In today’s data-driven world, data engineering plays a crucial role in designing and building robust data solutions that enable businesses to derive valuable insights and make informed decisions. Microsoft Azure offers a powerful suite of data engineering tools and services that facilitate the creation of scalable and efficient data pipelines. To validate your expertise in this domain, Microsoft introduced Exam DP-203: Data Engineering on Microsoft Azure.
Whether you are a data engineer, developer, or IT professional, this exam is an excellent opportunity to showcase your data engineering skills. In this comprehensive guide, we will explore the requirements to pass the DP-203 exam and highlight essential points to know before attempting it.
Exam Overview:
DP-203: Data Engineering on Microsoft Azure is designed to assess your proficiency in designing and implementing data storage, processing, and ingestion solutions on Azure. The exam covers a wide range of topics, including data integration, data transformation, data movement, data orchestration, and data monitoring. By earning this certification, you demonstrate your ability to work with Azure data services and build data solutions that meet business requirements.
Requirements to Pass the Exam:
To successfully pass the DP-203 exam, candidates must demonstrate expertise in the following key areas:
- Azure Data Storage Solutions: Familiarize yourself with various data storage options on Microsoft Azure, including Azure SQL Database, Azure Cosmos DB, Azure Data Lake Storage, and Azure Blob Storage. Understand when to use each storage solution based on data characteristics and workload requirements.
- Data Integration and Transformation: Master the process of data integration and transformation using Azure Data Factory, Azure Databricks, and Azure Synapse Analytics. Learn how to ingest data from different sources, transform it to meet analytical needs, and load it into target data stores.
- Data Movement and Orchestration: Understand how to efficiently move data between different data platforms and services using Azure Data Factory. Learn how to orchestrate complex data workflows to ensure the smooth flow of data across the data pipeline.
- Data Monitoring and Optimization: Be familiar with monitoring and optimizing data solutions on Azure. Learn how to use Azure Monitor and other monitoring tools to track the performance and health of data pipelines, and optimize data processing for better performance.
- Data Security and Compliance: Comprehend data security and compliance considerations in data engineering. Understand how to implement security measures and ensure data privacy and compliance with relevant regulations.
Important Points to Know Before Attempting the Exam:
- Review Official Microsoft Learning Paths: Microsoft provides free online learning paths and documentation for the DP-203 exam. These resources cover each topic in detail and are essential for building a strong foundation.
- Hands-On Experience: Practical experience is key to success in the DP-203 exam. Spend time working with Azure data services and building data pipelines. Practice implementing different data engineering scenarios to gain confidence in using Azure tools effectively.
- Explore Real-World Use Cases: Familiarize yourself with real-world data engineering use cases and challenges. Analyze different scenarios to understand how Azure data services can be leveraged to address specific business requirements.
- Join Azure Data Community: Engage with the Azure data engineering community through forums, webinars, and social media. Interacting with experienced professionals and peers can provide valuable insights and tips for exam preparation.
- Practice with Sample Projects: Attempt sample data engineering projects to get hands-on experience. Practice designing data solutions and implementing data pipelines to ensure you are comfortable with Azure data services.
- Stay Updated with Azure Updates: Azure data services are regularly updated with new features and improvements. Stay updated with the latest announcements and product updates to be aware of the latest capabilities available for data engineering on Azure.
In conclusion, the DP-203 exam, Data Engineering on Microsoft Azure, is a valuable certification for individuals looking to showcase their expertise in designing and building data solutions on Azure. By understanding Azure data storage options, data integration, transformation, and orchestration, candidates can confidently pass the exam and demonstrate their ability to handle complex data engineering tasks. Diligently study, gain hands-on experience, and utilize available resources to maximize your chances of success in the DP-203 exam. Good luck on your journey to becoming a Microsoft Certified Data Engineer on Azure!
