Data Lineage Tracking for AI: Complete Guide | QuizBy Eyal Doron / December 6, 2025 / 1 minute of reading Data Lineage Tracking for AI: Complete Guide | Quiz 1 / 8 1. What does the EU AI Act require regarding training data according to the article? 1. No documentation is required for any AI systems 2. Only the model output needs to be documented 3. Documentation is optional for all risk levels 4. Training data documentation for high-risk systems and demonstrable traceability requirements Correct! Why: The EU AI Act requires training data documentation for high-risk AI systems demonstrating what data trained the model and its characteristics plus traceability requirements. Context: Lineage is the technical foundation for meeting these regulatory requirements. Remember: Document training data plus demonstrate traceability. 2 / 8 2. How does lineage support GDPR right to erasure according to the article? 1. Erasure only requires deleting the original source data 2. Lineage shows which models were trained on a person's data enabling accurate deletion compliance 3. GDPR does not apply to AI training data 4. Lineage automatically deletes data when requested Correct! Why: If someone requests deletion you need to know which models were trained on their data – lineage answers this question and without it you cannot comply accurately. Context: Right to erasure creates complex challenges for AI that only lineage can address. Remember: Deletion requests require knowing which models used the data. 3 / 8 3. What tools does the article recommend for different lineage roles? 1. Custom tools must be built from scratch 2. Any database system works equally well 3. MLflow for experiment tracking – DVC for data versioning – Apache Atlas for enterprise lineage 4. Only spreadsheets and manual documentation Correct! Why: The article recommends MLflow for experiment tracking and model-to-data linkage and DVC for dataset version control and Apache Atlas for enterprise lineage and regulatory audits. Context: Tool integration is one approach for practical lineage implementation. Remember: MLflow for experiments – DVC for data – Atlas for enterprise. 4 / 8 4. What is the critical link for backward lineage according to the article? 1. Model-to-data linkage connecting each trained model to its training dataset versions 2. API authentication tokens 3. Database foreign keys 4. Network connection between servers Correct! Why: Model-to-data linkage explicitly connects each trained model to its training dataset versions – without it you cannot trace a prediction back to its training data. Context: Dataset version identification assigns unique identifiers to training data snapshots. Remember: No model-to-data link equals no backward traceability. 5 / 8 5. Why is transformation code versioning essential according to the article? 1. It makes the code run faster 2. It is only needed for compliance audits 3. Capturing Git hash lets you know exactly which code version processed the data 4. It reduces storage costs Correct! Why: Capturing the Git hash of the cleaning script lets you know exactly which code version processed the data enabling reproducibility. Context: This is part of documenting every transformation applied to raw data during preparation. Remember: Git hash equals reproducible transformations. 6 / 8 6. Why does feature engineering obscure data origins according to the article? 1. Engineering transforms data into unreadable formats 2. Features are stored in different databases than source data 3. Feature engineering deletes the original data 4. Derived features like ratios and aggregations create indirect connections to dozens of underlying data points Correct! Why: When you derive new features like ratios and aggregations and embeddings the connection to original data becomes indirect – a customer_risk_score might derive from dozens of underlying data points. Context: This is one of several factors that make AI lineage harder than traditional data lineage. Remember: Derived features hide their sources. 7 / 8 7. What are the six components of AI lineage described in the article? 1. Input – Processing – Output – Storage – Backup – Archive 2. Collection – Validation – Training – Testing – Production – Retirement 3. Source – Transformation – Model – Deployment – Inference – Governance 4. Authentication – Authorization – Encryption – Logging – Monitoring – Alerting Correct! Why: The article identifies source lineage and transformation lineage and model lineage and deployment lineage and inference lineage and governance lineage as the six interconnected elements. Context: Together these create the end-to-end chain-of-custody for AI systems. Remember: Source – Transform – Model – Deploy – Infer – Govern. 8 / 8 8. According to the article – what analogy best describes data lineage for AI? 1. A firewall that protects data from unauthorized access 2. A backup system that stores copies of all data 3. An encryption system that secures data at rest 4. A family tree for your data showing origin and transformations and destination Correct! Why: The article describes data lineage as a family tree for your data showing where data came from and what happened to it along the way and where it ended up. Context: This is also compared to chain-of-custody for your AI pipeline documenting every transformation. Remember: Family tree plus chain-of-custody for data. Your score isThe average score is 0% Restart quiz Download PDF Please leave this field empty๐ The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We donโt spam! Read our privacy policy for more info. 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