Exam Topics

Design and Implement Data Storage (40-45%)

Design a data storage structure

Design a partition strategy

Design the serving layer

Implement physical data storage structures

Implement logical data structures

Implement the serving layer

Ingest and transform data

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/IPython 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

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

Optimize and troubleshoot data storage and data processing