Serve low‑latency predictions without sacrificing quality. Explore streaming patterns, stateful features, and drift monitoring for production pipelines.
A common pattern for real‑time scoring is ______ features to ensure low‑latency reads.
online feature store with pre‑materialized aggregates
offline feature pipelines rebuilt hourly
joining features via nightly batch jobs
computing all features on the client device
To keep pipelines resilient, consumers of a Kafka topic should be ______ so replays don’t double‑charge.
idempotent
state‑free
synchronous only
GPU‑bound
Micro‑batch systems like Spark Structured Streaming trade a bit of latency for ______.
model accuracy independent of batch size
elimination of backpressure
throughput and easier exactly‑once semantics
lower memory usage than event‑time engines
For concept drift, a lightweight online guardrail is to track ______ over sliding windows.
the SHA‑256 of inputs
prediction/label distributions and key feature stats
only CPU temperature
raw message size averages
Feature freshness for counters (e.g., 7‑day clicks) is best maintained with ______ updates.
manual CSV uploads
incremental, windowed aggregations
client‑side caching only
full recomputation each night
If the 99th‑percentile latency budget is 50 ms, heavy models are often deployed using ______.
full‑precision ensembles only
daily batch scoring
model distillation or specialized low‑latency runtimes
browser‑side Python interpreters
Event‑time windowing with watermarks helps ______.
handle late arrivals while bounding state growth
replace monitoring entirely
remove the need for retries
guarantee ordered delivery
For models needing fresh embeddings, a practical approach is ______.
publishing embeddings once per quarter
asynchronous embedding updates with a staleness SLA
retraining the full model on every message
blocking requests until all features recompute
When serving at scale, autoscaling policies should consider ______.
repository commit frequency
concurrency, queue depth, and request latency
only average CPU over 24 hours
schema version count
A blue‑green rollout helps with streaming model updates because it ______.
requires deleting the old model first
removes the need for validation entirely
routes a fraction of traffic to the new version with instant rollback
forces a full outage window
Starter
Revisit latency budgets, feature freshness, and idempotent serving.
Solid
You grasp streaming joins, windowing, and monitoring for drift and throughput.
Expert!
You can run resilient low‑latency scoring at scale with proactive monitoring.
