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
0 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
0 practiced
What are the primary privacy and compliance considerations for a data engineer building ML pipelines that use personal data (PII)? Describe practical techniques (pseudonymization, anonymization, tokenization, differential privacy) and pipeline-level controls (access controls, encryption at rest/in transit, data minimization, audit logs) you would implement to minimize exposure of raw PII while enabling model development.
MediumTechnical
0 practiced
Your team observes a steady decline in model performance over a month. As the data engineer, outline a practical investigation plan to determine whether the cause is data drift, label drift, feature pipeline regression, or serving-side issues. Include which metrics and plots you would compute, sample queries to run, and short-term mitigations to restore acceptable performance while you investigate.
MediumTechnical
0 practiced
How would you implement end-to-end data lineage for ML features and training datasets so that a prediction can be traced back to the raw source records and transformation steps? Describe metadata stores, automated capture during ETL, integrations with model registry and feature store, and how you would surface lineage information to data scientists and auditors.
EasyTechnical
0 practiced
Write a PySpark code sketch (pseudocode acceptable) that reads a Parquet dataset from s3://my-bucket/events/, filters rows where event_time is within the last 7 days, computes per-user event counts, and writes results to a partitioned Hive/Delta table partitioned by event_date. Mention partitioning strategy, file format, and at least two optimizations you would consider for performance and cost.
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