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Microsoft DP-203 Exam Syllabus

Microsoft DP-203 Exam

Data Engineering on Microsoft Azure

Total Questions: 303

What is Included in the Microsoft DP-203 Exam?

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Microsoft DP-203 Exam Overview :

Exam Name Data Engineering on Microsoft Azure
Exam Code DP-203
Exam Registration Price $165
Official Information https://docs.microsoft.com/en-us/learn/certifications/exams/dp-203
See Expected Questions Microsoft DP-203 Expected Questions in Actual Exam
Take Self-Assessment Use Microsoft DP-203 Practice Test to Assess your preparation - Save Time and Reduce Chances of Failure

Microsoft DP-203 Exam Topics :

Section Weight Objectives
Design and Implement Data Storage 40-45% Design a data storage structure
  •     design an Azure Data Lake solution
  •     recommend file types for storage
  •     recommend file types for analytical queries
  •     design for efficient querying
  •     design for data pruning
  •     design a folder structure that represents the levels of data transformation
  •     design a distribution strategy
  •     design a data archiving solution
Design a partition strategy
  •     design a partition strategy for files
  •     design a partition strategy for analytical workloads
  •     design a partition strategy for efficiency/performance
  •     design a partition strategy for Azure Synapse Analytics
  •     identify when partitioning is needed in Azure Data Lake Storage Gen2
Design the serving layer
  •     design star schemas
  •     design slowly changing dimensions
  •     design a dimensional hierarchy
  •     design a solution for temporal data
  •     design for incremental loading
  •     design analytical stores
  •     design metastores in Azure Synapse Analytics and Azure Databricks
Implement physical data storage structures
  •     implement compression
  •     implement partitioning
  •     implement sharding
  •     implement different table geometries with Azure Synapse Analytics pools
  •     implement data redundancy
  •     implement distributions
  •     implement data archiving
Implement logical data structures
  •     build a temporal data solution
  •     build a slowly changing dimension
  •     build a logical folder structure
  •     build external tables
  •     implement file and folder structures for efficient querying and data pruning
Implement the serving layer
  •     deliver data in a relational star schema
  •     deliver data in Parquet files
  •     maintain metadata
  •     implement a dimensional hierarchy
Design and Develop Data Processing 25-30% Ingest and transform data
  •     transform data by using Apache Spark
  •     transform data by using Transact-SQL
  •     transform data by using Data Factory
  •     transform data by using Azure Synapse Pipelines
  •     transform data by using Stream Analytics
  •     cleanse data
  •     split data
  •     shred JSON
  •     encode and decode data
  •     configure error handling for the transformation
  •     normalize and denormalize values
  •     transform data by using Scala
  •     perform data exploratory analysis
Design and develop a batch processing solution
  •     develop batch processing solutions by using Data Factory, Data Lake, Spark, Azure Synapse Pipelines, PolyBase, and Azure Databricks
  •     create data pipelines
  •     design and implement incremental data loads
  •     design and develop slowly changing dimensions
  •     handle security and compliance requirements
  •     scale resources
  •     configure the batch size
  •     design and create tests for data pipelines
  •     integrate Jupyter/Python notebooks into a data pipeline
  •     handle duplicate data
  •     handle missing data
  •     handle late-arriving data
  •     upsert data
  •     regress to a previous state
  •     design and configure exception handling
  •     configure batch retention
  •     design a batch processing solution
  •     debug Spark jobs by using the Spark UI
Design and develop a stream processing solution
  •     develop a stream processing solution by using Stream Analytics, Azure Databricks, and Azure Event Hubs
  •     process data by using Spark structured streaming
  •     monitor for performance and functional regressions
  •     design and create windowed aggregates
  •     handle schema drift
  •     process time series data
  •     process across partitions
  •     process within one partition
  •     configure checkpoints/watermarking during processing
  •     scale resources
  •     design and create tests for data pipelines
  •     optimize pipelines for analytical or transactional purposes
  •     handle interruptions
  •     design and configure exception handling
  •     upsert data
  •     replay archived stream data
  •     design a stream processing solution
Manage batches and pipelines
  •     trigger batches
  •     handle failed batch loads
  •     validate batch loads
  •     manage data pipelines in Data Factory/Synapse Pipelines
  •     schedule data pipelines in Data Factory/Synapse Pipelines
  •     implement version control for pipeline artifacts
  •     manage Spark jobs in a pipeline
Design and Implement Data Security 10-15% Design security for data policies and standards
  •     design data encryption for data at rest and in transit
  •     design a data auditing strategy
  •     design a data masking strategy
  •     design for data privacy
  •     design a data retention policy
  •     design to purge data based on business requirements
  •     design Azure role-based access control (Azure RBAC) and POSIX-like Access Control List (ACL) for Data Lake Storage Gen2
  •     design row-level and column-level security
Implement data security
  •     implement data masking
  •     encrypt data at rest and in motion
  •     implement row-level and column-level security
  •     implement Azure RBAC
  •     implement POSIX-like ACLs for Data Lake Storage Gen2
  •     implement a data retention policy
  •     implement a data auditing strategy
  •     manage identities, keys, and secrets across different data platform technologies
  •     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

Monitor and Optimize Data Storage and Data Processing 10-15% Monitor data storage and data processing
  •     implement logging used by Azure Monitor
  •     configure monitoring services
  •     measure performance of data movement
  •     monitor and update statistics about data across a system
  •     monitor data pipeline performance
  •     measure query performance
  •     monitor cluster performance
  •     understand custom logging options
  •     schedule and monitor pipeline tests
  •     interpret Azure Monitor metrics and logs
  •     interpret a Spark directed acyclic graph (DAG)
Optimize and troubleshoot data storage and data processing
  •     compact small files
  •     rewrite user-defined functions (UDFs)
  •     handle skew in data
  •     handle data spill
  •     tune shuffle partitions
  •     find shuffling in a pipeline
  •     optimize resource management
  •     tune queries by using indexers
  •     tune queries by using cache
  •     optimize pipelines for analytical or transactional purposes
  •     optimize pipeline for descriptive versus analytical workloads
  •     troubleshoot a failed spark job
  •     troubleshoot a failed pipeline run

Updates in the Microsoft DP-203 Exam Syllabus:

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