Decagon Software Engineer Phone Screen Questions
4+ questions from real Decagon Software Engineer Phone Screen rounds, reported by candidates who interviewed there.
What does the Decagon Phone Screen round test?
The Decagon phone screen typically lasts 45-60 minutes and evaluates core Software Engineer fundamentals. Candidates should expect 1-2 algorithmic problems, basic system design discussion at senior levels, and questions about relevant experience. The goal is to confirm technical competence before bringing candidates onsite.
Top Topics in This Round
Decagon Software Engineer Phone Screen Questions
## Problem A hidden cell is placed uniformly at random in an N x M grid. You can query a row or a column; the oracle tells you whether the cell is in that row/column. Devise a strategy to locate the cell using the minimum worst-case number of queries, then implement it. ```python def locate_cell( n: int, m: int, oracle # callable: oracle('row', i) -> bool, oracle('col', j) -> bool ) -> tuple[int, int]: pass ``` ``` Example (n=4, m=4): Binary search on rows: ceil(log2(4))=2 queries -> narrow to 1 row Binary search on cols: 2 queries -> narrow to 1 col Total: 4 queries worst case Naive: query each row then each col -> up to n+m-1 queries = 7 ``` ## Follow-ups 1. Prove that binary search on rows then columns is optimal in terms of worst-case query count. 2. If the oracle is noisy (lies with probability p), how would you adapt the strategy? 3. How many expected queries does random probing require before finding the cell? 4. Extend to a 3D grid (N x M x D cube) — what is the optimal worst-case query count?
## Problem "Mado" (window in Japanese) average: given an integer array and an integer `k`, compute the moving average using a window of size `k`. Also implement a streaming version that processes one element at a time. ```python def mado_average(nums: list[float], k: int) -> list[float]: """Return list of averages. First k-1 entries use all available elements.""" pass class StreamingMadoAverage: def __init__(self, k: int): ... def next(self, val: float) -> float: """Return current window average after adding val.""" ... ``` ``` Input: nums = [1, 3, 5, 7, 9], k = 3 Output: [1.0, 2.0, 3.0, 5.0, 7.0] # Window [1]->1, [1,3]->2, [1,3,5]->3, [3,5,7]->5, [5,7,9]->7 Streaming: k=3, next(1)->1.0, next(3)->2.0, next(5)->3.0, next(7)->5.0 ``` ## Follow-ups 1. How do you maintain O(1) per element in the streaming version using a deque and running sum? 2. What happens to floating-point precision after many additions? How would you mitigate drift? 3. Extend to a weighted moving average where recent elements have higher weight. 4. How would you compute the moving median instead of the moving mean efficiently?
## Problem Compute or process the skyline silhouette given a set of building rectangles. ## Likely LeetCode equivalent LeetCode 218 - The Skyline Problem ## Tags coding, stack, heap, geometry, phone
## Problem You have a single-threaded CPU running `n` functions (0-indexed). You are given a log of start and end events. Compute the exclusive execution time of each function (time spent in that function, not counting nested calls). Log format: `"function_id:start|end:timestamp"` ```python def exclusive_time(n: int, logs: list[str]) -> list[int]: """Return list of length n with exclusive time for each function.""" pass ``` ``` Input: n = 2 logs = ["0:start:0","1:start:2","1:end:5","0:end:6"] Output: [3, 4] # Function 0: runs [0,1] and [6,6] -> 2+1=3 # Function 1: runs [2,5] -> 4 Input: n = 1 logs = ["0:start:0","0:start:2","0:end:5","0:end:6"] Output: [7] ``` ## Follow-ups 1. How does a stack naturally model the nesting of function calls here? 2. What edge cases arise with recursive functions (same function ID appears on the stack multiple times)? 3. If timestamps were floating-point, how would you adjust the interval arithmetic? 4. Extend to multi-threaded execution: each function has a thread ID — what changes?
What to Expect in the Decagon Phone Screen Round
The Decagon Software Engineer Phone Screen round has a specific calibration purpose distinct from other rounds in the loop. Across 4+ verified reports on LeakCode for this exact round type, the consistent expectations: clear scoping of the problem before diving into a solution, explicit reasoning about complexity, structured handling of edge cases, and the ability to discuss trade-offs between two reasonable approaches.
Reports tagged with the Phone Screen round at Decagon show recurring patterns in difficulty and topic distribution. The Phone Screen round is typically 45-60 minutes; the interviewer is calibrated against a specific rubric. The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the answer. It is the path: did you clarify, did you verbalize your approach, did you handle edge cases, and did you communicate throughout.
How To Prepare for This Specific Round
Filter the questions below to the most recent reports (past 6-12 months). Questions tagged for this exact round type from this exact company at this exact role level are the highest-signal data available. Older reports may reference questions that have since rotated out of the company's pool.
Practice 4-6 representative problems from this set under timed conditions. The goal is not memorization (companies rotate questions); the goal is to internalize the patterns the interviewer typically reaches for and the depth of follow-up to expect. Reports on LeakCode also tag the typical follow-up depth at this round type, which is the discriminating signal between hire and no-hire calibration.
Phone Screen Round Timing and Format
The Phone Screen round at Decagon typically runs 45-60 minutes. Use the first 2-3 minutes to clarify requirements; you should never start coding or designing without verifying the input/output format, constraints, and edge cases out loud. Use the next 5-7 minutes to verbalize your approach before writing any code. The middle 20-30 minutes are implementation. Reserve the final 10 minutes for testing with concrete examples and discussing optimization or trade-offs.
Time budget discipline is one of the most reliable senior-vs-junior discriminators in this round. Strong candidates verbalize where they are in their budget out loud ("I've used about 20 minutes, I have 15 minutes left for testing and one optimization"). This signals engineering maturity to the interviewer and creates positive feedback they can capture in writing.
Common Failure Modes in This Round
Reports tagged "no hire" at Decagon Software Engineer Phone Screen commonly cite: coding silently without verbalizing approach, jumping to implementation before clarifying requirements, missing edge cases (empty input, single element, very large input), producing working code that the candidate cannot refactor when asked, and failing to test their solution with concrete examples before declaring done.
The single most predictive failure mode in 2025-2026 reports: not asking clarifying questions. Interviewers at all FAANG companies are explicitly trained to weight this dimension. Strong candidates ask 3-5 clarifying questions even on problems that look obvious; weak candidates dive into code immediately. The clarifying-question check is often the first signal recorded in the interviewer's notes.
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