Adobe·Machine Learning Engineer·Technical Phone Screen·Intermediate

IntermediatePrefer not to say

Apr 2026

Summary

Adobe ML engineer round, one coding problem that looked like a straightforward prefix-sum thing but had enough edge cases to make me sweat. The follow-up about streaming caught me completely flat-footed.

Questions Asked (2)

Given an integer array and an integer k, determine if there's a contiguous subarray of length at least 2 whose sum is divisible by k. If k is zero, find a subarray with sum exactly zero instead. Walk through your approach and give the time and space complexity.

Algorithms & Data Structures

Author's notes

I knew prefix sums were involved but fumbled the modular arithmetic part for a bit.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Use prefix sums and a hash map to track remainders modulo k, checking if the same remainder appears at least two indices apart. For k=0, use a hash set to detect if any prefix sum repeats, indicating a zero-sum subarray. This yields O(n) time and O(min(n, k)) space.

Pro tip: Explicitly handle edge cases like k=0, negative numbers, and subarray length constraints; mention that the hash map approach works because if two prefix sums have the same remainder modulo k, their difference is divisible by k.

1. Clarify problem and constraints

Restate the problem: find a contiguous subarray of length ≥2 with sum divisible by k, or sum exactly zero if k=0. Confirm assumptions about input size, integer range, and k's sign.

2. Handle k=0 separately

For k=0, use a hash set to store prefix sums; if a prefix sum repeats, the subarray between the two occurrences sums to zero. Ensure the subarray length is at least 2.

3. Use prefix sums and remainders for k≠0

Compute prefix sums modulo k. If the same remainder appears at indices i and j with j - i ≥ 2, then the subarray from i+1 to j has sum divisible by k. Use a hash map to store the first occurrence of each remainder.

4. Implement and test

Write code that iterates through the array, updating the prefix sum and checking the hash map. Test with cases like [1,2,3], k=3; [0,0], k=0; and negative numbers.

5. Analyze complexity

Time complexity is O(n) because we traverse the array once. Space complexity is O(min(n, k)) for the hash map, as there are at most k distinct remainders.

Key Points to Mention

Prefix sum technique and its role in subarray sum problems
Modulo arithmetic and the pigeonhole principle for remainders
Handling k=0 as a special case with a hash set
Ensuring subarray length is at least 2 by checking index difference
Time and space complexity analysis: O(n) time, O(min(n, k)) space
Edge cases: negative numbers, zeros, and large k

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

How would you adapt your solution if the array arrives as a stream, one element at a time, rather than all at once?

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

Blanked.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by clarifying the problem constraints: what operation is needed (e.g., find max, compute mean, detect anomaly) and whether the stream is bounded or unbounded. Then explain how to adapt the algorithm to a streaming setting, focusing on maintaining state incrementally and discussing trade-offs between memory, accuracy, and latency.

Pro tip: Mention that for unbounded streams, you often need to switch from exact algorithms to approximate ones (e.g., reservoir sampling, count-min sketch) and that Adobe values scalable ML pipelines, so highlight how your choice impacts downstream model training or inference.

1. Clarify the problem and constraints

Ask whether the stream is finite or infinite, what the required output is (e.g., exact vs approximate), and what resources (memory, time) are available.

2. Identify the core operation and its streaming adaptation

Determine if the operation is decomposable (e.g., sum, max) or requires full data (e.g., median). For decomposable operations, maintain a running aggregate; for others, consider approximations or windowing.

3. Choose appropriate data structures and algorithms

Select streaming-friendly structures like heaps for top-k, reservoir sampling for uniform sampling, or sketches for frequency/quantile estimation, and explain why they fit.

4. Discuss trade-offs and practical considerations

Compare memory usage, accuracy, and latency of your streaming approach versus batch processing, and mention how you would handle concept drift or late-arriving data.

5. Relate to ML engineering context

Explain how the streaming solution integrates with ML pipelines (e.g., online learning, feature updates) and why it matters for Adobe's products.

Key Points to Mention

Streaming algorithms: reservoir sampling, count-min sketch, HyperLogLog, t-digest
Trade-offs: exact vs approximate, memory vs accuracy, latency vs throughput
Windowing techniques: sliding window, tumbling window, decayed window
Handling unbounded streams: bounded memory, incremental updates
Online learning and model updates in ML pipelines
Concept drift and adaptive algorithms

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.