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Microsoft Azure Data Engineer Associate (DP-203)

Exam Overview

The Azure Data Engineer Associate certification validates skills and knowledge to design and implement the management, monitoring, security, and privacy of data using the full stack of Azure data services. Data engineers design and implement data solutions that ingest, store, and retrieve data from multiple sources and serve it to data scientists, data analysts, and other data consumers.

Exam Details: - Exam Code: DP-203 - Duration: 180 minutes - Number of Questions: 40-60 questions - Passing Score: 700 out of 1000 - Question Types: Multiple choice, multiple select, drag and drop, hot area, case studies, labs - Cost: $165 USD - Prerequisites: Experience with data processing languages and Azure data services

Exam Domains

1. Design and Implement Data Storage (15-20%)

  • Design a Data Storage Structure
  • Design an Azure Data Lake solution
  • Recommend file types for storage
  • Recommend file types for analytical queries
  • Design for efficient querying
  • Design for data pruning
  • Design a folder structure that represents the levels of data transformation
  • Design a distribution strategy

  • Design a data archiving solution

  • Design a Partition Strategy

  • Design a partition strategy for files
  • Design a partition strategy for analytical workloads
  • Design a partition strategy for efficiency/performance
  • Design a partition strategy for Azure Synapse Analytics

  • Identify when partitioning is needed in Azure Data Lake Storage Gen2

  • Design the Serving Layer

  • Design star schemas
  • Design slowly changing dimensions
  • Design a dimensional hierarchy
  • Design a solution for temporal data
  • Design for incremental loading
  • Design analytical stores
  • Design metastores in Azure Synapse Analytics and Azure Databricks

2. Design and Develop Data Processing (40-45%)

  • Ingest and Transform Data
  • Transform data by using Apache Spark
  • Transform data by using Transact-SQL
  • Transform data by using Data Factory
  • Transform data by using Azure Synapse Pipelines
  • Transform data by using Stream Analytics
  • Cleanse data
  • Split data
  • Shred JSON
  • Encode and decode data
  • Configure error handling for the transformation
  • Normalize and denormalize values
  • Transform data by using Scala

  • Perform data exploratory analysis

  • Design and Develop a Batch Processing Solution

  • Develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
  • Create data pipelines
  • Design and implement incremental data loads
  • Design and develop slowly changing dimensions
  • Handle security and compliance requirements
  • Scale resources
  • Configure the batch size
  • Design and create tests for data pipelines
  • Integrate Jupyter/IPython notebooks into a data pipeline
  • Upsert data
  • Regress to a previous state
  • Design and configure exception handling
  • Configure batch retention

  • Design a batch processing solution

  • Design and Develop a Stream Processing Solution

  • Develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
  • Process data by using Spark structured streaming
  • Monitor for performance and functional regressions
  • Design and create windowed aggregates
  • Handle schema drift
  • Process time series data
  • Process across partitions
  • Process within one partition
  • Configure checkpoints/watermarking during processing
  • Scale resources
  • Design and create tests for data pipelines
  • Optimize pipelines for analytical or transactional purposes
  • Handle interruptions
  • Design and configure exception handling
  • Upsert data

  • Replay archived stream data

  • Manage Batches and Pipelines

  • Trigger batches
  • Handle failed batch loads
  • Validate batch loads
  • Manage data pipelines in Data Factory/Synapse Pipelines
  • Schedule data pipelines in Data Factory/Synapse Pipelines
  • Implement version control for pipeline artifacts
  • Manage Spark jobs in a pipeline

3. Design and Implement Data Security (10-15%)

  • Design Security for Data Policies and Standards
  • Design data encryption for data at rest and in transit
  • Design a data auditing strategy
  • Design a data masking strategy
  • Design for data privacy
  • Design a data retention policy
  • Design to purge data based on business requirements

