Operationalizing ML Models
Free Certified Artificial Intelligence Practitioner practice — 6 questions on Operationalizing ML Models, with explanations. No sign-up. Full 12-question mixed test →
An ML engineering team wants to validate a new fraud-detection model against live production traffic before it ever influences a customer-facing decision. The new model must receive real requests and produce predictions in parallel with the current production model, but its outputs must never be returned to users or used to make decisions — only logged for comparison against the incumbent model's predictions. Which deployment strategy BEST fits this requirement?
Shadow deployment (also called dark launch) sends live production traffic to the new model in parallel with the incumbent, but discards or logs its output instead of returning it to users. This lets teams compare prediction quality and latency risk-free before any user is affected.
A production fraud model's precision has steadily degraded over three months. Investigation shows the statistical distributions of all input features remain nearly identical to training data, but the underlying relationship between those features and the fraud label has changed because fraudsters adopted new tactics. Which phenomenon does this describe?
Concept drift occurs when the statistical relationship between inputs and the target variable changes over time, even if the input feature distribution stays stable — exactly the case here where fraud patterns evolved.
A company deploys ML models to production several times per week. Leadership requires that if a newly deployed model causes a measurable business regression, the team must be able to revert to the exact prior serving model within minutes, with full confidence about which artifact, training data snapshot, and hyperparameters produced it. Which capability is MOST critical to satisfy this requirement?
A model registry with immutable versioning and lineage tracking (training data, code commit, hyperparameters, metrics) is the standard MLOps mechanism that enables fast, auditable rollback to a known-good prior version.
An ML platform team is hardening a training pipeline that currently reads a database password and third-party API key from plaintext values checked into the pipeline's configuration file in source control. Which change BEST secures these credentials while keeping the pipeline operational?
A dedicated secrets manager or vault issues credentials to authorized identities at runtime, supports rotation, enforces least-privilege access via RBAC, and provides audit trails — the accepted MLOps pattern for pipeline secrets.
A company has deployed a hiring-recommendation model. Six months post-launch, they want an ongoing practice that specifically detects whether the model's approval rates diverge significantly across demographic groups as new applicant data flows through the system in production. Which practice addresses this requirement?
Post-deployment ethical risk management requires ongoing monitoring of outcome disparities (e.g., disparate impact ratio, demographic parity) on live predictions, since fairness at training time does not guarantee fairness persists as production data shifts.
A team runs an online A/B test comparing a new recommendation model (treatment) against the current production model (control), splitting live traffic between the two. After one week, the treatment group shows a slightly higher click-through rate on average. Which additional condition is REQUIRED before the team can validly conclude the treatment model is genuinely better?
A/B test results must be validated with a statistical significance test and sufficient sample size to rule out the observed difference being due to random variance before drawing conclusions about which model truly performs better.
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