DP-203

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Acing the Microsoft DP-203 Exam with QuestionsTube

The Data Engineering on Microsoft Azure DP-203 exam is the part of the requirements for Microsoft Certified: Azure Data Engineer Associate certification. You for this certification should have subject matter expertise in integrating, transforming, and consolidating data from various structured, unstructured, and streaming data systems into a suitable schema for building analytics solutions. The Microsoft DP-203 exam is available in multiple languages, including English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia). Make sure that you can pass the DP-203 exam and earn the Microsoft Certified: Azure Data Engineer Associate certification to promote your positions.

Microsoft Certification DP-203 Exam Skills Measured

• Implement a partition strategy for files
• Implement a partition strategy for analytical workloads
• Implement a partition strategy for streaming workloads
• Implement a partition strategy for Azure Synapse Analytics
• Identify when partitioning is needed in Azure Data Lake Storage Gen2
• Create and execute queries by 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
• Design and implement incremental loads
• Transform data by using Apache Spark
• Transform data by using Transact-SQL (T-SQL)
• Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory
• Transform data by using Azure Stream Analytics
• Cleanse data
• Handle duplicate data
• Handle missing data
• Handle late-arriving data
• Split data
• Shred JSON
• Encode and decode data
• Configure error handling for a transformation
• Normalize and denormalize data
• Perform data exploratory analysis
• Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
• Use PolyBase to load data to a SQL pool
• Implement Azure Synapse Link and query the replicated data
• Create data pipelines
• Scale resources
• Configure the batch size
• Create tests for data pipelines
• Integrate Jupyter or Python notebooks into a data pipeline
• Upsert data
• Revert data to a previous state
• Configure exception handling
• Configure batch retention
• Read from and write to a delta lake
• Create a stream processing solution by using Stream Analytics and Azure Event Hubs
• Process data by using Spark structured streaming
• Create windowed aggregates
• Handle schema drift
• Process time series data
• Process data across partitions
• Process within one partition
• Configure checkpoints and watermarking during processing
• Scale resources
• Create tests for data pipelines
• Optimize pipelines for analytical or transactional purposes
• Handle interruptions
• Configure exception handling
• Upsert data
• Replay archived stream data
• Trigger batches
• Handle failed batch loads
• Validate batch loads
• Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines
• Schedule data pipelines in Data Factory or Azure Synapse Pipelines
• Implement version control for pipeline artifacts
• Manage Spark jobs in a pipeline
• Implement data masking
• Encrypt data at rest and in motion
• Implement row-level and column-level security
• Implement Azure role-based access control (RBAC)
• Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
• Implement a data retention policy
• Implement secure endpoints (private and public)
• Implement resource tokens in Azure Databricks
• Load a DataFrame with sensitive information
• Write encrypted data to tables or Parquet files
• Manage sensitive information
• Implement logging used by Azure Monitor
• Configure monitoring services
• Monitor stream processing
• Measure performance of data movement
• Monitor and update statistics about data across a system
• Monitor data pipeline performance
• Measure query performance
• Schedule and monitor pipeline tests
• Interpret Azure Monitor metrics and logs
• Implement a pipeline alert strategy
• Compact small files
• Handle skew in data
• Handle data spill
• Optimize resource management
• Tune queries by using indexers
• Tune queries by using cache
• Troubleshoot a failed Spark job
• Troubleshoot a failed pipeline run, including activities executed in external services

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