reports locally
-   Service: publish, share, collaborate
-   Mobile: consume on devices
-   Dashboard = single page, pins from reports
-   Report = multi-page, interactive, one dataset
-   Paginated = pixel-perfect, print-ready

#### Data Warehousing

-   Star schema: fact table + dimension tables
-   Snowflake schema: normalised dimensions
-   Fact tables: measurements/events (numeric)
-   Dimension tables: descriptive attributes
-   Slowly Changing Dimensions (SCD) types

#### Governance

-   Microsoft Purview: unified data governance
-   Data Catalog: discover and classify assets
-   Data Map: automated metadata scanning
-   Data Lineage: trace data flow end-to-end
-   Sensitivity labels for compliance

Section 6 / Method of Loci

## The Memory Palace

Walk through themed rooms — each object anchors a concept in spatial memory.

### The Foundation Hall

Core Data Concepts — Where understanding begins

📊

Structured Data

Tables with rows and columns. Fixed schema. SQL databases

📄

Semi-Structured

JSON, XML, YAML. Flexible tags/keys. No fixed table format

🖼️

Unstructured Data

Images, video, audio, documents. No inherent schema

💎

ACID Properties

Atomicity (all/nothing), Consistency, Isolation, Durability

📦

ETL vs ELT

Transform then load (ETL) vs load then transform (ELT). ELT preferred for cloud

⚡

Batch vs Stream

Batch = scheduled bulk. Stream = real-time. Different tools for each

### The SQL Chamber

Relational Data — Where structure reigns

🗄️

Azure SQL Database

Fully managed PaaS. Elastic pools for multi-tenant. Serverless option

🔄

Managed Instance

Near 100% SQL Server compat. Lift-and-shift migrations. VNet integration

🖥️

SQL VM

Full IaaS control. Any SQL Server version. OS-level access

🔑

Keys & Relations

Primary Key = unique ID. Foreign Key = reference. Creates table relationships

📐

Normalisation

1NF → 2NF → 3NF. Reduces redundancy. Improves data integrity

🐘

Open Source DBs

Azure Database for PostgreSQL and MySQL. Fully managed PaaS

### The Cosmos Chamber

Non-Relational Data — Where flexibility thrives

🌐

Cosmos DB

Global distribution. <10ms latency. 5 APIs. 5 consistency levels

📄

Document Store

JSON documents. Flexible schema. Each doc can differ. Cosmos DB NoSQL API

🔑

Key-Value Store

Simplest model. Fast lookups by key. Azure Table Storage, Redis Cache

📊

Column-Family

Wide columns. Good for time-series, IoT. Cosmos DB Cassandra API

🕸️

Graph Store

Vertices + edges. Model relationships. Cosmos DB Gremlin API

📦

Blob Storage

Unstructured data. Hot/Cool/Archive tiers. Block, append, page blobs

### The Pipeline Room

Data Integration — Where data flows

🏭

Data Factory

90+ connectors. Pipelines. Mapping data flows. Schedule/event triggers

⚡

Stream Analytics

SQL-like queries on streams. 4 windowing functions. Real-time output

🔥

Azure Databricks

Apache Spark platform. Notebooks. Python/Scala/SQL/R. Delta Lake

🔬

Synapse Analytics

Unified: SQL pools + Spark pools + Pipelines. Serverless & dedicated

🏔️

Data Lake Gen2

Blob + hierarchical namespace. Parquet/Avro/Delta. Big data optimised

📡

Event Hubs

Millions of events/second. Kafka compatible. Streaming ingestion

### The Insight Gallery

Analytics & Governance — Where value emerges

📊

Power BI Desktop

Author reports. Connect to data. DAX calculations. Publish to Service

📋

Power BI Service

Share reports. Create dashboards. Schedule refresh. Collaborate

⭐

Star Schema

Fact table (measures) + dimension tables (attributes). Analytics standard

🔍

Microsoft Purview

Data Catalog + Map + Lineage. Sensitivity labels. Unified governance

🛡️

Data Security

Azure AD auth. Firewall rules. TDE at rest. Always Encrypted

📈

Paginated Reports

Pixel-perfect. Print-optimised. Multi-page tables. SSRS-based

Section 7 / Pattern Recognition

## Pattern Spotter

Decision trees and trigger-answer pairs — see the pattern, know the answer.

Which Azure Database Service?

Which Azure Database Service?  
  ├── Relational + new cloud app → Azure SQL Database  ├── Relational + migrating SQL Server → SQL Managed Instance  ├── Relational + need OS access → SQL Server on VM  ├── NoSQL documents + global scale → Cosmos DB  ├── Simple key-value lookups → Azure Table Storage  ├── PostgreSQL or MySQL → Azure Database for PostgreSQL/MySQL  └── Unstructured files/blobs → Azure Blob Storage

Which Analytics Service?

Which Analytics Service?  
  ├── Full data warehouse (provisioned) → Synapse Dedicated SQL Pool  ├── Ad-hoc queries on Data Lake files → Synapse Serverless SQL Pool  ├── Big data processing with Spark → Synapse Spark Pool / Databricks  ├── Real-time stream processing → Azure Stream Analytics  ├── Business intelligence dashboards → Power BI  └── Data governance & cataloguing → Microsoft Purview

Which Cosmos DB API?

Which Cosmos DB API?  
  ├── New app, JSON documents → NoSQL API (recommended)  ├── Migrating from MongoDB → MongoDB API  ├── Wide-column / Cassandra workload → Cassandra API  ├── Graph / relationship-heavy data → Gremlin API  └── Migrating from Azure Table Storage → Table API

Which File Format?

Which File Format?  
  ├── Analytics / columnar queries → Parquet  ├── Streaming / schema evolution → Avro  ├── API / semi-structured exchange → JSON  ├── Simple tabular / human-readable → CSV  └── Delta Lake / ACID on data lake → Delta (Parquet + transaction log)

## Decision Cards

“globally distributed” or “multi-region writes”→Azure Cosmos DB

“single-digit millisecond latency”→Cosmos DB

“Request Units” or “RU/s”→Cosmos DB throughput

“ACID transactions” on relational data→Azure SQL Database

“lift-and-shift” SQL Server migration→SQL Managed Instance

“elastic pool” for multi-tenant→Azure SQL Database

“hierarchical namespace”→Data Lake Storage Gen2

“Hot, Cool, Archive” tiers→Azure Blob Storage

“tumbling window” or “hopping window”→Stream Analytics windowing

“mapping data flow” or “90+ connectors”→Azure Data Factory

“serverless SQL pool” or “pay-per-query”→Synapse Analytics

“Synapse Link” or “HTAP”→No-ETL analytics on operational data

“star schema” or “fact and dimension”→Data Warehouse design

“Data Catalog” or “data lineage”→Microsoft Purview

“dashboard vs report”→Dashboard = single page, Report = multi-page

“paginated report” or “pixel-perfect”→Power BI Paginated Reports

“partition key” for NoSQL→Cosmos DB data distribution

“graph database” or “vertices and edges”→Cosmos DB Gremlin API

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Microsoft Solutions Partner architect specialising in Fabric, Azure Data & AI, and GitHub Enterprise. 18+ years delivering data platforms for Australian businesses — building the systems these exams test every day.

[LinkedIn ↗](https://www.linkedin.com/in/keithoak/)[lucidlabs.com.au ↗](https://lucidlabs.com.au/)Published 29-03-2026