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. Retrofitting lineage takes only a few hours 2. You cannot reconstruct transformation history from final outputs so lineage must be built from the start 3. Final outputs contain all transformation history 4. Lineage can easily be added at any time 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. Training data documentation for high-risk systems and demonstrable traceability requirements 3. Only the model output needs to be documented 4. No documentation is required for any AI systems 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 is the critical link for backward lineage according to the article? 1. Network connection between servers 2. Database foreign keys 3. API authentication tokens 4. Model-to-data linkage connecting each trained model to its training dataset versions 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. 4 / 8 4. Why is transformation code versioning essential according to the article? 1. It reduces storage costs 2. It makes the code run faster 3. It is only needed for compliance audits 4. Capturing Git hash lets you know exactly which code version processed the data 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. 5 / 8 5. What metadata should be captured during the data collection stage? 1. Just the database connection string 2. Only the file size and format 3. Only metadata required by the AI model 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. 6 / 8 6. Why does feature engineering obscure data origins according to the article? 1. Derived features like ratios and aggregations create indirect connections to dozens of underlying data points 2. Features are stored in different databases than source data 3. Engineering transforms data into unreadable formats 4. Feature engineering deletes the original data 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 is the difference between forward and backward lineage? 1. Forward is automatic while backward requires manual effort 2. Forward traces source to output while backward traces output to source 3. Forward is for training while backward is for inference only 4. Forward is for new data while backward is for historical data 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. What three critical questions does lineage answer according to the article? 1. Who accessed – when accessed – why accessed 2. Where stored – when backed up – who owns it 3. What data – what transformations – what model version 4. How much – how fast – how accurate Correct! Why: The article states lineage answers what data and what transformations and what model version – if you cannot answer all three you have a lineage gap. Context: These questions form the foundation of traceability from prediction back to source. Remember: What data – What transformations – What model version. 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. Thank you! Please check your inbox to confirm your subscription.