  • Design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2

  • Implement Data Security

  • Implement data masking
  • Encrypt data at rest and in motion
  • Implement row-level and column-level security
  • Implement Azure RBAC
  • Implement POSIX-like ACLs for Data Lake Storage Gen2
  • Implement a data retention policy
  • Implement a data auditing strategy
  • Manage identities, keys, and secrets across different data platform technologies
  • Implement secure endpoints (private and service)
  • Implement resource tokens in Azure Databricks
  • Load a DataFrame with sensitive information
  • Write encrypted data to tables or Parquet files
  • Manage sensitive information

4. Monitor and Optimize Data Storage and Data Processing (10-15%)

  • Monitor Data Storage and Data Processing
  • Implement logging used by Azure Monitor
  • Configure monitoring services
  • Measure performance of data movement
  • Monitor and update statistics about data across a system
  • Monitor data pipeline performance
  • Measure query performance
  • Monitor cluster performance
  • Understand custom logging options
  • Schedule and monitor pipeline tests
  • Interpret Azure Monitor metrics and logs

  • Implement a pipeline alert strategy

  • Optimize and Troubleshoot Data Storage and Data Processing

  • Compact small files
  • Handle skew in data
  • Handle data spill
  • Tune shuffle partitions
  • Find shuffling in a pipeline
  • Optimize resource management
  • Tune pipelines for performance
  • Optimize pipeline for analytical or transactional purposes
  • Optimize pipeline for descriptive versus analytical workloads
  • Troubleshoot a failed spark job
  • Troubleshoot a failed pipeline run

Skills Measured in Detail

Data Storage Architecture

  • Data Lake Design: Gen2 features, hierarchical namespace, performance optimization
  • Partitioning Strategies: Date-based, categorical, hash partitioning
  • File Formats: Parquet, Delta Lake, ORC, JSON, CSV selection criteria
  • Storage Tiers: Hot, cool, archive tier strategies

Data Processing Technologies

  • Apache Spark: DataFrames, RDDs, Spark SQL, performance tuning
  • Azure Data Factory: Pipelines, activities, triggers, monitoring
  • Azure Synapse Analytics: SQL pools, Spark pools, pipelines
  • Stream Analytics: Windowing, joins, anomaly detection

Data Integration Patterns

  • ETL vs ELT: When to use each approach
  • Batch Processing: Scheduling, monitoring, error handling
  • Real-time Processing: Stream processing, event-driven architectures
  • Hybrid Architectures: Lambda and Kappa architectures

Security and Governance

  • Access Control: RBAC, ACLs, managed identities
  • Data Protection: Encryption, masking, auditing
  • Compliance: GDPR, retention policies, data lineage
  • Network Security: Private endpoints, firewalls, VNet integration

Performance Optimization

  • Query Optimization: Indexing, statistics, query plans
  • Resource Scaling: Auto-scaling, cluster sizing
  • Bottleneck Identification: Performance monitoring, troubleshooting
  • Cost Optimization: Resource management, storage optimization

Study Tips

  1. Weeks 1-2: Data engineering fundamentals and Azure data services overview
  2. Weeks 3-4: Data Lake Storage Gen2 and file systems
  3. Weeks 5-6: Azure Data Factory and pipeline development
  4. Weeks 7-8: Apache Spark and Azure Databricks
  5. Weeks 9-10: Azure Synapse Analytics and SQL pools
  6. Weeks 11-12: Stream processing with Stream Analytics and Event Hubs
  7. Weeks 13-14: Security, monitoring, and optimization
  8. Weeks 15-16: Practice exams and hands-on projects

Key Study Resources

  • Microsoft Learn Learning Paths:
  • DP-203: Data engineering on Microsoft Azure
  • Azure Data Factory documentation and tutorials
  • Azure Databricks learning resources
  • Azure Synapse Analytics documentation
  • Apache Spark and PySpark tutorials

Technical Skills Required

  • Programming Languages: SQL, Python, Scala, .NET
  • Big Data Technologies: Apache Spark, Hadoop ecosystem
  • Cloud Platforms: Azure services, hybrid architectures
  • Data Formats: JSON, Parquet, Delta Lake, ORC, Avro

