Question 1

Explain the differences between Repartition and Coalesce. When would you use each?

Accepted Answer

**Repartition(n)**: Full shuffle; creates exactly n partitions. Can increase or decrease. **Coalesce(n)**: Merges partitions without full shuffle; only decreases. **Why it matters**: Shuffle is expensive—network and disk I/O. Coalesce avoids shuffle when reducing partitions by merging within existing partitions. **When Repartition**: Increasing partitions, fixing skew (repartition by key), or before a join to align partition counts....

Question 2

Explain the types of triggers in ADF, including schedule, tumbling window, and event-based triggers.

Accepted Answer

Schedule: Fixed cadence (cron, every N mins). Predictable batch windows; simple ops. Tumbling window: Fixed non-overlapping intervals; fires once per window. Ideal for idempotent, exactly-once semantics—no overlap means no double-processing. Event-based: Fires on blob created, queue message, etc. Enables near real-time pipelines....

Question 3

Joins and window functions - INNER, LEFT, RIGHT, FULL OUTER, ROW_NUMBER(), RANK(), DENSE_RANK()

Accepted Answer

Joins: INNER = intersection only; LEFT = all left + matching right (NULL fill); RIGHT = mirror of LEFT; FULL OUTER = union of both. Why it matters: Join choice affects result cardinality and NULL handling—wrong join = wrong business logic (e.g., LEFT to preserve all customers even without orders). Window functions: ROW_NUMBER() = unique rank 1,2,3; RANK() = ties same rank, gaps after; DENSE_RANK() = ties same rank, no gaps....

Question 4

Can you explain the architecture of Apache Spark and its components?

Accepted Answer

**Section 1 — The Context (The 'Why')**
Apache Spark's distributed execution model faces the core challenge of coordinating hundreds of executors while avoiding driver bottlenecks and shuffle storms. At scale, the driver's single-threaded scheduling and result aggregation become failure points....

Question 5

Provide a detailed walkthrough of your career journey

Accepted Answer

Situation: Behavioral deep dive. Task: Narrative with progression. Action: I walked through: (1) Backend/DB foundations, (2) First data role—batch ETL, dimensional modeling, (3) Scale—Spark, cloud, real-time, (4) Platform—self-serve tooling, quality frameworks, (5) Leadership—mentoring, RFCs, open source. Each step tied to skills and impact....

Question 6

Share examples of successful stakeholder communication

Accepted Answer

Situation: Stakeholders needed visibility without meeting overload. Task: Structured, async communication. Action: (1) Weekly data quality one-pagers, (2) Architecture review decks before major changes, (3) Slack channels per domain with SLAs, (4) Proactive incident updates (15-min rule), (5) Shared roadmaps in Confluence, (6) Glossaries for self-serve....

Question 7

Difference between pipelines and data flows in ADF

Accepted Answer

Architectural logic: Pipelines = orchestration (activities, flow); Data Flows = Spark-based transformation activity. Pipelines orchestrate; Data Flows transform. A pipeline can contain Copy, Lookup, Data Flow, etc. When to use: Copy for simple move; Data Flow for filter, join, aggregate. Scalability: Data Flow runs on Spark; tune partitions. Cost: Data Flow consumes more than Copy....

Question 8

Fabric dataflows vs. ADF dataflows

Accepted Answer

Architectural difference: Fabric Dataflows = Power Query–style, Fabric-native, OneLake. ADF Dataflows = Spark-based, ADF-native, Azure-focused. When: Fabric for Fabric-centric, Power Query users. ADF when already in ADF, need Azure integration....

Nihilent Data Engineer Interview Questions

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Nihilent Data Engineer Interview Questions

Reading isn't practice. Get AI feedback on your answers.

Other Companies