Azure DP-203: Data Engineering on Microsoft Azure - Fact Sheet¶

Exam Overview¶

Exam DP-203: Data Engineering on Microsoft Azure validates skills in designing and implementing data solutions using Azure services. This certification demonstrates expertise in integrating, transforming, and consolidating data from various sources into analytics solutions.

Exam Details¶

Duration: 120 minutes
Question Types: Multiple choice, multiple select, case studies, drag-and-drop
Passing Score: 700/1000
Cost: $165 USD
Languages: English, Japanese, Chinese (Simplified), Korean, German, French, Spanish, Portuguese (Brazil), Russian, Indonesian, Arabic, Chinese (Traditional), Italian

Exam Domains (Skills Measured)¶

Design and implement data storage (15-20%)
Develop data processing (40-45%)
Secure, monitor, and optimize data storage and data processing (30-35%)

1. Azure Data Storage Solutions¶

Azure Data Lake Storage Gen2¶

📖 Azure Data Lake Storage Gen2 Introduction - Overview of hierarchical namespace and big data analytics capabilities

📖 Data Lake Storage Gen2 Best Practices - Performance optimization and design patterns

📖 Hierarchical Namespace - Understanding file and directory organization

📖 Access Control Lists (ACLs) - POSIX-style permissions for files and directories

📖 Multi-Protocol Access - Blob API and ADLS Gen2 API compatibility

📖 Performance Tuning Guide - Optimizing throughput and latency

📖 Lifecycle Management Policies - Automating data tiering and deletion

📖 Access Tiers - Hot, Cool, Cold, and Archive storage tiers

📖 Security Recommendations - Securing data lake storage accounts

📖 Disaster Recovery - High availability and failover strategies

Azure Synapse Analytics¶

📖 Azure Synapse Analytics Overview - Unified analytics platform for data warehousing and big data

📖 Dedicated SQL Pools - Massively parallel processing (MPP) architecture

📖 Serverless SQL Pools - Query data in data lake without provisioning infrastructure

📖 Apache Spark Pools - In-memory big data processing with Spark

📖 Data Integration Pipelines - ETL/ELT orchestration within Synapse

📖 Synapse Studio - Unified workspace for data engineering tasks

📖 Table Distribution Strategies - Round-robin, hash, and replicated distribution

📖 Table Indexing - Clustered columnstore, clustered, and nonclustered indexes

📖 Partitioning Tables - Improving query performance with partitioning

📖 Resource Classes and Workload Management - Managing query concurrency and resources

📖 PolyBase - Loading and querying external data sources

📖 COPY Statement - High-throughput data ingestion

📖 Delta Lake Support - ACID transactions for data lakes

📖 Link Feature - Near real-time analytics over operational data

Azure Cosmos DB¶

📖 Azure Cosmos DB Overview - Globally distributed, multi-model database service

📖 Consistency Levels - Strong, bounded staleness, session, consistent prefix, eventual

📖 Partitioning Strategy - Logical and physical partitions

📖 Request Units (RUs) - Understanding throughput pricing model

📖 Change Feed - Processing data changes in real-time

📖 Analytical Store - Column-oriented storage for analytics

📖 Synapse Link for Cosmos DB - Hybrid transactional and analytical processing (HTAP)

