Microsoft Certified : Azure Data Engineer Associate (DP-203)

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

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 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 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