Data Engineering on Microsoft Azure (DP-203) Practice Exam

Data Engineering on Microsoft Azure (DP-203) is designed for individuals with expertise in integrating, transforming, and consolidating data from diverse structured, unstructured, and streaming data systems into an appropriate schema to construct analytics solutions.

In the role of an Azure data engineer, candidates facilitate stakeholders' understanding of the data through exploration, and design and maintain secure, compliant data processing pipelines using a variety of tools and methods. They use different Azure data services and frameworks to store and generate refined, augmented datasets for analysis. 

As an Azure data engineer, they also contribute to ensuring that data pipelines and data stores are high-performing, efficient, well-organized, and dependable while adhering to specified business requirements and constraints. And, assist in identifying and resolving operational and data quality issues, and you design, implement, monitor, and optimize data platforms to align with the requirements of data pipelines.

Who should take the exam?

For Data Engineering on Microsoft Azure (DP-203) exam, candidates must have a solid knowledge of data processing languages, including:

• SQL
• Python
• Scala

They must have proficiency in parallel processing and data architecture patterns. And, knowledge of using the following for creating data processing solutions:

• Azure Data Factory
• Azure Synapse Analytics
• Azure Stream Analytics
• Azure Event Hubs
• Azure Data Lake Storage
• Azure Databricks

Exam Details

• Exam Code: DP-203
• Exam Name: Data Engineering on Microsoft Azure
• Exam Languages: English, Chinese (Simplified), Japanese, Korean, German, French, Spanish, Portuguese (Brazil), Arabic (Saudi Arabia), Russian, Chinese (Traditional), Italian, Indonesian (Indonesia)
• Exam Questions: 40-60 Questions
• Passing Score: 700 or greater (On a scale 1 - 1000)

DP-203 Exam Course Outline 

The Exam covers the given topics  - 

Topic 1: Understand how to design and implement data storage (15–20%)

Implementing a partition strategy

• Applying a partition strategy for files
• Implementing a partition strategy for analytical workloads
• Implementing a partition strategy for streaming workloads
• Applying a partition strategy for Azure Synapse Analytics
• Identifying when partitioning is needed in Azure Data Lake Storage Gen2

Designing the data exploration layer

• Creating and executing 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

Topic 2: Learn about developing data processing (40–45%)

Ingesting and transforming data

• Designing incremental loads
• Transform data by using Apache Spark
• Transforming 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

Developing batch processing solution

• Developing 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

Developing a stream processing solution

• 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

Managing batches and pipelines

• 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

Topic 3: Understand about securing, monitoring, and optimizing data storage and data processing (30–35%)

Implementing data security

• Implementing data masking
• Encrypt data at rest and in motion
• Implementing 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
• Implementing a data retention policy
• Implement secure endpoints (private and public)
• Implementing resource tokens in Azure Databricks
• Load a DataFrame with sensitive information
• Write encrypted data to tables or Parquet files
• Managing sensitive information

Monitoring data storage and data processing

• Implementing logging used by Azure Monitor
• Configure monitoring services
• Monitoring stream processing
• Measure performance of data movement
• Monitoring and updating statistics about data across a system
• Monitor data pipeline performance
• Measuring query performance
• Schedule and monitor pipeline tests
• Interpreting Azure Monitor metrics and logs
• Implement a pipeline alert strategy

Optimizing and troubleshooting data storage and data processing

• 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