**Approach:** Maintain window (deque). Compute rolling mean, std. Flag if value beyond k*std (e.g., 3-sigma). For streaming: update incrementally (Welford's algo for running mean/var). **Robustness:** Mean/std sensitive to outliers. Use median + MAD for robust. Or EWMA for...
This hard-level Python/Coding question appears frequently in data engineering interviews at companies like Disney+ Hotstar. While less common, it tests deeper understanding that distinguishes strong candidates. Mastering the underlying concepts (window) will help you answer variations of this question confidently.
This is a senior-level question that tests architectural thinking. Lead with the high-level design, then drill into specifics. Discuss trade-offs explicitly - there is rarely one correct answer. Show awareness of scale, fault tolerance, and operational complexity.
Approach: Maintain window (deque). Compute rolling mean, std. Flag if value beyond k*std (e.g., 3-sigma). For streaming: update incrementally (Welford's algo for running mean/var).
Robustness: Mean/std sensitive to outliers. Use median + MAD for robust. Or EWMA for trend. Alert on anomaly rate, not single spike.
def detect_anomalies(values, window=10, k=3):
for i in range(window, len(values)):
w = values[i-window:i]
if np.std(w) > 0 and abs(values[i] - np.mean(w)) > k * np.std(w):
yield i, values[i]
Want feedback on your answer?
Paste your answer to this question and our AI Coach scores it, finds gaps, and shows you the FAANG-level version.
Get the most asked SQL questions with expert answers. Instant download.
No spam. Unsubscribe anytime.
Paste your answer and get instant AI feedback with a FAANG-level improved version.
Analyze My Answer — FreeAccording to DataEngPrep.tech, this is one of the most frequently asked Python/Coding interview questions, reported at 1 company. DataEngPrep.tech maintains a curated database of 1,863+ real data engineering interview questions across 7 categories, verified by industry professionals.