Innovation and Emerging Technology Questions

Covers how organizations and engineering leaders identify, evaluate, pilot, and adopt emerging technologies and industry trends in a safe, strategic, and measurable way. Areas include continuous horizon scanning and trend monitoring; assessing technology maturity, vendor road maps, open standards, and lock in risks; designing pilots, sandboxes, and proofs of concept with clear success criteria and measurement plans; balancing innovation with reliability, operational cost, security, and compliance; risk and regulatory assessment; architectural fit and integration planning with existing systems; stage gate and portfolio decision making to adopt, delay, or reject technologies; change management, stakeholder alignment, and adoption planning including training and communication; production readiness and governance for prototypes versus production systems; scaling and operationalization concerns such as automation, observability, and supportability; and building repeatable prioritization frameworks, funding models, and processes for continuous innovation. At senior levels this also includes strategic thinking about future proofing, long term technical direction, ecosystem and go to market implications, and governance models that steward technology portfolios across business units.

MediumTechnical

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Implement Python pseudocode or outline steps to compute bootstrap confidence intervals for the difference in conversion rates between control and treatment groups in an A/B test. Describe how to interpret the interval and when you would prefer bootstrap methods over parametric approaches.

MediumTechnical

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How do you balance innovation speed versus reliability in ML model rollout? Provide a staged rollout plan that includes dark-launch, shadow mode, canary, and full release phases and specify instrumentation and acceptance criteria you would use at each stage.

MediumTechnical

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You are asked to evaluate a vendor-provided black-box foundation model for customer support automation. In the first week list the key evaluation dimensions (for example accuracy, safety, latency, cost, interpretability, data leakage risk) and propose at least one quick empirical test or dataset you would run to probe each dimension.

EasyTechnical

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Implement a Python function to compute Cohen's Kappa given two labeler arrays of equal length. Function signature: def cohen_kappa(labels_a: List[int], labels_b: List[int]) -> float. Aim for O(n) time, handle edge cases like single-label cases, and return a value between -1 and 1.

EasyTechnical

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At a high level, what inputs are required to compute sample size for an A/B test to detect a 2% absolute lift with 80% power? Explain the roles of baseline conversion, variance, significance level, and minimum detectable effect in your calculation and any practical considerations for pilot duration.

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