Domain and Product Technical Knowledge Questions

Evaluation of deep, domain specific technical knowledge relevant to the team, product, or role. Candidates should demonstrate subject matter expertise in the relevant problem space and be able to explain core concepts, architectures, algorithms, and practical engineering trade offs. Example domains include recommendation systems, data platform engineering, security, and analytics, as well as platform areas such as application programming interface platform management, developer experience, deployment orchestration, infrastructure and reliability, and observability. Expect questions on domain specific algorithms, data pipelines, real time versus batch trade offs, feature stores, data governance, versioning strategies, integration patterns, common customer use cases, and typical product pain points. For product focused roles, be prepared to explain core product features, typical customer workflows, integration points, and how domain constraints influence product decisions. For role or platform focused discussions, describe how the domain shapes responsibilities, challenges, and priorities and outline approaches to initial discovery, diagnosis, and early improvements. This topic tests both conceptual depth and the ability to map domain knowledge to concrete product and engineering decisions.

MediumTechnical

0 practiced

Describe how to incorporate human-in-the-loop (HITL) feedback into a classification product to improve long-term model quality. Cover labeling workflows, active learning strategies, UI/UX for labelers, latency of feedback, quality control, and cost-benefit trade-offs for continuous learning.

MediumTechnical

0 practiced

Describe a cost-optimized training pipeline for large transformer fine-tuning or pretraining that uses spot instances, mixed precision, gradient accumulation, sharded checkpoints, and autoscaling. Focus on checkpoint frequency, resume strategies, fault tolerance, and how to balance lower cost vs longer wall-clock time.

EasyTechnical

0 practiced

Describe the core components of the machine learning lifecycle as they apply to a product team building AI features. Cover data collection, data validation, feature engineering, model training, evaluation, deployment, monitoring, and feedback loops. Explain responsibilities and handoffs between an AI engineer, product manager, data engineer, and SRE at each stage.

EasyTechnical

0 practiced

Explain technical differences and product trade-offs between fine-tuning a large language model and relying on prompt-engineering or adapter layers to add capabilities. Discuss costs, latency, maintenance, reproducibility, and speed of updates for each approach.

EasyTechnical

0 practiced

List common evaluation metrics for classification, regression, ranking, and generative models. For each metric, explain when it aligns with product goals and mention pitfalls when relying solely on offline metrics to predict online user impact.

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