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

Design & Implement Data Storage (15-20%)

Master the fundamentals of data storage in Azure Data Lake Storage Gen2. This includes:

• Partitioning Strategies: Learn to effectively partition files for optimal performance, tailored to both file and analytical workloads.
• When to Partition: Identify scenarios where partitioning is crucial for efficient data management.
• Data Security: Implement robust data security measures, including encryption at rest and in transit, row-level and column-level security, and Azure Role-Based Access Control (RBAC).
• Data Retention: Establish and enforce data retention policies.
• Secure Endpoints: Configure secure endpoints (private and public) for data access.
• Resource Tokens: Utilize resource tokens in Azure Databricks for enhanced security.
• POSIX-like ACLs: Implement POSIX-like access control lists for Data Lake Storage Gen2.

Data Exploration & Processing (40-45%)

Gain expertise in data exploration and processing techniques:

• Data Ingestion & Transformation: Ingest and transform data using various methods.
• Apache Spark: Transform data using Apache Spark.
• Transact-SQL (T-SQL): Transform data using T-SQL in Azure Synapse Analytics.
• Azure Synapse Pipelines & Data Factory: Leverage Azure Synapse Pipelines and Azure Data Factory for data ingestion and transformation.
• Azure Stream Analytics: Ingest and transform data using Azure Stream Analytics.
• Data Cleansing: Implement data cleansing techniques to handle duplicate, missing, and late-arriving data.
• Data Splitting & Shredding: Split and shred JSON data for efficient processing.
• Data Encoding & Decoding: Encode and decode data formats.
• Error Handling: Configure robust error handling for data transformations.
• Data Normalization & Denormalization: Normalize and denormalize data for optimal storage and retrieval.
• Data Exploratory Analysis: Perform data exploratory analysis to uncover insights.
• Batch Processing: Develop and implement batch processing solutions using Azure Data Lake Storage, Azure Databricks, Azure Synapse Analytics, and Azure Data Factory.
• PolyBase: Utilize PolyBase to load data into SQL pools.
• Azure Synapse Link: Implement Azure Synapse Link to query replicated data.
• Data Pipelines: Create and manage data pipelines, including scaling resources, configuring batch sizes, and integrating Jupyter/Python notebooks.
• Upsert & Revert: Implement upsert operations and the ability to revert data to previous states.
• Exception Handling: Configure exception handling for data pipelines.
• Delta Lake: Read from and write to Delta Lake for reliable data management.
• Stream Processing: Develop stream processing solutions using Stream Analytics and Azure Event Hubs.
• Spark Structured Streaming: Process data using Spark Structured Streaming.
• Windowed Aggregates: Handle windowed aggregates for time-based data analysis.
• Schema Drift: Manage schema drift in streaming data.
• Time Series Data: Process time series data effectively.
• Data Partitioning: Process data across partitions.
• Checkpoints & Watermarking: Configure checkpoints and watermarking during processing.
• Pipeline Testing: Create comprehensive tests for data pipelines.
• Data Pipeline Optimization: Optimize pipelines for analytical and transactional workloads.
• Interruption Handling: Handle pipeline interruptions gracefully.
• Data Replay: Replay archived stream data.
• Batch Management: Manage batches and pipelines effectively.
• Pipeline Scheduling: Schedule data pipelines in Azure Data Factory or Azure Synapse Pipelines.
• Version Control: Implement version control for pipeline artifacts.
• Spark Job Management: Manage Spark jobs within a pipeline.

Secure, Monitor & Optimize Data Storage and Processing (30-35%)

Ensure the security, performance, and reliability of your data storage and processing systems:

• Data Security Implementation: Implement comprehensive data security measures.
• Data Masking: Mask sensitive data to protect privacy.
• Performance Monitoring: Monitor data storage and data processing performance.
• Azure Monitor: Implement logging using Azure Monitor.
• Performance Metrics: Measure performance of data movement and data pipelines.
• Data Statistics: Monitor and update statistics about data across a system.
• Query Performance: Measure and optimize query performance.
• Pipeline Alerting: Implement a pipeline alert strategy.
• Performance Optimization: Optimize and troubleshoot data storage and data processing.
• File Optimization: Compact small files and handle data skew.
• Resource Management: Optimize resource management for efficient utilization