📖 Global Distribution - Multi-region writes and reads

📖 Indexing Policies - Automatic and custom indexing strategies

📖 Time to Live (TTL) - Automatic data expiration

Azure SQL Database¶

📖 Azure SQL Database Overview - Intelligent, scalable cloud database service

📖 Service Tiers - DTU and vCore purchasing models

📖 Elastic Pools - Resource sharing across multiple databases

📖 Hyperscale Service Tier - Highly scalable storage and compute

📖 Geo-Replication - Active geo-replication and failover groups

2. Data Processing and Transformation¶

Azure Data Factory¶

📖 Azure Data Factory Overview - Cloud-based ETL and data integration service

📖 Pipelines and Activities - Orchestrating data movement and transformation

📖 Linked Services - Connection information to data stores

📖 Datasets - Data structure references within stores

📖 Integration Runtime - Compute infrastructure for data integration

📖 Mapping Data Flows - Visual data transformation at scale

📖 Wrangling Data Flows - Code-free data preparation with Power Query

📖 Control Flow Activities - Conditional execution, loops, and branching

📖 Triggers - Schedule, tumbling window, and event-based triggers

📖 Parameters and Expressions - Dynamic pipeline configuration

📖 Copy Activity - Data movement between stores

📖 Lookup Activity - Retrieving configuration data

📖 ForEach Activity - Iterating over collections

📖 Execute Pipeline Activity - Modular pipeline design

📖 Schema Drift Handling - Managing evolving data structures

📖 Performance Optimization - Tuning data movement performance

📖 Fault Tolerance - Handling incompatible rows

📖 Incremental Loading - Loading only changed data

Azure Databricks¶

📖 Azure Databricks Overview - Apache Spark-based analytics platform

📖 Workspace Organization - Notebooks, clusters, jobs, and libraries

📖 Clusters - Interactive and job clusters configuration

📖 Autoscaling - Dynamic cluster scaling

📖 Notebooks - Interactive development with Python, Scala, R, SQL

📖 Jobs - Scheduling and orchestrating workflows

📖 Delta Lake - ACID transactions for data lakes

📖 Delta Table Optimization - Compaction and Z-ordering

📖 Time Travel - Querying historical versions of data

📖 Structured Streaming - Real-time data processing with Spark

📖 Auto Loader - Incremental file processing

📖 Unity Catalog - Unified governance for data and AI

📖 Secret Management - Securely storing credentials

📖 MLflow Integration - Machine learning lifecycle management

Azure Stream Analytics¶

📖 Stream Analytics Overview - Real-time analytics on streaming data

📖 Inputs - Event Hubs, IoT Hub, Blob storage

📖 Outputs - SQL Database, Blob storage, Event Hubs, Power BI

📖 Query Language - SQL-like syntax for stream processing

📖 Windowing Functions - Tumbling, hopping, sliding, and session windows

📖 Streaming Units (SUs) - Compute capacity allocation

📖 Time Policies - Event time vs. arrival time

📖 Late Arrival and Out-of-Order Events - Handling event timing issues

📖 User-Defined Functions - JavaScript UDFs for custom logic

📖 Geospatial Functions - Location-based analytics

📖 Compatibility Level - Feature availability across versions

Azure Event Hubs¶

📖 Event Hubs Overview - Big data streaming platform and event ingestion service

📖 Partitions - Parallel processing and ordering guarantees

📖 Consumer Groups - Multiple readers on the same stream

📖 Capture Feature - Automatic data archival to storage

📖 Throughput Units - Capacity planning for standard tier

📖 Auto-Inflate - Automatic scaling of throughput units

📖 Event Hubs Dedicated - Single-tenant deployments

📖 Apache Kafka Integration - Kafka protocol support

📖 Schema Registry - Schema validation and evolution

Azure Functions¶

📖 Azure Functions Overview - Serverless compute for event-driven applications

📖 Triggers and Bindings - Declarative connections to services

📖 Durable Functions - Stateful workflows in serverless

📖 Event Hub Trigger - Processing streaming events

📖 Cosmos DB Trigger - Change feed processing

📖 Timer Trigger - Schedule-based execution

3. Data Security and Governance¶

Authentication and Authorization¶

📖 Azure Active Directory Integration - Identity and access management

📖 Managed Identities - System-assigned and user-assigned identities

📖 Service Principal - Application identity for resource access

📖 Azure RBAC - Role-based access control for Azure resources

📖 Storage Account Keys - Managing shared access keys

📖 Shared Access Signatures (SAS) - Delegated access to storage resources

📖 Account SAS vs Service SAS - Different SAS token types

📖 Stored Access Policy - Centralized SAS management

Encryption and Key Management¶

📖 Encryption at Rest - Data protection when stored

📖 Encryption in Transit - TLS/SSL for data movement

📖 Azure Key Vault - Secrets, keys, and certificates management

📖 Customer-Managed Keys - Bring your own encryption keys

📖 Transparent Data Encryption - SQL Database encryption

📖 Always Encrypted - Column-level encryption in SQL

Network Security¶

📖 Virtual Networks - Network isolation for Azure resources

📖 Service Endpoints - Direct routing to Azure services

📖 Private Endpoints - Private IP access to Azure services

📖 Firewall Rules - IP-based access restrictions

📖 Azure Private Link - Accessing services over private connection

Data Governance and Compliance¶

📖 Azure Purview Overview - Unified data governance service

📖 Data Catalog - Discovering and understanding data assets

