1. Home
  2. Microsoft
  3. DP-203 Exam Syllabus

Microsoft DP-203 Exam Syllabus

Microsoft DP-203 Exam

Data Engineering on Microsoft Azure

Total Questions: 241

What is Included in the Microsoft DP-203 Exam?

Authentic information about the syllabus and an effective study guide is essential to go through the Microsoft DP-203 exam in the first attempt. The study guide of Study4Exam provides you with comprehensive information about the syllabus of the Microsoft DP-203 exam. You should get this information at the start of your preparation because it helps you make an effective study plan. We have designed this Microsoft Azure Data Engineer Associate certification exam preparation guide to give the exam overview, practice questions, practice test, prerequisites, and information about exam topics that help to go through the Microsoft Data Engineering on Microsoft Azure (2022) exam. We recommend you to the preparation material mentioned in this study guide to cover the entire Microsoft DP-203 syllabus. Study4Exam offers 3 formats of Microsoft DP-203 exam preparation material. Each format provides new practice questions in PDF format, web-based and desktop practice exams to get passing marks in the first attempt.

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:

Microsoft DP-203 exam questions and practice test are the best ways to get fully prepared. Study4exam's trusted preparation material consists of both practice questions and practice test. To pass the actual Azure Data Engineer Associate DP-203 exam on the first attempt, you need to put in hard work on these Microsoft DP-203 questions that provide updated information about the entire exam syllabus. Besides studying actual questions, you should take the Microsoft DP-203 practice test for self-assessment and actual exam simulation. Revise actual exam questions and remove your mistakes with the Data Engineering on Microsoft Azure DP-203 exam practice test. Online and windows-based formats of the DP-203 exam practice test are available for self-assessment.