AI and Machine Learning Background Questions
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.
HardTechnical
83 practiced
Design a pipeline that incorporates differential privacy (DP) for aggregating sensitive user statistics used by models, and outline how secure multi-party computation (MPC) could be used for cross-organization aggregation. Explain how DP noise addition, privacy budgets, and MPC alter ingestion, testing, model evaluation, and how you would measure the utility versus privacy trade-off in practice.
EasyTechnical
72 practiced
Explain the difference between a model training pipeline and an inference (serving) pipeline in a production data platform from a data engineer's perspective. Describe the main components, data flows, scheduling and resource differences, and three operational tasks a data engineer typically owns for training versus inference (e.g., data validation, feature materialization, monitoring). Include examples of where inconsistencies commonly arise between the two pipelines and how you'd prevent them.
MediumTechnical
66 practiced
A PySpark job joining a 100GB fact table with a 50MB dimension table is running slower than expected. List concrete code and configuration changes you would attempt to speed it up. Provide a short PySpark snippet showing how and when to use a broadcast join and explain trade-offs (memory footprint, shuffling, skew). Mention at least two Spark configuration settings to tune.
MediumTechnical
64 practiced
A model training job on expensive GPU instances is taking too long and costing too much. As a data engineer, propose a prioritized plan to reduce runtime and cost. Consider data-related actions (sampled or cached datasets), distributed training strategies, mixed precision, checkpointing, and reuse of preprocessed inputs. Explain why you would prioritize each step and potential risks.
EasyTechnical
66 practiced
What is a feature store and why is it important for production ML systems? As a data engineer, describe the core components (offline store, online store, ingestion, materialization, metadata), how the store supports both offline training and low-latency online lookups, and outline a workflow to keep features consistent between training and serving (including backfills and real-time updates). Give an example of one trade-off you might make when choosing online storage.
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