📖 Data Lineage - Tracking data origin and transformations

📖 Data Classification - Automated and manual sensitivity labeling

📖 Dynamic Data Masking - Limiting sensitive data exposure

📖 Row-Level Security - Restricting row access in tables

📖 Column-Level Security - Controlling column access

4. Monitoring and Optimization¶

Azure Monitor¶

📖 Azure Monitor Overview - Full-stack monitoring for Azure resources

📖 Metrics - Time-series performance data

📖 Logs - Detailed diagnostic and operational data

📖 Log Analytics Workspace - Centralized log storage and querying

📖 KQL (Kusto Query Language) - Query language for log analytics

📖 Alerts - Proactive notification on conditions

📖 Action Groups - Notifications and automated responses

📖 Diagnostic Settings - Routing platform logs and metrics

📖 Application Insights - Application performance monitoring

Performance Optimization¶

📖 Query Performance Insight - SQL Database query analysis

📖 Automatic Tuning - AI-powered database optimization

📖 Index Advisor - Index recommendations

📖 Materialized Views - Pre-computed aggregations

📖 Result Set Caching - Caching query results

📖 Partition Elimination - Reducing data scanned

📖 Statistics - Query optimizer input data

📖 Dynamic Management Views - System health and performance

Cost Management¶

📖 Azure Cost Management - Analyzing and optimizing spending

📖 Budgets and Alerts - Spending limits and notifications

📖 Pricing Calculator - Estimating Azure service costs

📖 Reserved Capacity - Discounted pricing with commitments

5. Data Formats and Serialization¶

File Formats¶

📖 Parquet Format - Columnar storage format for analytics

📖 Avro Format - Row-based serialization with schema evolution

📖 ORC Format - Optimized row columnar format

📖 JSON Format - Human-readable text-based format

📖 CSV Format Best Practices - Delimited text file handling

Compression¶

📖 Compression in Data Lake - Choosing compression codecs

📖 Snappy Compression - Fast compression for Hadoop workloads

📖 Gzip Compression - High compression ratio format

6. Best Practices and Design Patterns¶

Data Ingestion Patterns¶

📖 Batch Processing - Processing data in scheduled intervals

📖 Stream Processing - Continuous data processing

📖 Lambda Architecture - Batch and speed layers combined

📖 Kappa Architecture - Stream processing only approach

📖 Medallion Architecture - Bronze, silver, gold data layers

Data Modeling¶

📖 Star Schema - Dimensional modeling with fact and dimension tables

📖 Snowflake Schema - Normalized dimension tables

📖 Slowly Changing Dimensions - Handling dimension updates

📖 Data Vault - Agile data warehouse modeling

High Availability and Disaster Recovery¶

📖 High Availability Design - Architecting resilient solutions

📖 Backup and Restore - Automated backup strategies

📖 Business Continuity - Ensuring service continuity

📖 Geo-Redundancy - LRS, ZRS, GRS, RA-GRS, GZRS options

7. Exam Preparation Tips¶

Key Study Areas¶

Understand service capabilities and limitations: Know when to use each Azure service
Hands-on practice: Create resources and implement solutions in Azure portal
Performance optimization: Learn distribution, indexing, and partitioning strategies
Security implementation: Practice configuring authentication, encryption, and network security
Monitoring and troubleshooting: Use Azure Monitor, diagnostic logs, and KQL queries
Cost optimization: Understand pricing models and cost-saving features
Design patterns: Learn common architectures for batch and stream processing

Practice Resources¶

📖 Microsoft Learn DP-203 Path - Official Microsoft learning path

📖 Azure Free Account - $200 credit for 30 days

📖 Azure Sandbox Environment - Practice without Azure subscription

📖 Exam Skills Outline - Detailed exam objectives PDF

Common Exam Scenarios¶

Data ingestion from multiple sources: Choosing appropriate tools and patterns
Transforming and cleansing data: Using Data Factory, Databricks, or Synapse
Designing storage solutions: Selecting storage types and configurations
Implementing security: Authentication, authorization, and encryption
Optimizing query performance: Indexing, partitioning, and caching
Monitoring data pipelines: Setting up alerts and diagnostics
Handling streaming data: Event Hubs and Stream Analytics configuration
Implementing disaster recovery: Backup, geo-replication, and failover

8. Important Concepts Summary¶

Data Storage Concepts¶

Hierarchical namespace: File system semantics for data lakes
Partition keys: Distribution strategy for parallel processing
Consistency levels: Trade-offs between performance and data consistency
Data redundancy: LRS, ZRS, GRS, RA-GRS, GZRS options
Access tiers: Hot, Cool, Cold, Archive for cost optimization
ACLs vs RBAC: File-level vs resource-level permissions

Data Processing Concepts¶

MPP architecture: Massively parallel processing in Synapse
Serverless computing: On-demand compute without infrastructure management
Pipeline orchestration: Dependencies, triggers, and control flow
Schema drift: Handling evolving data structures
Windowing: Tumbling, hopping, sliding, session windows
Event time vs arrival time: Temporal processing considerations

Security Concepts¶

Defense in depth: Multiple layers of security
Least privilege: Minimum necessary permissions
Zero trust: Verify explicitly, assume breach
Data encryption: At rest and in transit
Network isolation: VNets, private endpoints, service endpoints
Identity management: AAD, managed identities, service principals

