Easy interview questions in MLOps & Infrastructure, page 2
What triggers automatic full retraining in an ML pipeline?
A strong answer lists four triggers: fresh data, code changes, model drift, and scheduled cadence.
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 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.
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.
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 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.
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.
Describe a basic lifecycle policy to manage cloud storage costs
This tests cost optimization via tiered storage and automated expiration. Strong answers list transitions from Standard to IA to Glacier, then deletion after set days, plus retrieval costs. A red flag is using manual scripts instead of native lifecycle rules.
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