Demo Candidate
Senior Backend Engineer · eng/L5 · candidate@example.test
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May 21, 2026, 2:27 PM
Competency rollup
| Competency | Score | |||
|---|---|---|---|---|
Code quality c.code_quality | 3.60 | 0.10 | 4 | 4 |
Debugging fluency c.debugging_fluency | 3.60 | 0.25 | 4 | 4 |
Modalities
| # | Modality | Driver | Status | Duration |
|---|---|---|---|---|
| 0 | live_code | live_code@0.1.0 | completed | 2 s |
| 1 | live_code | live_code@0.1.0 | completed | 2 s |
| 2 | live_code | live_code@0.1.0 | completed | 2 s |
| 3 | live_code | live_code@0.1.0 | completed | 2 s |
Transcript
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Round 1 — The Liar Filter. 10 minutes, python. Solve this: Write `most_common(words: list[str]) -> str | None` that returns the most-frequent word. Ties: return the lexicographically smallest. Empty list returns None.
```python def most_common(words: list[str]) -> str | None: pass ```
def most_common(words: list[str]) -> str | None: if not words: return None counts = {} for word in words: counts[word] = counts.get(word, 0) + 1 best = None for word, count in counts.items(): if best is None or count > counts[best] or (count == counts[best] and word < best): best = word return best
Integrity monitoring
Multiple sustained window-focus violations and one multi-face detection suggest significant integrity concerns requiring urgent human review.
- 6:46:48Multiple faces detected (2 ID faces, 1 selfie) with low similarity (0.24). This is a strong indicator of potential identity mismatch or unauthorized third party presence.
- 9:42:52 – 9:43:59Sustained burst of 10 consecutive HIGH-severity window-focus losses within ~70 seconds, including repeated tab switches and fullscreen exits. Suggests active navigation away from interview environment.
- 6:48:45 – 6:55:53Extended cluster of focus losses escalating to HIGH severity with multiple tab switches and window blurs, indicating prolonged distraction or resource consultation.
- 25:00:37 – 25:00:41Rapid succession of 4 focus-loss events in 4 seconds, culminating in HIGH-severity tab switch, suggesting deliberate window navigation.
The combination of multi-face detection (potential identity or third-party concern) and multiple clusters of escalating window-focus violations across the interview is concerning. The 9:43 cluster shows 10 consecutive HIGH-severity events, and three other clusters demonstrate similar patterns of tab-switching and fullscreen exits. This profile suggests either unauthorized assistance or deliberate resource consultation during the interview.