Hands-on Practice Requirements

  • Real Projects: Build end-to-end data pipelines
  • Multiple Data Sources: APIs, databases, files, streams
  • Performance Tuning: Optimize queries and processing
  • Security Implementation: Implement access controls and encryption

Exam Strategy

  • Architecture Focus: Understand when to use different services
  • Performance Considerations: Know optimization techniques
  • Security Requirements: Always consider data protection
  • Cost Optimization: Understand pricing and cost factors

Common Gotchas

  • Data Lake Partitioning: Optimal partitioning strategies for different scenarios
  • Spark Performance: Understanding shuffle operations and optimization
  • Security Models: RBAC vs ACL differences in Data Lake Storage
  • Pipeline Orchestration: Triggers, dependencies, and error handling
  • File Formats: When to use Parquet vs Delta Lake vs other formats

Hands-on Lab Areas

Data Storage Labs

  • Design and implement Data Lake Storage Gen2 architecture
  • Configure partitioning strategies for different scenarios
  • Implement security and access controls
  • Optimize storage for performance and cost

Data Processing Labs

  • Build batch processing pipelines with Data Factory
  • Develop Spark applications in Azure Databricks
  • Create streaming solutions with Stream Analytics
  • Implement data transformation workflows

Azure Synapse Labs

  • Configure dedicated SQL pools and serverless SQL
  • Build and optimize data warehouse solutions
  • Implement CI/CD for Synapse artifacts
  • Create integrated analytics solutions

Security and Monitoring Labs

  • Implement data encryption and masking
  • Configure audit and compliance monitoring
  • Set up performance monitoring and alerting
  • Implement cost optimization strategies

Real-world Projects

  • Build medallion architecture (bronze, silver, gold)
  • Implement real-time analytics dashboard
  • Create data quality monitoring solution
  • Design disaster recovery for data platform

Key Technologies Deep Dive

Azure Data Lake Storage Gen2

  • Hierarchical Namespace: File system semantics
  • Access Control: POSIX-like ACLs and RBAC
  • Performance: Parallel processing capabilities
  • Integration: Native integration with analytics services

Azure Data Factory / Synapse Pipelines

  • Activities: Copy, transform, control flow activities
  • Triggers: Schedule, tumbling window, event-based
  • Integration Runtime: Self-hosted, Azure, Azure-SSIS
  • Monitoring: Pipeline runs, activity monitoring

Apache Spark / Azure Databricks

  • Core Concepts: RDDs, DataFrames, Datasets
  • Performance: Catalyst optimizer, Tungsten execution
  • Libraries: MLlib, GraphX, Structured Streaming
  • Cluster Management: Autoscaling, job scheduling

Azure Synapse Analytics

  • Architecture: Dedicated pools, serverless, Spark pools
  • Data Integration: Built-in pipelines and connectors
  • Analytics: T-SQL, Spark, Power BI integration
  • Security: Column-level, row-level, dynamic masking

Comprehensive Study Resources

👉 Complete Azure Study Resources Guide

For detailed information on courses, practice tests, hands-on labs, communities, and more, see our comprehensive Azure study resources guide which includes: - Official Microsoft Learn paths (FREE) - Top-rated video courses with specific instructors - Practice test platforms with pricing and comparisons - Hands-on lab environments and free tier details - Community forums and study groups - Essential tools and Azure CLI resources - Pro tips and budget-friendly study strategies

Prerequisites and Next Steps

Prerequisites

  • Data fundamentals knowledge (DP-900 recommended)
  • Programming experience (Python, SQL, Scala)
  • Understanding of data processing concepts
  • Experience with cloud platforms

Career Path

  • Next Certifications: DP-600 (Fabric Analytics Engineer), specialized Azure certs
  • Role Focus: Data Engineer, Analytics Engineer, Data Platform Engineer
  • Skills Development: MLOps, DataOps, advanced analytics, real-time processing

Remember: DP-203 requires hands-on experience with data engineering workflows and Azure data services. Focus on building complete data solutions and understanding performance optimization techniques.