Amazon VO: Behavioral Deep Dive + Document Processing Under Limited Capacity + Splitting Alexa Conversations into Sessions

Amazon's VO interviewers really dig deep! Behavioral, system design and coding all came with follow-ups, but it went fine overall.

Behavioral

The main topics:

• What do you do when requirements keep changing? For example, a new requirement shows up halfway through a project (scope creep) — how did you handle it?
• How do you make trade-offs under a tight deadline? For example, did you use temporary workarounds or isolate traffic to ship on time?
• Have you proactively asked your manager for resources? For example, when you realized the work wouldn't fit, did you talk to your manager and re-prioritize?

System design

Problem: you can only process a limited number of requests per second but have a massive amount of documents. How do you maximize the useful output?

My approach:

• Process in batches by priority
• Deduplicate documents
• Use caching
• Queue and rate-limit
• Adjust batch sizes dynamically

That squeezes the most out of the limited capacity.

Coding: split Alexa conversations into sessions

Problem: split a user's conversation with Alexa into "sessions" by time. Rule: utterances no more than 60 seconds apart belong to the same session; a gap over 60 seconds starts a new one.

Solution: sort all records by time, then look at the gap between each pair of neighbors. If it's at most 60 seconds, add to the current session; otherwise close it and start a new one. Return the list of sessions.

def split_sessions(records, gap=60):
    """records: [(timestamp, text)]; returns a list of sessions, each a time-ordered list of records"""
    sessions = []
    for rec in sorted(records, key=lambda r: r[0]):
        if sessions and rec[0] - sessions[-1][-1][0] <= gap:
            sessions[-1].append(rec)
        else:
            sessions.append([rec])
    return sessions

Follow-up: what if utterances arrive out of order?

Question: if utterances don't arrive in order but trickle in (not necessarily at the end), how do you put each one into the right session?

My answer in the interview: go through the existing sessions and compare the new record's time with each session's last record. If it's within 60 seconds, append it; if none match, start a new session.

Further optimization: sessions are ordered by start time, so binary search can find the two sessions around the new record — O(log S) instead of scanning them all. One case that's easy to miss: the new record may fill the gap between two sessions, in which case the two must be merged into one.

from bisect import bisect_right

def insert_record(sessions, rec, gap=60):
    """sessions are sorted by start time; inserts a new record in place"""
    t = rec[0]
    i = bisect_right([s[0][0] for s in sessions], t)    # sessions[i-1] starts at or before t
    join_prev = i > 0 and t <= sessions[i - 1][-1][0] + gap
    join_next = i < len(sessions) and sessions[i][0][0] - t <= gap
    if join_prev and join_next:                         # fills the gap: merge both sides
        sessions[i - 1:i + 1] = [sessions[i - 1] + [rec] + sessions[i]]
        sessions[i - 1].sort(key=lambda r: r[0])
    elif join_prev:
        sessions[i - 1].append(rec)
        sessions[i - 1].sort(key=lambda r: r[0])
    elif join_next:
        sessions[i].insert(0, rec)
    else:
        sessions.insert(i, [rec])