DP-203: Data Engineering on Microsoft Azure

Design and implement data storage (15–20%)

Develop data processing (40–45%)

Secure, monitor, and optimize data storage and data processing (30–35%)

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