Question 1

Data access strategy for clients

Accepted Answer

**Strategy**: Provide APIs or views. Options: Delta Sharing, read replicas, data marketplace. Use RBAC; audit access. Tiered access (internal vs. external). **Best practice**: Self-service with governance; document contracts.

Question 2

Describe a project where you implemented a data quality framework.

Accepted Answer

**STAR**: **Situation**: 30% of reports had errors. **Task**: Build validation. **Action**: Great Expectations + dbt tests. Integrated in Airflow. Automated alerts. **Result**: Errors dropped 80%. **Emphasize**: Automated detection; integration with pipeline.

Question 3

Describe a time when you had to work with a difficult stakeholder.

Accepted Answer

**STAR**: **Situation**: VP pushed for unrealistic 2-week deadline for 6-week scope. **Task**: Manage expectations. **Action**: Shared capacity plan; proposed MVP—core in 2 weeks, rest in phases. Presented trade-offs. **Result**: Delivered core on time; built trust; full scope in 4 weeks....

Question 4

Handle dimension changes

Accepted Answer

**SCD Types**: Type 1—overwrite, no history; Type 2—effective_from/to, is_current; Type 3—previous value column. Type 2 most common.

**Implementation**: On change: set effective_to=now() on current; insert new with effective_from=now(), is_current=1. Query: WHERE is_current=1 or effective_from<=@date AND (effective_to IS NULL OR effective_to>@date). Delta merge/upsert....

Question 5

Handling node failures

Accepted Answer

**Strategies**: (1) Replication—data on multiple nodes; (2) Health checks—heartbeats, liveness; (3) Failover—auto promotion (Kafka ISR, Spark); (4) Idempotency—reprocessing = same result; (5) Checkpointing—resume from last.

**Spark**: Driver failover; executor retry. Kafka: partition replication....

Question 6

Mapper and Reducer design for solving Two-Sum

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in General/Other 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

DSA: Array-based problem - brute-force and optimized solutions

Accepted Answer

**Example—two sum:** Brute O(n²) nested loops. Optimized O(n) hash map. **Max subarray (Kadane):** Brute O(n²); Kadane O(n). **Why both:** Show analysis; optimize when needed. **Production:** Document complexity; profile.

Question 8

Create data models for storing users, artists, and related data for music platform

Accepted Answer

**Architectural Logic**: Normalize for writes; denormalize for analytics. **OLTP**: users (user_id PK, email, created_at); artists (artist_id PK, name); tracks (track_id PK, artist_id FK, title, duration); playlists (playlist_id PK, user_id FK, name); playlist_tracks (playlist_id, track_id, position). **Analytics**: fact_listening (user_id, track_id, played_at, duration_played)—partition by played_at. dim_user, dim_artist, dim_track, dim_date....

Question 9

SQL Case study involving bank liabilities, loans, and balances - extensive JOINs and CASE statements

Accepted Answer

Bank case: JOIN liabilities, loans, balances on account/customer. Use CASE for categorization (asset vs liability), status logic. Structure: fact balances, dim products; join on keys. Example: SELECT a.account_id, CASE WHEN b.balance > 0 THEN 'Asset' ELSE 'Liability' END, SUM(l.amount) FROM accounts a JOIN balances b ON a.id = b.account_id JOIN loans l ON a.id = l.account_id GROUP BY 1,2. Use CTEs for clarity....

Question 10

Write a SQL query to find users to recommend specific artists

Accepted Answer

Find users who listened to similar artists but not target: SELECT u.user_id FROM user_listens u JOIN artist_fans a ON u.artist_id = a.artist_id WHERE a.artist_id = :target_artist_id AND u.user_id NOT IN (SELECT user_id FROM user_listens WHERE artist_id = :target_artist_id). **Why**: Affinity-based recommendation. Collaborative filtering uses co-listening; this is simpler....

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