Question 1

How do you prioritize tasks when handling multiple projects with tight deadlines?

Accepted Answer

Situation: I managed 4 projects with overlapping deadlines. Task: Deliver on all without dropping balls. Action: I used an Eisenhower matrix; mapped dependencies—migration foundation unblocked 2 others. I published a shared backlog with project tags; discussed trade-offs with stakeholders. I timeboxed lower-priority work and batched meetings. When overwhelmed, I escalated....

Question 2

Share a time when you explained a technical concept to a non-technical stakeholder.

Accepted Answer

Situation: VP Product asked why pipeline was delayed. Task: Translate without dumbing down. Action: I used analogy—'traffic doubled on the data highway; we're adding lanes and optimizing route.' Shared simple dashboard. Followed up with one-pager: timeline, impact, mitigation....

Question 3

Explain how you would implement a caching mechanism for frequently accessed video metadata.

Accepted Answer

**Why Cache**: Video metadata (title, duration, thumbnail) is read-heavy; DB would bottleneck at scale.

**Design**: (1) Multi-tier—Redis/Memcached for hot keys; TTL 1-24h; (2) Cache-aside—check cache, on miss load from DB, populate; (3) Invalidation—on update, invalidate/update entry; (4) Warming—preload popular during low traffic; (5) Partition by content_id for Disney+ scale.

**Cost**: Reduces DB load; Redis cost vs. DB cost....

Question 4

Implement a rate-limiter to control API requests per user.

Accepted Answer

Rate limit = restrict requests per user per window. ALGORITHMS: (1) Token bucket—refill rate, consume per request; smooths bursts. (2) Sliding window—exact count in window; more accurate, more state. (3) Fixed window—simple but burst at boundaries. WHY: Protect backends, ensure fair usage, cost control. IMPLEMENTATION: Redis for distributed state—INCR + EXPIRE (fixed); Lua script (sliding)....

Question 5

Solve Longest Consecutive Sequence.

Accepted Answer

Hash set; only count from sequence start (no n-1 in set). Python: s=set(nums); best=0; for n in s: if n-1 not in s: c,x=1,n; while x+1 in s: c+=1; x+=1; best=max(best,c); return best. Key: only start when n-1 not in set (avoids recounting)....

Question 6

Solve Minimum Remove to Make Valid Parentheses.

Accepted Answer

Stack: track indices of '('; for ')', pop if stack else mark for removal; remaining stack also removed. Python: stack,remove=[],set(); [stack.append(i) if c=='(' else (stack.pop() if stack else remove.add(i)) for i,c in enumerate(s) if c in '()']; remove.update(stack); return ''.join(c for i,c in enumerate(s) if i not in remove). Or: for i,c: if c=='(': stack.append(i); elif c==')': stack.pop() if stack else remove.add(i); remove.update(stack)....

Question 7

What techniques would you use to ensure data consistency in a distributed database?

Accepted Answer

**Techniques:** (1) 2PC—coordinator commits/aborts all. (2) Saga—compensating tx for eventual consistency. (3) Distributed tx (Percolator/Spanner). (4) Eventual + conflict resolution (CRDTs, vector clocks)....

Question 8

Design an algorithm to merge k sorted lists of video streaming data.

Accepted Answer

**Section 1 — The Context (The 'Why')**
The primary challenge for this design in Python/Coding 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 9

Given an n-ary tree, write code to flatten it and store the output in a list.

Accepted Answer

**DFS:** def dfs(node): result.append(node.val); for child in node.children: dfs(child). **BFS:** Queue. **Why:** Preorder flatten. **Production:** Handle None children.

Question 10

Implement an algorithm to find the longest common prefix among an array of strings.

Accepted Answer

**Why Prefix Matters in Data Engineering:** Common in key design (e.g., S3 prefix for partitioning), schema evolution (shared column prefixes), and deduplication logic.

**Scalability Trade-offs:** (1) Horizontal scanning: O(S) where S = total chars—optimal for small-to-medium. (2) Vertical scanning: stops early on first mismatch—better when strings diverge quickly. (3) Sort-and-compare: O(n log n * m)—avoids full scan but sort dominates for large n....

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