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Home - Lessons - Azure Data Engineering Training - Module 14 — Interview Preparation and Career Development - Azure Data Engineering Interview Question Bank

Azure Data Engineering Training Azure Data Engineering Interview Question Bank
Azure Data Engineering Training
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Module 1 — Data Engineering Foundations and Architecture
Introduction to Data Engineering and the Modern Data Stack
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Data Pipeline Components: Ingestion, Storage, Transformation, Serving
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Batch vs Streaming vs Lambda Architecture
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Cloud Data Engineering on Azure: Overview and Roadmap
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Module 2 — SQL for Data Engineering
Advanced SQL: Joins, Window Functions, CTEs
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Data Cleaning and Transformation with SQL
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Indexing, Partitioning, and Query Optimization
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SQL Interview Patterns for Data Engineers
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Module 3 — Python for Data Engineering
Python Essentials for Data Engineering
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Working with APIs, Files, and Pandas
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PySpark Basics and DataFrame Operations
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Python Interview Patterns and Coding Practice
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Module 4 — Azure Data Lake Storage Gen2
ADLS Gen2 Architecture and Hierarchical Namespace
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Container, Directories, and File Management
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Security: ACLs, RBAC, and Shared Access Signatures
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Integrating ADLS Gen2 with ADF and Databricks
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Module 5 — Azure Data Factory
ADF Pipelines, Datasets, Linked Services, and Integration Runtime
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Data Movement and Transformation Activities
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Parameterization, Dynamic Expressions, and Metadata
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Incremental Load and CDC Patterns in ADF
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Module 6 — Azure Synapse Analytics
Synapse Workspace, SQL Pools, and Spark Pools
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Dedicated SQL Pool: Tables, Indexing, and Distribution
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Synapse Pipelines and Data Flows
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Serverless SQL and External Tables over Data Lake
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Module 7 — Azure Databricks and Apache Spark
Databricks Workspace, Clusters, and Notebooks
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Spark Architecture: RDDs, DataFrames, and Catalyst Optimizer
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Delta Lake: ACID, Time Travel, and Optimize
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Databricks Unity Catalog and Governance
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Module 8 — Microsoft Fabric, Data Warehousing, and Data Modelling
Microsoft Fabric: Lakehouse, Warehouse, and OneLake
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Data Warehouse Design: Star Schema, Snowflake Schema
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Slowly Changing Dimensions and Surrogate Keys
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Dimensional Modeling Best Practices
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Module 9 — ETL, ELT, CDC, and Metadata-Driven Pipelines
ETL vs ELT: When to Use Which
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Change Data Capture with Azure SQL and Debezium Patterns
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Metadata-Driven Pipeline Design
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Incremental Data Loading Strategies
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Module 10 — Batch and Streaming Data Engineering
Batch Processing Patterns and Scheduling
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Streaming Concepts: Windowing, Watermarking, Checkpointing
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Azure Event Hubs: Producers, Consumers, and Partitions
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Spark Structured Streaming on Azure
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Module 11 — Security, Governance, Quality, and Observability
Azure RBAC, Managed Identity, and Azure Key Vault
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Data Security: Encryption, Masking, and PII Handling
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Data Quality Frameworks and Great Expectations
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Data Observability and Governance with Microsoft Purview
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Module 12 — DevOps, Monitoring, Performance, and Cost Optimization
Git, Branching Strategies, and Code Reviews
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CI/CD with Azure DevOps for Data Pipelines
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Pipeline Monitoring, Alerting, and Troubleshooting
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Performance Tuning and Cost Optimization on Azure
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Module 13 — Real-World Projects and Production Architecture
Retail Data Engineering Pipeline: End-to-End Build
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Customer 360 Data Platform: Design and Implementation
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Real-Time IoT Data Pipeline with Event Hubs and Spark
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Metadata-Driven Enterprise Pipeline Framework
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Module 14 — Interview Preparation and Career Development
Azure Data Engineering Interview Question Bank
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SQL and Python Interview Rounds
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Project Explanation and Portfolio Building
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Resume, LinkedIn, and Job Search Strategy
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Announcements Course Info
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