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Microsoft DP-203 Exam Syllabus

Data Engineering on Microsoft Azure

Last Update Nov 24, 2024
Total Questions : 341

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DP-203 Exam Details

Microsoft DP-203 Exam Detail

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Free DP-203 Exam Questions

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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
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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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