Data Organization and Infrastructure Challenges Questions
Demonstrate knowledge of the technical and operational problems faced by large scale data and machine learning teams, including data infrastructure scaling, data quality and governance, model deployment and monitoring in production, MLOps practices, technical debt, standardization across teams, balancing experimentation with reliability, and responsible artificial intelligence considerations. Discuss relevant tooling, architectures, monitoring strategies, trade offs between innovation and stability, and examples of how to operationalize models and data products at scale.
Sample Answer
Sample Answer
class StreamingStats:
def __init__(self):
self.n = 0 # count
self.mean = 0.0 # running mean
self.M2 = 0.0 # sum of squared deviations
def update(self, x: float) -> None:
"""Ingest a single numeric value."""
self.n += 1
delta = x - self.mean
self.mean += delta / self.n
delta2 = x - self.mean
self.M2 += delta * delta2
def merge(self, other: "StreamingStats") -> None:
"""Merge another StreamingStats into this one (in-place)."""
if other.n == 0:
return
if self.n == 0:
self.n, self.mean, self.M2 = other.n, other.mean, other.M2
return
n1, n2 = self.n, other.n
delta = other.mean - self.mean
n = n1 + n2
# combined mean
self.mean = (n1 * self.mean + n2 * other.mean) / n
# combined M2 (parallel algorithm)
self.M2 = self.M2 + other.M2 + delta * delta * n1 * n2 / n
self.n = n
def variance(self, sample: bool = True) -> float:
"""Return sample variance by default; population if sample=False."""
if self.n < 1:
return float("nan")
if sample:
return self.M2 / (self.n - 1) if self.n > 1 else float("nan")
else:
return self.M2 / self.n
def to_tuple(self):
"""Return (n, mean, M2) for serialization."""
return (self.n, self.mean, self.M2)Sample Answer
Sample Answer
Sample Answer
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