Instructions and covered skills
Read each scenario carefully before selecting a response. Focus on the stated purpose, the data needed to support it, and the lifecycle controls described. Do not infer permissions or business needs that are not included in the question. Work in a quiet setting and turn off notifications before you begin. Keep your attention on each option rather than rushing to finish. Review your selections for consistency with data minimization and retention governance principles.
Key Areas
This test covers the ability to apply data minimization and retention governance to AI-enabled products and workflows. Candidates evaluate whether a proposed collection is relevant and proportionate to a documented processing purpose. They distinguish information required for model performance, service operations, security monitoring, and legal obligations from information collected merely because it may be useful later.
The test also examines lifecycle decisions. This includes identifying clear retention triggers, assigning a defined retention period, separating active records from archived copies, and recognizing that backups, feature stores, logs, evaluation sets, and training datasets can have different operational roles. Candidates consider how deletion, de-identification, aggregation, and access restriction can reduce exposure when information must be retained.
Another focus is governance evidence. Strong practice links data inventories, purpose statements, retention schedules, deletion procedures, vendor commitments, legal holds, and audit records. Candidates should be able to identify when a change to an AI use case, data source, or model design requires a review of the existing retention decision.
Recommended Preparation
Prepare by reviewing the lifecycle of an AI system from intake through disposal. Practice writing concise purpose statements and mapping each data element to a specific operational need. Compare collection forms, telemetry designs, conversation logs, and model-training datasets to identify fields that can be removed, shortened, masked, or aggregated.
Review organizational retention schedules and learn how triggers such as account closure, case resolution, contract termination, or dataset supersession affect disposal timing. Consider how legal holds suspend routine deletion for relevant records while preserving limits on unrelated data. Study how deletion requests are executed across production stores, analytics environments, vendor systems, and backup processes. Finally, practice evaluating scenario-based tradeoffs where a team seeks useful data while needing clear limits, documented accountability, and verifiable disposal.