Description

Questions: 161
Release Date: 09/2021 (Last Updated: 02/2024)
Job Role: Data Engineer
Language: English

The DP-203 practice test contains 161 questions and covers the following objectives:

• 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) in Azure Synapse Analytics
• 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
• Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery
• 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