Easy everything in AI & ML, page 4
Differences between on-demand, reserved, and spot EC2 instances?
Tests cost-reliability-commitment tradeoffs for ML infrastructure. Good answers map on-demand to experiments, reserved for production training, and spot to fault-tolerant batch jobs. Red flag: spot for real-time serving or skipping reserved capacity analysis.
How do you attribute cloud costs to ML projects and implement tagging?
Tests knowledge of resource tagging for cost attribution. A strong answer names provider-specific tags or labels, embeds them in infrastructure-as-code, and activates cost allocation reports.
Why was this customer denied: global or local explanation?
This tests matching questions to explanation scope. Global methods show overall behavior; local methods explain one prediction. Specific denials need local methods like SHAP. A red flag is using global summaries like permutation importance or PDPs for a case.
How do you give read-only access to a shared cloud storage bucket?
Bind an IAM role with read permissions to the team at the bucket level, avoid object-level ACLs, and mount read-only on training VMs.
What is a feature store and how does it prevent training-serving skew?
This tests training-serving consistency via centralized feature management. Covers offline batch storage, online serving, shared transformations, and alternatives like ad-hoc ETL. A red flag is calling it just a database and ignoring point-in-time correctness.
What are the essential components of an end-to-end ML platform?
Tests mapping the ML lifecycle to infrastructure. A strong answer walks through six stages: data ingestion, feature store, training pipeline, model registry, deployment/serving, and monitoring with feedback loops.
What triggers automatic full retraining in an ML pipeline?
A strong answer lists four triggers: fresh data, code changes, model drift, and scheduled cadence.
Differences between monitoring a traditional REST API and a production ML model
Contrast latency/errors with ML signals like data drift and training-serving skew against baselines, noting ground truth delays.
Deploy a trained model as a containerized REST API
This tests practical MLOps fluency. A strong answer covers loading the artifact, wrapping it in a web server, building a Dockerfile, and exposing health and predict endpoints. Red flag: conflating training with serving or omitting resource limits.
Describe the difference between online and batch inference.
Online uses autoscaling APIs for millisecond-to-second latency; batch uses scheduled compute for minute-to-hour latency.
Describe the difference between online and batch inference.
Online serves single requests in ms on live endpoints; batch processes data offline with elastic compute.
What is a model registry's purpose in CI/CD4ML and its CI/CD interaction?
Tests if you see the model registry as the bridge between experiments and production, not just storage. A strong answer explains how CI publishes validated artifacts and CD consumes versioned models. Red flag: calling it a passive file dump without versioning.
What automated tests belong in CI before deploying a classification model?
Name data schema checks, performance regression vs baseline, bias audits, and artifact integrity.
Key differences between traditional and ML CI/CD pipelines?
Contrast code deploys with data versioning, model registries, and retraining; note holdout eval.
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.
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.
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.
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.
Online vs offline feature store architecture and use cases
This tests latency trade-offs between real-time and batch infrastructure. Contrast fast online lookups against batch offline stores; fraud detection maps to online and model training to offline. Red flag: treating them as interchangeable and ignoring latency.
What problems does a Feature Store solve in ML systems?
Tests understanding of feature store value beyond storage. Great answers cover: feature reuse across teams, managed transformation pipelines, and online/offline consistency to prevent training-serving skew. Red flag: calling it simply a database or cache.
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