Microsoft DP-203 Exam Topics

Microsoft DP-203 Exam

Data Engineering on Microsoft Azure

Total Questions: 316

Last Updated : 18-04-2024

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 Topics:

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