Interview questions in MLOps & Infrastructure, page 2
Design a system to detect training-serving skew for a numerical feature
Tests ML monitoring design via statistical distribution comparison between training and live data. Strong answers cover PSI/KS tests, windowed thresholding, and tiered alerting. Red flag: comparing raw values instead of distributions or ignoring alert fatigue.
Sub-20ms online feature serving
An in-memory key-value store (Redis) as the online feature store, precomputed features, streaming updates, and offline-online consistency.
Backfill a complex feature for millions of users without impacting production
Reuse the live pipeline on historical partitions, run bounded batches on separate compute, stage results, and validate before promotion.
How would you implement versioning for feature definitions in a feature store?
Tests separation of metadata schema versions from data snapshots for reproducible training. Strong answers cover: immutable schema versions on breaking changes, safe appends without backfill, and time-travel data reads.
Managing model-as-a-feature pipelines
An upstream embedding model becomes a versioned dependency, creating cascading retraining, version skew, latency stacking, and lineage complexity.
Design system ensuring point-in-time correctness for training data joins
Tests temporal join design to prevent data leakage from slowly changing dimensions. Strong answers use an AS OF join on entity ID and timestamp, materialize features as of label time, and handle late arrivals. Joining on user_id alone is a red flag.
Argue for declarative or imperative feature platforms with trade-offs
This tests whether you weigh control flow against data flow. A strong answer argues from org maturity: declarative systems abstract DAG topology, while imperative ones offer Spark control at the cost of manual idempotency. Red flag: ignoring org culture.
Why use a Model Registry over dated pickle files?
A strong answer covers versioning, lineage, promotion aliases, and governance.
Design a CI/CD pipeline that automates model promotion from Staging to Production
Tests whether you treat model promotion as a gated software delivery workflow. Strong answers use registry state-change triggers, automated drift and performance checks, canary deployment gates, and rollback.
How would you design a system to detect training-serving skew using model registry metadata?
This tests statistical monitoring between production data and registry training baselines. Strong answers: schema-bound metadata, incremental stats, drift metrics PSI, tiered alerting. Red flag: schema validation mistaken for drift or manual checks only.
Walk me through essential Dockerfile commands for a reproducible Python ML environment
Tests your ability to containerize Python ML scripts reproducibly. A strong answer covers FROM with a pinned slim image, WORKDIR, COPY for requirements and code, RUN pip install, and CMD or ENTRYPOINT.
How do you structure a Dockerfile to leverage layer caching for dependencies?
Tests Docker layer invalidation and cache-aware instruction ordering. Copy requirements.txt and run pip install before source code so deps cache independently. Red flag: copying everything at once or installing deps after code, busting cache every build.
How do you persist notebooks and artifacts in Docker?
Tests Docker storage abstractions. A strong answer distinguishes bind mounts for live notebook editing from named volumes for datasets and artifacts, and warns against docker commit for persistence. Red flag: treating containers as stateful VMs.
How do you build dev and production Docker images from one source?
Mastery of Docker multi-stage builds for isolating dev and production dependencies. Use a base stage, a dev target with linters and tests, and a lean production target copying only the build artifact.
How do you version and distribute Docker dev environments consistently?
Tests immutable dev environment distribution. Strong answers cover: versioned Dockerfiles in Git, immutable image tags pushed to a registry, and enforcing identical pulls for CI and developers. Red flag: using the "latest" tag or local Dockerfile rebuilds.
Describe two secure methods for providing secrets to a running container
Mention runtime mounts like Docker secrets, orchestrator secret injection, and cloud IAM patterns.
Strategies to reduce a 5GB ML Docker image size
Tests multi-stage build hygiene and ML bloat reduction. Strong answers use multi-stage builds, strip CUDA dev libs, use slim bases, and collapse cache cleanup into one RUN. Red flag: rm -rf in a separate RUN step, which still bloats the layer.
Design on-demand containerized dev environments for data scientists
Tests multi-tenant notebook infrastructure design. Cover a Notebook Controller, curated Jupyter and VS Code images, namespace isolation with RBAC, resource quotas, and persistent storage. Red flag: a single shared VM without tenancy or idle shutdown.
How do you containerize a Python training script for GPU cloud VMs?
This tests reproducible GPU containerization. A strong answer uses an NVIDIA CUDA base image, installs Python dependencies at build time, copies the training script, and runs with --gpus.
Difference between data and model parallelism, and when to prefer each
Tests split axis: data parallelism replicates model and shards data; model parallelism shards model across devices. Use data parallelism for throughput; model parallelism when layers exceed GPU memory.
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