Role Specific Job Understanding Questions
Covers familiarity with specific job families and titles and the typical responsibilities and challenges associated with them. Examples include customer success, project management, account management, business intelligence, operations, sales operations, and executive roles such as vice president positions. Candidates should show domain knowledge about daily tasks, common tools, stakeholder interactions, and specific outcomes expected in those named roles, and ask role specific questions about scope and priorities.
HardSystem Design
0 practiced
Compare the trade-offs of on-device inference versus cloud inference for a privacy-sensitive mobile health application. Discuss latency, privacy, model size and compression, update cadence, energy consumption, personalization, and regulatory considerations. Provide recommended criteria to choose one approach or a hybrid solution.
EasyTechnical
0 practiced
You have two high-priority requests: a Sales-sourced quick UI tweak expected to improve conversion next week, and a Product-sourced model architecture change requiring three months of work promising larger long-term gains. As the AI Engineer with limited resources, describe a prioritization framework you would apply and justify a recommended course of action with concrete criteria.
HardTechnical
0 practiced
Design a compensation and career ladder for AI Engineers that clearly differentiates the individual contributor (IC) technical track from the engineering management track. Define four levels for each track (e.g., IC1-IC4, EM1-EM4), core competencies, measurable promotion criteria, ownership expectations, and sample compensation band considerations.
MediumTechnical
0 practiced
You must evaluate three third-party OCR APIs for production integration. Create an evaluation checklist covering technical (accuracy, latency, throughput), legal (data retention, deletion, residency), operational (SLAs, support), and cost criteria. Describe a short PoC plan with sample inputs, metrics to collect, and decision gates to choose a vendor.
HardTechnical
0 practiced
Design an experimental plan to train classifiers under severe class imbalance and limited positive samples. Include data augmentation and synthetic generation strategies, resampling approaches (oversampling, undersampling), algorithmic choices (focal loss, class-weighted loss, cost-sensitive learning), evaluation methodology (stratified cross-validation, precision-recall curves), and steps to prevent overfitting.
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