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Why design ML pipeline steps to be idempotent?
Re-running a step with the same input yields the same result and no duplicate side effects; enables safe retries and backfills.
Compare and contrast Apache Airflow versus Kubeflow Pipelines for ML orchestration
This tests matching orchestrators to ML constraints. A strong answer contrasts Airflow's task scheduling and backfills with Kubeflow's K8s-native GPU scaling, choosing based on team skills.
Design an ML workflow that masks PII from scientists
This tests privacy-preserving pipeline design and least-privilege access for ML teams. Propose automated de-identification before experimentation, restrict re-identification to production jobs, and enforce role-based access with audit logs.
Low GPU utilization on multi-GPU instance: diagnose and right-size
Tests distributed bottleneck triage. Strong answers profile CPU/GPU/disk, compare gradient sync time to compute, validate per-GPU batch size, and check NVLink vs PCIe. Red flag: suggesting more GPUs before ruling out data starvation or all-reduce overhead.
Why systematically track ML experiments and what should you log?
This tests reproducibility mindset over bookkeeping. A strong answer names three motivations—reproducibility, selection, debugging—and three logs: hyperparameters, metrics, and code versions.
Describe a Model Registry and how it differs from versioned storage
It tests governance and lifecycle metadata beyond file storage. A strong answer covers lineage, stage transitions, approval gates, and artifact metadata, contrasting with buckets that only store file versions.
Reproduce a six-month-old model using experiment tracking
Trace code commit, dataset version, feature pipeline, hyperparameters, dependency manifest, and random seeds through a model registry.
Debug sudden model degradation using experiment tracking and model registry
Tests unified use of experiment tracking and registry lineage. Great answers verify the exact production artifact, inspect linked training data and hyperparameters, compare input distributions, and check dependency metadata.
Design a centralized model registry for a large enterprise
Tests ML artifact governance at scale. Strong answers cover immutable versioned artifacts with dependency manifests, a framework-agnostic API, and pluggable deployment targets. Red flag: treating models as opaque files without environment reproducibility.
Why avoid global Python dependencies for ML, and how do containers help?
This probes environment isolation and reproducibility in ML. A strong answer cites global dependency conflicts, system library skew, and brittle environments; then notes containers freeze the full stack for deterministic deployment.
How do you configure Docker for host GPU access and CUDA libraries?
This tests GPU passthrough via the NVIDIA Container Toolkit. Strong answers use nvidia/cuda base images matching the host driver, pass GPUs with --gpus all, and avoid installing drivers inside the container.
Hybrid parallelism for large-model training
Split the model itself via tensor or pipeline parallelism so each replica is smaller, shrinking gradient all-reduce; combine with data parallelism in 2D/3D.
Flask/Gunicorn vs Triton/TorchServe for serving
Flask is simple and flexible but lacks dynamic batching, GPU scheduling, and multi-model management; Triton/TorchServe add those plus metrics and versioning.
How do you monitor model health with delayed ground truth labels?
Tests ML ops maturity beyond accuracy. A strong answer covers input drift via PSI or KS tests, prediction distribution shifts, proxy business metrics, and human spot-checking. A red flag is passively waiting for labels or retraining blindly without validation.
How would you design drift detection for high-dimensional embeddings?
Tests distribution shift in latent spaces beyond per-feature stats. Strong answers use maximum mean discrepancy, k-NN two-sample tests, or domain-classifier AUC, plus windowing. Red flag: per-dimension KS tests or mean-difference thresholds.
Explain a model registry's purpose and what to store per version
Tests if you treat the registry as a governance bridge between training and production, not just storage. Strong answers cite versioned artifacts, lineage, metrics, dependencies, and approval gates. Red flag: calling it a file dump or experiment tracker.
How does a model registry differ from cloud storage like S3?
This tests model governance beyond raw storage. A strong answer contrasts storage with stage transitions, lineage, and ACLs, then lists metadata like metrics, dependencies, and schemas. A red flag is treating S3 folders with naming conventions as a registry.
Declarative vs imperative ML platform design
Declarative GitOps gives auditable, reproducible, reviewable desired-state config with strong governance but a steeper learning curve; imperative SDKs are flexible and fast for scientists but harder to…
Epsilon in differential privacy and its trade-offs
Epsilon is the privacy budget bounding how much one record can change outputs; smaller epsilon means stronger privacy but more noise and lower accuracy.
Design a cost-aware ML training platform for heterogeneous hardware
Tests hardware abstraction and cost-aware cross-accelerator scheduling. Strong answers cover a device-agnostic spec, a performance predictor, a cost-per-step model, and bin-packing against spot prices. Red flag: ignoring per-step cost and migration overhead.