Monitoring Concepts¶

Telemetry collection: Metrics, logs, traces
KQL queries: Analyzing log data
Alert rules: Metric, log, activity log alerts
Diagnostic settings: Routing platform logs
Performance baselines: Establishing normal behavior
Cost analysis: Understanding spending patterns

9. Quick Reference Commands¶

Azure CLI Commands¶

# Data Factory
az datafactory create
az datafactory pipeline create-run
az datafactory pipeline-run show

# Synapse Analytics
az synapse workspace create
az synapse spark pool create
az synapse sql pool create

# Storage Account
az storage account create
az storage blob upload-batch
az storage account keys list

# Databricks
az databricks workspace create
az databricks workspace update

# Event Hubs
az eventhubs namespace create
az eventhubs eventhub create
az eventhubs eventhub consumer-group create

# Stream Analytics
az stream-analytics job create
az stream-analytics input create
az stream-analytics output create

PowerShell Commands¶

# Resource Management
New-AzResourceGroup
New-AzStorageAccount
New-AzDataFactoryV2

# Synapse
New-AzSynapseWorkspace
New-AzSynapseSqlPool
New-AzSynapseSparkPool

# Monitor
New-AzMetricAlertRuleV2
Get-AzLog
New-AzActionGroup

# Key Vault
New-AzKeyVault
Set-AzKeyVaultSecret
Get-AzKeyVaultSecret

SQL Queries for Synapse¶

-- Create external data source
CREATE EXTERNAL DATA SOURCE DataLakeSource
WITH (LOCATION = 'abfss://container@account.dfs.core.windows.net');

-- Create external file format
CREATE EXTERNAL FILE FORMAT ParquetFormat
WITH (FORMAT_TYPE = PARQUET);

-- Create external table
CREATE EXTERNAL TABLE ExternalSales
WITH (LOCATION = '/sales/', DATA_SOURCE = DataLakeSource, FILE_FORMAT = ParquetFormat);

-- Use COPY statement for fast loading
COPY INTO StagingTable
FROM 'https://account.blob.core.windows.net/container/*.parquet'
WITH (FILE_TYPE = 'PARQUET');

-- Create statistics
CREATE STATISTICS stats_date ON SalesTable(SaleDate);

-- Update statistics
UPDATE STATISTICS SalesTable;

10. Troubleshooting Common Issues¶

Data Factory Issues¶

Pipeline failures: Check activity outputs and diagnostic logs
Slow copy performance: Review DIU settings and parallelism
Authentication errors: Verify linked service credentials and permissions
Timeout issues: Adjust activity timeout settings

Synapse Analytics Issues¶

Query performance: Check distribution, indexing, and statistics
Concurrency limits: Review resource class assignments
Load failures: Examine rejected rows and error files
Memory errors: Optimize resource class or simplify queries

Databricks Issues¶

Cluster startup delays: Consider pool clusters for faster start
Out of memory: Increase driver/executor memory or reduce data per partition
Slow jobs: Review shuffle operations and data skew
Library conflicts: Isolate dependencies using cluster-scoped libraries

Stream Analytics Issues¶

Late arrival events: Adjust late arrival tolerance policy
Out-of-order events: Configure out-of-order tolerance window
Insufficient SUs: Scale streaming units based on query complexity
Output errors: Verify output connection strings and permissions

Appendix: Service Limits and Quotas¶

Azure Data Lake Storage Gen2¶

Max storage account size: 5 PB
Max blob size: 190.7 TiB
Max throughput: 60 Gbps ingress, 120 Gbps egress

Azure Synapse Analytics¶

Max DWU for dedicated pool: 30,000 DWU
Max concurrent queries (serverless): 20
Max Spark pools per workspace: 20

Azure Data Factory¶

Max activities per pipeline: 40
Max parameters per pipeline: 50
Max integration runtimes per factory: 100
Max concurrent pipeline runs: 100,000

Azure Event Hubs¶

Max throughput units (Standard): 40
Max message size: 1 MB
Max partition count: 32 (Standard), 100 (Premium)
Max retention period: 7 days (Standard), 90 days (Premium)

Azure Databricks¶

Max clusters per workspace: 150
Max nodes per cluster: 250
Max concurrent jobs: 1,000

Conclusion¶

This fact sheet provides a comprehensive overview of Azure data engineering services and concepts covered in the DP-203 exam. Focus on hands-on practice with these services, understand their capabilities and limitations, and learn when to apply each service to real-world scenarios. Review the linked documentation regularly as Azure services are continuously updated with new features.

Total Documentation Links: 120

Good luck with your DP-203 certification exam!