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. Why is the misconception that we can add lineage later dangerous? 1. Final outputs contain all transformation history 2. Lineage can easily be added at any time 3. Retrofitting lineage takes only a few hours 4. You cannot reconstruct transformation history from final outputs so lineage must be built from the start Correct! Why: Retrofitting lineage is extremely difficult because you cannot reconstruct transformation history from final outputs – the article advises building lineage tracking from the start. Context: This is one of four common misconceptions the article addresses. Remember: Cannot reconstruct history from outputs. 2 / 8 2. What does the EU AI Act require regarding training data according to the article? 1. Documentation is optional for all risk levels 2. Only the model output needs to be documented 3. No documentation is required for any AI systems 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. 3 / 8 3. What Quick Win does the article recommend for starting lineage tracking? 1. Mandate DVC or MLflow to version control training dataset and model file for highest-risk AI system 2. Hire a dedicated lineage team 3. Purchase an enterprise lineage platform immediately 4. Document all lineage manually in spreadsheets Correct! Why: The article recommends mandating DVC or MLflow to version control the training dataset and model file for your highest-risk AI system this week. Context: This creates the essential model-to-data linkage required for basic compliance. Remember: Version control training data and model for highest-risk system. 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. Database foreign keys 3. API authentication tokens 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. Capturing Git hash lets you know exactly which code version processed the data 2. It is only needed for compliance audits 3. It makes the code run faster 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. What metadata should be captured during the data collection stage? 1. Just the database connection string 2. Only metadata required by the AI model 3. Only the file size and format 4. Source system identification – collection timestamps – consent and permission metadata Correct! Why: The article specifies capturing source system identification (which database or API) and collection timestamps (when data was extracted) and consent and permission metadata (legal basis for use). Context: This metadata becomes critical for GDPR compliance. Remember: Source – Timestamp – Consent. 7 / 8 7. What is the difference between forward and backward lineage? 1. Forward traces source to output while backward traces output to source 2. Forward is automatic while backward requires manual effort 3. Forward is for new data while backward is for historical data 4. Forward is for training while backward is for inference only Correct! Why: Forward lineage traces data from source to output answering what happened to this data while backward lineage traces from output to source answering where did this prediction come from. Context: Both directions matter – forward supports compliance and auditing while backward enables debugging and explanation. Remember: Forward equals source to output – Backward equals output to source. 8 / 8 8. According to the article – what analogy best describes data lineage for AI? 1. A family tree for your data showing origin and transformations and destination 2. An encryption system that secures data at rest 3. A firewall that protects data from unauthorized access 4. A backup system that stores copies of all data 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 empty🔐 The AI Security Manager's Newsletter Weekly insights on AI risk management, EU AI Act compliance, and practical security strategies. We don’t spam! Read our privacy policy for more info. Thank you! Please check your inbox to confirm your subscription.
