• Describe the key features and benefits of Azure Data Lake Storage Gen2
• Enable Azure Data Lake Storage Gen2 in an Azure Storage account
• Compare Azure Data Lake Storage Gen2 and Azure Blob storage
• Describe where Azure Data Lake Storage Gen2 fits in the stages of analytical processing
• Describe how Azure data Lake Storage Gen2 is used in common analytical workloads

• Identify the business problems that Azure Synapse Analytics addresses.
• Describe core capabilities of Azure Synapse Analytics.
• Determine when to use Azure Synapse Analytics.

• Identify capabilities and use cases for serverless SQL pools in Azure Synapse Analytics
• Query CSV, JSON, and Parquet files using a serverless SQL pool
• Create external database objects in a serverless SQL pool

• Use a CREATE EXTERNAL TABLE AS SELECT (CETAS) statement to transform data.
• Encapsulate a CETAS statement in a stored procedure.
• Include a data transformation stored procedure in a pipeline.

• Understand lake database concepts and components
• Describe database templates in Azure Synapse Analytics
• Create a lake database

• Identify core features and capabilities of Apache Spark.
• Configure a Spark pool in Azure Synapse Analytics.
• Run code to load, analyze, and visualize data in a Spark notebook.

• Use Apache Spark to modify and save dataframes
• Partition data files for improved performance and scalability.
• Transform data with SQL

• Describe core features and capabilities of Delta Lake.
• Create and use Delta Lake tables in a Synapse Analytics Spark pool.
• Create Spark catalog tables for Delta Lake data.
• Use Delta Lake tables for streaming data.
• Query Delta Lake tables from a Synapse Analytics SQL pool.

• Design a schema for a relational data warehouse.
• Create fact, dimension, and staging tables.
• Use SQL to load data into data warehouse tables.
• Use SQL to query relational data warehouse tables.

• Load staging tables in a data warehouse
• Load dimension tables in a data warehouse
• Load time dimensions in a data warehouse
• Load slowly changing dimensions in a data warehouse
• Load fact tables in a data warehouse
• Perform post-load optimizations in a data warehouse

• Describe core concepts for Azure Synapse Analytics pipelines.
• Create a pipeline in Azure Synapse Studio.
• Implement a data flow activity in a pipeline.
• Initiate and monitor pipeline runs.

• Describe notebook and pipeline integration.
• Use a Synapse notebook activity in a pipeline.
• Use parameters with a notebook activity.

• Describe Hybrid Transactional / Analytical Processing patterns.
• Identify Azure Synapse Link services for HTAP.

• Configure an Azure Cosmos DB Account to use Azure Synapse Link.
• Create an analytical store enabled container.
• Create a linked service for Azure Cosmos DB.
• Analyze linked data using Spark.
• Analyze linked data using Synapse SQL.

• Understand key concepts and capabilities of Azure Synapse Link for SQL.
• Configure Azure Synapse Link for Azure SQL Database.
• Configure Azure Synapse Link for Microsoft SQL Server.

• Understand data streams.
• Understand event processing.
• Understand window functions.
• Get started with Azure Stream Analytics.

• Describe common stream ingestion scenarios for Azure Synapse Analytics.
• Configure inputs and outputs for an Azure Stream Analytics job.
• Define a query to ingest real-time data into Azure Synapse Analytics.
• Run a job to ingest real-time data, and consume that data in Azure Synapse Analytics.

• Configure a Stream Analytics output for Power BI.
• Use a Stream Analytics query to write data to Power BI.
• Create a real-time data visualization in Power BI.

• Evaluate whether Microsoft Purview is appropriate for data discovery and governance needs.
• Describe how the features of Microsoft Purview work to provide data discovery and governance.

• Catalog Azure Synapse Analytics database assets in Microsoft Purview.
• Configure Microsoft Purview integration in Azure Synapse Analytics.
• Search the Microsoft Purview catalog from Synapse Studio.
• Track data lineage in Azure Synapse Analytics pipelines activities.

• Provision an Azure Databricks workspace.
• Identify core workloads and personas for Azure Databricks.
• Describe key concepts of an Azure Databricks solution.

• Describe key elements of the Apache Spark architecture.
• Create and configure a Spark cluster.
• Describe use cases for Spark.
• Use Spark to process and analyze data stored in files.
• Use Spark to visualize data.

• Describe how Azure Databricks notebooks can be run in a pipeline.
• Create an Azure Data Factory linked service for Azure Databricks.
• Use a Notebook activity in a pipeline.
• Pass parameters to a notebook.

A: DP-203 training is designed to equip individuals with the necessary skills to become proficient Azure Data Engineers. It covers various topics such as data storage, data processing, data integration, and data security in the Azure environment. The training helps individuals gain the knowledge required to design and implement effective data solutions using Azure data services.

A: DP 203 training is suitable for data professionals who want to become Azure Data Engineers or enhance their existing skills in designing and implementing data solutions using Azure services. It is particularly relevant for individuals involved in tasks such as data integration, data transformation, data storage, and data analytics.

A: To enroll in DP 203 training, Microsoft recommends having a foundational knowledge of Azure fundamentals and basic understanding of data-related concepts and technologies. It is also beneficial to have experience with data processing, data integration, and data analytics.

A: To prepare for DP203, Microsoft offers official training courses, online learning paths, and self-paced study materials. You can explore the Microsoft Learning website to access these resources. Additionally, there are various third-party study guides and practice tests available to help you prepare for the certification exam.

A: The DP203 certification exam consists of multiple-choice questions, case studies, and other question formats that assess your ability to design and implement data solutions using Azure services. The exact number of questions and the duration of the exam may vary, so it’s recommended to check the official Microsoft certification website for the latest information.

A: DP203 certification is valid for two years. After that, you will need to renew your certification by passing a renewal exam or meeting other criteria specified by Microsoft.

A: Earning DP203 certification demonstrates your expertise in designing and implementing data solutions using Azure data services, which can significantly enhance your career prospects as an Azure Data Engineer. It validates your skills and knowledge in working with various data technologies and showcases your ability to provide effective data solutions on the Azure platform.