Top 10 TCS Azure Data Factory & Azure Databricks Interview Questions (2026)

TCS Azure Data Engineer interviews primarily focus on Azure Data Factory (ADF), Azure Databricks, ADLS Gen2, Azure SQL Database, Azure Synapse Analytics, PySpark, Spark architecture, ETL pipelines, and real-world production scenarios. Candidates are expected to explain concepts with practical examples and demonstrate knowledge of optimization, security, and troubleshooting.

In Apache Spark, an action such as collect(), count(), show(), or write() triggers a Job. Each job is divided into one or more Stages based on shuffle boundaries created by wide transformations like groupBy(), join(), or reduceByKey(). Every stage is further divided into multiple Tasks, where each task processes a single partition of data in parallel.

Azure Data Factory provides several activities for data movement, transformation, and orchestration. Copy Activity transfers data between source and destination. Mapping Data Flow performs visual ETL transformations. Notebook Activity executes Azure Databricks notebooks. Stored Procedure Activity executes SQL procedures. Web Activity invokes REST APIs, while Lookup, Get Metadata, Set Variable, ForEach, and If Condition activities help control pipeline execution.

The recommended production approach is to authenticate using a Service Principal with OAuth. Credentials are securely stored in Azure Key Vault or Databricks Secret Scope. After authentication, the ADLS Gen2 container is mounted into Databricks so that it can be accessed like a local file system for reading and writing data.

A palindrome is a string that reads the same from left to right and right to left. The easiest approach is to compare the string with its reverse after converting it to lowercase and removing spaces.

Data governance ensures that enterprise data remains secure, discoverable, and compliant. Microsoft Purview is commonly used for metadata management, data cataloging, lineage tracking, and data classification. ADLS Gen2 uses RBAC and Access Control Lists (ACLs) for authorization, while Delta Lake provides schema enforcement and auditing capabilities to maintain data quality.

Azure SQL Database is a fully managed Platform-as-a-Service database designed for modern cloud applications. Azure SQL Managed Instance provides almost complete SQL Server compatibility, making it ideal for migrating existing on-premises SQL Server applications without major code changes.

Azure SQL Database can be monitored using Query Performance Insight, Azure Monitor, Log Analytics, SQL Auditing, and Dynamic Management Views (DMVs). During troubleshooting, analyze long-running queries, deadlocks, execution plans, CPU utilization, memory usage, and blocking sessions using system views such as sys.dm_exec_requests and sys.dm_exec_query_stats.

Azure Synapse supports both batch and streaming data ingestion. Batch data can be loaded using COPY INTO, PolyBase, or Synapse Pipelines from Azure Data Lake Storage, Blob Storage, or SQL databases. Streaming data is ingested through Event Hubs or Apache Kafka into Spark pools. The ingested data can then be stored in Dedicated SQL Pools, Serverless SQL Pools, or Delta Lake depending on the architecture.