Vanta·Software Engineer·Technical Phone Screen·Intermediate

Intermediate

Jun 2026

Summary

Vanta software engineer interview with a pretty interesting coding problem around test run logging and failure tracking. Two parts to the same problem, and the second part was where things got tricky.

Questions Asked (2)

Implement a log(test_id, timestamp, status) function to record test runs where timestamps are strictly increasing across all test IDs. Then implement get_min_time_to_pass(test_id) that returns the minimum duration from the start of any consecutive failure streak to the next passing status, or null if the test never passes.

Algorithms & Data StructuresSystem Design

Author's notes

The log part was fine, just needed a sorted structure keyed by timestamp globally.

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AI HintsAI Generated

Suggested Approach

Clarify the semantics of consecutive failure streaks and the definition of 'start' (first failure after a pass or the beginning of the log). Then design a data structure that stores per-test status history and efficiently computes the minimum duration by scanning for failure-to-pass transitions, handling edge cases like no failures or no passes.

Pro tip: Mention that the strictly increasing timestamps allow you to process logs in order and maintain state per test ID, which simplifies streak tracking and avoids sorting. Also, discuss how to handle multiple failure streaks and ensure you return the minimum duration, not the first or last.

1. Clarify requirements and edge cases

Ask questions to confirm: What defines a failure streak? Does it start at the first failure after a pass, or at the beginning of the log? What if a test never fails? What if it never passes? Are timestamps guaranteed unique and increasing?

2. Design data structures

Choose a structure to store per-test status history, such as a list of (timestamp, status) per test ID, or maintain running state like current streak start time and minimum duration seen so far.

3. Implement log function

Append the new log entry to the test's history and update any running state: if status is failure and no streak is active, start a new streak; if status is pass and a streak is active, compute duration and update minimum, then end streak.

4. Implement get_min_time_to_pass

Return the stored minimum duration for the test ID, or null if no passing status has occurred after a failure streak. Ensure it reflects the minimum across all streaks.

5. Analyze complexity and trade-offs

Discuss time and space complexity: O(1) per log and O(1) per query with running state, or O(n) query if scanning history. Mention trade-offs between memory and query speed.

Key Points to Mention

Definition of a failure streak: consecutive failures for a test ID, starting after a pass or at the first log entry.
Handling of multiple streaks: compute duration for each streak and keep the minimum.
Edge cases: test never fails, never passes, or passes without a preceding failure streak.
Timestamp ordering: strictly increasing allows sequential processing without sorting.
Data structure choice: per-test state (e.g., current streak start, min duration) vs. full history.
Time and space complexity: aim for O(1) log and query with state, or O(n) query with history.

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

Using the same log() function, implement get_longest_failure_window(min_tests) that returns the start and end timestamps of the longest contiguous time window where at least min_tests distinct tests are failing simultaneously. Return null if no such window exists.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This one genuinely stumped me for a bit.

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AI HintsAI Generated

Suggested Approach

Parse the log entries to extract failure events, then use a sweep-line algorithm with a hash map to track the count of distinct failing tests over time. Identify intervals where the count meets or exceeds min_tests, and merge overlapping intervals to find the longest contiguous window. Return the start and end timestamps of that window, or null if none exists.

Pro tip: Clarify whether the window boundaries are inclusive and how to handle simultaneous events (e.g., a test starting and another ending at the same timestamp). This demonstrates attention to edge cases and real-world data nuances.

1. Parse and Extract Events

Process the log data to create a list of events, each with a timestamp, test ID, and type (failure start or end). Ensure distinct tests are tracked.

2. Sort Events Chronologically

Sort all events by timestamp. For events at the same timestamp, decide on a consistent order (e.g., process starts before ends) to correctly handle overlapping intervals.

3. Sweep and Track Count

Iterate through sorted events, maintaining a set of currently failing tests. At each event, update the set and record the count of distinct failing tests.

4. Identify and Merge Valid Intervals

Whenever the count meets or exceeds min_tests, start or continue a valid interval. When it drops below, close the interval. Merge adjacent intervals if they are contiguous.

5. Find Longest Window and Return

Track the longest valid interval found. After processing all events, return its start and end timestamps, or null if no interval met the condition.

Key Points to Mention

Handling distinct tests: use a set or hash map to avoid double-counting the same test.
Time complexity: O(N log N) due to sorting, where N is the number of events; space complexity O(N) for storing events and active tests.
Edge cases: no failures, min_tests <= 0, simultaneous events, and windows that start/end at the same timestamp.
Definition of 'contiguous': intervals are contiguous if they overlap or touch (end of one equals start of next).
Trade-offs: alternative approaches like interval trees or segment trees may be overkill; sweep-line is optimal for this problem.
Clarify assumptions: whether the log provides failure start and end events, or only failure occurrences; how to handle tests that fail multiple times.

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