Question 1

Explain the differences between Data Warehouse, Data Lake, and Delta Lake

Accepted Answer

**Data Warehouse**: Structured, schema-on-write; optimized for SQL analytics (Snowflake, BigQuery). High compute cost, fast queries. **Data Lake**: Raw/semi-structured object storage (S3, ADLS); schema-on-read; low cost, flexible. **Delta Lake**: Open-source storage layer on a data lake adding ACID transactions, schema enforcement, time travel, upserts. **Why the distinction**: Warehouses scale compute and storage together; lakes decouple them....

Question 2

Describe the process and use cases of implementing Azure Data Factory pipelines.

Accepted Answer

Architectural process: Linked Services (connections) → Datasets (data shapes) → Pipelines (orchestration) → Activities (Copy, Transform). Triggers run pipelines; IR executes (Azure, SHIR, SSIS). Use cases: Lift-and-shift ETL, Synapse/Blob ingestion, SaaS API ingestion, lake orchestration. Example: Daily trigger → Copy SQL Server (SHIR) to ADLS → Data Flow cleanse → Copy to Synapse. Scalability: Parameterize for reuse; stage large files. Cost: Activity runs, IR runtime....

Question 3

Explain Microsoft Fabric and its use in data integration.

Accepted Answer

Architectural logic: Fabric = unified analytics (engineering, warehouse, science, BI). Data integration: Fabric Pipelines (ADF-based), Dataflows Gen2 (Power Query), OneLake. Use case: Ingest SAP → Dataflows transform → Warehouse → Power BI. Why: Single platform for Microsoft-centric analytics; shortcuts for virtual consolidation. Trade-off: Fabric vs standalone ADF—Fabric for end-to-end; ADF for hybrid/multi-cloud....

Question 4

Explain the difference between Azure Event Hub and Azure Service Bus.

Accepted Answer

Architectural difference: Event Hub = high-throughput ingestion, streaming, pub-sub; retention by time. Service Bus = messaging, queues, topics, dead-letter, sessions, transactions. Use Event Hub for: telemetry, IoT, clickstreams, data pipelines. Use Service Bus for: decoupling, workflows, ordered processing. Trade-off: Event Hub for scale; Service Bus for semantics....

Question 5

Explain the differences between Azure SQL Database, Azure SQL Managed Instance, and Azure Synapse.

Accepted Answer

Architectural difference: SQL Database = single PaaS DB; app backends. Managed Instance = near SQL Server compat; VNet; lift-and-shift. Synapse = analytics; MPP; Spark + SQL; warehouse/lakehouse. Workload: SQL DB for transactional; MI for migration; Synapse for analytics....

Question 6

Explain the purpose and architecture of Azure Synapse Analytics.

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 7

How does Azure Kubernetes Service (AKS) manage scaling and updates for containerized applications?

Accepted Answer

Scaling: HPA by CPU/memory/custom; Cluster Autoscaler for nodes. Updates: Node pool upgrade; rolling update; cordon and drain. Best practice: HPA for apps; Cluster Autoscaler; multiple node pools; test upgrades; PodDisruptionBudgets.

Question 8

What are Azure Blueprints, and how are they different from Azure Policies?

Accepted Answer

**Azure Blueprints**: Package of Azure artifacts (ARM templates, Policy assignments, RBAC, Resource Groups) for repeatable environment deployment. Versioned; assignable at subscription level. Use for landing zones, compliance baselines. **Azure Policy**: Defines rules (allow/deny/audit)—e.g., 'all storage must have encryption', 'only certain regions'. Policy is enforcement; Blueprint is deployment....

Fractal Data Engineer Interview Questions

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Fractal Data Engineer Interview Questions

+2 More Questions with Expert Answers

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