-
Design and implement data storage (15–20%)
-
Implement a partition strategy
-
(50) Community CommentImplement a partition strategy for files
-
(46) Community CommentImplement a partition strategy for analytical workloads
-
(76) Community CommentImplement a partition strategy for streaming workloads
-
(58) Community CommentImplement a partition strategy for Azure Synapse Analytics
-
(59) Community CommentIdentify when partitioning is needed in Azure Data Lake Storage Gen2
-
-
Design and implement the data exploration layer
-
(33) Community CommentCreate and execute queries by using a compute solution that leverages SQL serverless and Spark cluster
-
(37) Community CommentRecommend and implement Azure Synapse Analytics database templates
-
(31) Community CommentPush new or updated data lineage to Microsoft Purview
-
(40) Community CommentBrowse and search metadata in Microsoft Purview Data Catalog
-
-
Develop data processing (40–45%)
-
Ingest and transform data
-
(33) Community CommentDesign and implement incremental loads
-
(32) Community CommentTransform data by using Apache Spark
-
(56) Community CommentTransform data by using Transact-SQL (T-SQL)
-
(40) Community CommentIngest and transform data by using Azure Synapse Pipelines or Azure Data Factory
-
(77) Community CommentTransform data by using Azure Stream Analytics
-
(36) Community CommentCleanse data
-
(42) Community CommentHandle duplicate data
-
(38) Community CommentHandle missing data
-
(71) Community CommentHandle late-arriving data
-
(37) Community CommentSplit data
-
(40) Community CommentShred JSON
-
(39) Community CommentEncode and decode data
-
(40) Community CommentConfigure error handling for a transformation
-
(48) Community CommentNormalize and denormalize data
-
(29) Community CommentPerform data exploratory analysis
-
-
Develop a batch processing solution
-
(37) Community CommentDevelop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory
-
(34) Community CommentUse PolyBase to load data to a SQL pool
-
(36) Community CommentImplement Azure Synapse Link and query the replicated data
-
(42) Community CommentCreate data pipelines
-
(41) Community CommentScale resources
-
(40) Community CommentConfigure the batch size
-
(43) Community CommentCreate tests for data pipelines
-
(52) Community CommentIntegrate Jupyter or Python notebooks into a data pipeline
-
(40) Community CommentUpsert data
-
(28) Community CommentRevert data to a previous state
-
(38) Community CommentConfigure exception handling
-
(37) Community CommentConfigure batch retention
-
(45) Community CommentRead from and write to a delta lake
-
-
Develop a stream processing solution
-
(38) Community CommentCreate a stream processing solution by using Stream Analytics and Azure Event Hubs
-
(35) Community CommentProcess data by using Spark structured streaming
-
(40) Community CommentCreate windowed aggregates
-
(39) Community CommentHandle schema drift
-
(60) Community CommentProcess time series data
-
(34) Community CommentProcess data across partitions
-
(56) Community CommentProcess within one partition
-
(36) Community CommentConfigure checkpoints and watermarking during processing
-
(44) Community CommentScale resources
-
(38) Community CommentCreate tests for data pipelines
-
(36) Community CommentOptimize pipelines for analytical or transactional purposes
-
(25) Community CommentHandle interruptions
-
(41) Community CommentConfigure exception handling
-
(37) Community CommentUpsert data
-
(35) Community CommentReplay archived stream data
-
-
Manage batches and pipelines
-
(33) Community CommentTrigger batches
-
(35) Community CommentHandle failed batch loads
-
(34) Community CommentValidate batch loads
-
(36) Community CommentManage data pipelines in Azure Data Factory or Azure Synapse Pipelines
-
(52) Community CommentSchedule data pipelines in Data Factory or Azure Synapse Pipelines
-
(33) Community CommentImplement version control for pipeline artifacts
-
(35) Community CommentManage Spark jobs in a pipeline
-
-
Secure, monitor, and optimize data storage and data processing (30–35%)
-
Implement data security
-
(36) Community CommentImplement data masking
-
(33) Community CommentEncrypt data at rest and in motion
-
(55) Community CommentImplement row-level and column-level security
-
(37) Community CommentImplement Azure role-based access control (RBAC)
-
(30) Community CommentImplement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2
-
(40) Community CommentImplement a data retention policy
-
(66) Community CommentImplement secure endpoints (private and public)
-
(38) Community CommentImplement resource tokens in Azure Databricks
-
(41) Community CommentLoad a DataFrame with sensitive information
-
(60) Community CommentWrite encrypted data to tables or Parquet files
-
(30) Community CommentManage sensitive information
-
-
Monitor data storage and data processing
-
(31) Community CommentImplement logging used by Azure Monitor
-
(37) Community CommentConfigure monitoring services
-
(30) Community CommentMonitor stream processing
-
(33) Community CommentMeasure performance of data movement
-
(35) Community CommentMonitor and update statistics about data across a system
-
(30) Community CommentMonitor data pipeline performance
-
(35) Community CommentMeasure query performance
-
(35) Community CommentSchedule and monitor pipeline tests
-
(41) Community CommentInterpret Azure Monitor metrics and logs
-
(39) Community CommentImplement a pipeline alert strategy
-
-
Optimize and troubleshoot data storage and data processing
-
(83) Community CommentCompact small files
-
(31) Community CommentHandle skew in data
-
(26) Community CommentHandle data spill
-
(41) Community CommentOptimize resource management
-
(36) Community CommentTune queries by using indexers
-
(43) Community CommentTune queries by using cache
-
(41) Community CommentTroubleshoot a failed Spark job
-
(40) Community CommentTroubleshoot a failed pipeline run, including activities executed in external services
-
-
-
No Video Found!
-
-
-
No Books Found!
-
Leave a Reply
You must be logged in to post a comment.