Azure Data Engineering Certification
15 Dec 2024

Study Guide for Exam DP-203: Data Engineering on Microsoft Azure

The DP-203 certification exam validates your expertise in designing and implementing data solutions on Azure. This study guide highlights key topics, resources, and strategies to help you succeed.

Purpose of this Document

This study guide aims to:

  • Provide an overview of what to expect on the exam.
  • Summarize key skills and topics.
  • Link to additional resources to deepen your understanding.
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Updates to the Exam

  • Exams are periodically updated to reflect the latest industry skills.
  • English versions are updated first, followed by localized versions after eight weeks.
  • Most questions cover features in General Availability (GA), but commonly used preview features may also appear.

Skills Measured as of October 24, 2024

Audience Profile

This exam targets professionals with expertise in:

  • Integrating, transforming, and consolidating structured, unstructured, and streaming data.
  • Building secure, high-performance data processing pipelines using Azure tools like Synapse, Data Factory, and Databricks.
  • Designing data platforms based on requirements, including modern data warehouses, lakehouses, and big data architectures.

You must also have:

  • Proficiency in SQL, Python, and Scala.
  • Experience with Azure services such as:
    • Azure Data Factory
    • Azure Synapse Analytics
    • Azure Stream Analytics
    • Azure Event Hubs
    • Azure Data Lake Storage
    • Azure Databricks

Skills at a Glance

Design and Implement Data Storage (15–20%)

Develop Data Processing (40–45%)

Ingest and Transform Data

  • Incremental data loads.
  • Data transformation using:
    • Apache Spark
    • T-SQL in Azure Synapse Analytics
    • Azure Synapse Pipelines or Azure Data Factory
    • Azure Stream Analytics
  • Handle data issues:
    • Duplicate, missing, or late-arriving data.
    • JSON shredding.
    • Encoding and decoding.
    • Normalization and denormalization.

Batch Processing Solutions

  • Use Azure Data Lake Storage Gen2, Azure Databricks, Synapse Analytics, and Data Factory.
  • Implement PolyBase for SQL pool data loading.
  • Integrate Jupyter or Python notebooks into pipelines.
  • Handle exceptions and manage batch retention.

Stream Processing Solutions

  • Use Azure Stream Analytics and Event Hubs.
  • Perform time-series processing and handle schema drift.
  • Configure checkpoints, watermarking, and replay archived streams.

Secure, Monitor, and Optimize Data Storage and Processing (30–35%)

Implement Data Security

  • Data masking, encryption, and RBAC.
  • POSIX-like ACLs in Data Lake Storage Gen2.
  • Secure endpoints and implement retention policies.

Monitor Data

  • Use Azure Monitor for logging and metrics.
  • Monitor pipelines and query performance.
  • Configure alerts for pipeline tests.

Optimize and Troubleshoot

  • Compact small files and handle data skew.
  • Optimize resource management and tune queries.
  • Troubleshoot failed Spark jobs and pipeline runs.

Study Resources

  1. Microsoft Learn:
    • Comprehensive learning paths for DP-203 exam topics.
  2. Azure Documentation:
    • Guides for Azure Synapse, Data Factory, and Databricks.
  3. Practice Questions:
    • Available on Microsoft Learn or third-party platforms.
  4. Community Forums:
    • Join discussions on Azure data engineering in forums like Reddit or Stack Overflow.

Change Log

  • October 2024 Update: Added skills related to Synapse Analytics database templates and Purview integration.
  • Previous Updates: Incremental changes to analytical workload partitioning strategies and security features.

Conclusion

The DP-203 certification exam is designed to validate your expertise in modern data engineering. By following this study guide and leveraging the resources provided, you can confidently prepare for the exam and advance your career as an Azure data engineer.

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