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Privacy by Design and Principles Questions

Comprehensive coverage of foundational privacy principles and the practice of embedding privacy into systems, products, and processes from inception. Candidates should understand core concepts including data minimization, purpose limitation, lawfulness and fairness of processing, accuracy, integrity and confidentiality, transparency, user control, privacy by default, retention limits, accountability, and security controls. The topic includes operationalization for product and engineering workflows: mapping data flows and inventories, conducting privacy impact assessments, threat modeling for privacy risks, defining retention and deletion policies, consent and user rights handling, choosing anonymization or pseudonymization strategies, and applying privacy enhancing technologies. It also covers integrating privacy requirements into the software development lifecycle with traceable requirements and design reviews, stakeholder collaboration with product managers engineers legal teams and compliance functions, measurement and monitoring of privacy controls in production, documentation and governance, and balancing privacy trade offs with business objectives and regulatory obligations such as the General Data Protection Regulation.

HardSystem Design
0 practiced
Design an automated 'privacy-ops' pipeline that runs as part of nightly ETL jobs: it should detect PII fields added to datasets, verify retention tags, and produce notifications. Sketch the components, rules engine, and integration points with data catalog and access control systems.
MediumTechnical
0 practiced
Describe a scalable process to build and maintain a data inventory and data flow map for dozens of datasets across analytics, ML, and production systems. What metadata would you capture, which stakeholders to involve, and how to keep it up to date?
HardSystem Design
0 practiced
Design a multi-tenant ML training pipeline that enforces tenant-level data isolation, supports pseudonymization for cross-tenant analytics, and allows secure model evaluation on tenant-held PII without centralizing raw data. Sketch components and security controls.
HardTechnical
0 practiced
You need to assess open-source differential privacy libraries for production use. Create an audit checklist covering correctness of implementations, privacy accounting, community support, performance, and potential failure modes. What red flags would cause you to reject a library?
MediumTechnical
0 practiced
Design a data classification schema suitable for a data science organization (levels, examples per level, handling rules). Include how classification drives access control, retention, and monitoring policies and how you'd train data owners to apply it consistently.

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