Practice Exams | Microsoft Azure DP-203 Data Engineering

Based on the detailed role description you've provided, it appears that this role involves a broad set of skills and responsibilities across various aspects of data engineering, management, security, and optimization within the Microsoft Azure ecosystem. Here's a breakdown of how you might approach this role, considering the different percentages allocated to each responsibility:

Data Exploration Layer (40-45%)

• Create and execute queries using a compute solution that leverages SQL serverless and Spark cluster.
• Recommend and implement Azure Synapse Analytics database templates.
• Push new or updated data lineage to Microsoft Purview.
• Browse and search metadata in Microsoft Purview Data Catalog.

Ingesting and Transforming Data (40-45%)

• Design and implement incremental loads to optimize data movement.
• Transform data using Apache Spark for distributed processing.
• Transform data using T-SQL in Azure Synapse Analytics for SQL-based operations.
• Ingest and transform data using Azure Synapse Pipelines or Azure Data Factory for workflow automation.
• Transform data using Azure Stream Analytics for real-time processing.
• Cleanse data to ensure quality.
• Handle duplicate data to prevent redundancy.
• Use Exactly Once Delivery in Azure Stream Analytics to ensure data integrity.
• Handle missing data and late-arriving data.
• Split data, such as shredding JSON, to make it easier to process.
• Encode and decode data for secure transmission.
• Normalize and denormalize data based on the needs of the system.
• Perform exploratory analysis to understand data characteristics.

Batch Processing Solution (20%)

• Develop batch processing solutions using Azure Data Lake Storage, Azure Databricks, and Azure Synapse Analytics.
• Implement version control for pipeline artifacts to maintain code integrity.
• Manage Spark jobs in a pipeline to ensure efficient job execution.
• Schedule data pipelines using Data Factory or Synapse Pipelines.

Security, Monitoring, and Optimization (30-35%)

• Implement data security measures, including encryption, masking, and access controls.
• Monitor data storage and processing using Azure Monitor and implement a strategy for alerts.
• Optimize and troubleshoot data storage and processing to handle performance issues and resource management challenges.

Data Storage Considerations (15%)

• Implement data retention policies to ensure compliance and efficient storage use.
• Compact small files in Azure Data Lake Storage to optimize storage costs and performance.
• Handle data skew to ensure balanced processing.
• Manage sensitive information securely within the system.

General Considerations

• Implement resource tokens in Azure Databricks for secure access control.
• Load a DataFrame with sensitive information following best practices for data security.
• Write encrypted data to tables or Parquet files to protect data at rest.
• Tune queries by using indexers and cache to improve performance.
• Troubleshoot a failed Spark job or pipeline run to resolve technical issues.

Final Thoughts

This role requires a deep understanding of Azure services, particularly Azure Synapse Analytics, Azure Data Factory, Azure Databricks, and Microsoft Purview. It also demands expertise in data engineering patterns, security practices, and performance optimization techniques. The ability to monitor, alert, and troubleshoot effectively is crucial for maintaining high-quality, secure, and performant data processing pipelines. Additionally, staying up-to-date with Azure service updates and best practices will be essential for continued success in this role.