What This Is
37 production scenarios, plus 3 advanced variants, covering what Azure data engineering interviews actually ask: - incremental loads and metadata-driven ADF pipelines; - schema changes, REST API ingestion and duplicate files; - Databricks performance and Delta upserts; - dimensional modelling and slow Synapse reports; - monitoring, PII security, disaster recovery, cost spikes and CI/CD across environments. Each scenario gives the possible approaches, the recommended answer and why, the follow-ups and the mistakes to avoid. A quick-fire table of ADF follow-ups and reference appendices are included. Updated for Azure services as of 2026, with Microsoft Fabric noted where it is now a natural alternative.
What You'll Learn
- Design incremental and metadata-driven ingestion in Azure Data Factory
- Handle schema drift, late data, duplicates and failed re-runs safely
- Optimise Databricks jobs on ADLS and explain Delta upserts
- Model data for reporting and tune slow Synapse queries
- Explain security with Key Vault, managed identity, access control and masking
- Answer disaster recovery, cost and CI/CD questions with confidence
What's Inside
1 document- DataVerse Hub Azure Data Engineering Scenario QuestionsPDF
What You Get
- 61-page PDF with 37 scenarios and 3 advanced variants in 6 topic groups
- A short answer, possible approaches and a recommended answer for every scenario
- Likely follow-ups and mistakes to avoid
- A quick-fire table of ADF follow-up questions
- Reference appendices, including a cloud service mapping for AWS and GCP
- Free updates to this edition
Who It's For
- Azure data engineers with 2 to 8 years of experience preparing for a switch
- ADF and Databricks developers moving into senior roles
- Engineers from AWS or GCP preparing for Azure-focused roles
What You Need First
- Hands-on exposure to Azure Data Factory or Databricks
- Basic SQL and PySpark
- General understanding of data lakes and ETL/ELT
What Learners Say
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