Unknown
Software Engineer · mid
46e954b8-c70e-44b1-82d9-6390c1f16b9c
May 21, 2026, 3:41 PM
Competency rollup
No scored competencies yet. The modalities that ran did not produce evidence.
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 |
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
Integrity monitoring
Moderate concern due to multiple faces detected at start and escalating window focus losses, but pattern suggests possible technical/environmental issues rather than deliberate cheating.
- 0:00Multiple faces detected (3 ID faces + 1 selfie face, similarity 0.25) at interview start—suggests potential unauthorized person(s) in frame or camera/lighting artifacts.
- 0:00–0:22Four window focus loss events (3 medium, 1 high severity) including 2 blur events and 1 tab switch within 22 seconds—indicates candidate is switching away from interview platform or losing visual focus.
The multiple-faces detection combined with rapid window focus losses (especially the tab switch) raises integrity concerns. However, the low face similarity score (0.25) and clustering of events at the very start suggest possible camera/setup miscalibration or environmental reflections rather than a coordinated cheating attempt. Human review should clarify whether the detected faces are genuine additional people or technical artifacts, and whether the tab switches correlate with known exam instructions.
5 signals · AI-generated · human review required
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