Microsoft DP-203 Exam Practice Questions *Updated Nov 2023*

The course includes the below concepts for the exam: *Updated Nov 2023*

Design and implement data storage (15–20%)

Implement a partition strategy

• Implement a partition strategy for files

• Implement a partition strategy for analytical workloads

• Implement a partition strategy for streaming workloads

• Implement a partition strategy for Azure Synapse Analytics

• Identify when partitioning is needed in Azure Data Lake Storage Gen2

Design and implement the data exploration layer

• Create and execute queries by using a compute solution that leverages SQL serverless and Spark cluster

• Recommend and implement Azure Synapse Analytics database templates

• Push new or updated data lineage to Microsoft Purview

• Browse and search metadata in Microsoft Purview Data Catalog

Develop data processing (40–45%)

Ingest and transform data

• Design and implement incremental loads

• Transform data by using Apache Spark

• Transform data by using Transact-SQL (T-SQL) in Azure Synapse Analytics

• Ingest and transform data by using Azure Synapse Pipelines or Azure Data Factory

• Transform data by using Azure Stream Analytics

• Cleanse data

• Handle duplicate data

• Avoiding duplicate data by using Azure Stream Analytics Exactly Once Delivery

• Handle missing data

• Handle late-arriving data

• Split data

• Shred JSON

• Encode and decode data

• Configure error handling for a transformation

• Normalize and denormalize data

• Perform data exploratory analysis

Develop a batch processing solution

• Develop batch processing solutions by using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory

• Use PolyBase to load data to a SQL pool

• Implement Azure Synapse Link and query the replicated data

• Create data pipelines

• Scale resources

• Configure the batch size

• Create tests for data pipelines

• Integrate Jupyter or Python notebooks into a data pipeline

• Upsert data

• Revert data to a previous state

• Configure exception handling

• Configure batch retention

• Read from and write to a delta lake

Develop a stream processing solution

• Create a stream processing solution by using Stream Analytics and Azure Event Hubs

• Process data by using Spark structured streaming

• Create windowed aggregates

• Handle schema drift

• Process time series data

• Process data across partitions

• Process within one partition

• Configure checkpoints and watermarking during processing

• Scale resources

• Create tests for data pipelines

• Optimize pipelines for analytical or transactional purposes

• Handle interruptions

• Configure exception handling

• Upsert data

• Replay archived stream data

Manage batches and pipelines

• Trigger batches

• Handle failed batch loads

• Validate batch loads

• Manage data pipelines in Azure Data Factory or Azure Synapse Pipelines

• Schedule data pipelines in Data Factory or Azure Synapse Pipelines

• Implement version control for pipeline artifacts

• Manage Spark jobs in a pipeline

Secure, monitor, and optimize data storage and data processing (30–35%)

Implement data security

• Implement data masking

• Encrypt data at rest and in motion

• Implement row-level and column-level security

• Implement Azure role-based access control (RBAC)

• Implement POSIX-like access control lists (ACLs) for Data Lake Storage Gen2

• Implement a data retention policy

• 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

• Monitor stream processing

• Measure performance of data movement

• Monitor and update statistics about data across a system

• Monitor data pipeline performance

• Measure query performance

• Schedule and monitor pipeline tests

• Interpret Azure Monitor metrics and logs

• Implement a pipeline alert strategy

Optimize and troubleshoot data storage and data processing

• Compact small files

• Handle skew in data

• Handle data spill

• Optimize resource management

• Tune queries by using indexers

• Tune queries by using cache

• Troubleshoot a failed Spark job

• Troubleshoot a failed pipeline run, including activities executed in external services

I hope you find the course useful,

If I can help in any way please message me,

Happy learning!

Thanks,

Neil

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