Question 1

Business generates TBs of data daily. How would you design the data pipeline in Azure?

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in Cloud/Tools is balancing scale, cost, and reliability. At scale, naive approaches fail: single points of failure cause cascades, schema evolution breaks consumers, and over-provisioning explodes cost. Failure modes include silent data loss from non-idempotent writes, cascading job failures from tight coupling, and operational burden from manual intervention....

Question 2

Explain Snowpipe as a continuous data ingestion service.

Accepted Answer

Architectural logic: Snowpipe = event-driven or scheduled load from stages (S3, GCS, Azure) into Snowflake. Why: Near real-time without manual COPY; serverless. Flow: Files land in stage → Snowpipe triggers load. Scalability: Auto-scales; typically minutes latency. Cost: Per compute-second; batch smaller files....

Question 3

How would you copy files and folders present in Azure Data Lake Storage (ADLS)?

Accepted Answer

Options: (1) ADF Copy Data with ADLS Gen2 source; path or wildcard (folder/**). (2) GetMetadata + ForEach for per-file. (3) AzCopy for bulk; orchestrate via ADF or script. Best practice: Wildcard for bulk; ForEach for custom logic; stage if transforming.

Question 4

What is Unity Catalog, and how is it implemented in your project?

Accepted Answer

**Unity Catalog**: Central governance for Databricks—metadata, access control, lineage, audit. Metastore → Catalog → Schema → Table. RBAC via grants; row filters and column masks for PII. **Implementation**: Catalog per environment (dev, prod) or per domain. Schemas group tables. Managed tables for durability. Grant SELECT to analysts, MODIFY to engineers. Enable lineage; use row filters for PII tables. Sync groups from IdP....

Cognizant Cloud & Tools Interview Questions

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Cognizant Cloud & Tools Interview Questions

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