Describe the Solutions for Code, Data, and Model Traceability CERTIFIED-AI-EXPERT Practice Questions (Page 4)

Questions 16–20

1. 16application · medium

   A healthcare AI team is developing a model to predict patient readmission. They receive a new version of the patient dataset every month, and they apply a series of SQL transformations before training. A researcher wants to reproduce a model trained in June, but the original SQL scripts have been overwritten. What should the team have done to ensure the June model could be reproduced?

   • ARecord the output schema of the transformed data in a data dictionary.
   • BStore the SQL transformation scripts in a version control system and tag them with the date they were applied.
   • CKeep a copy of the raw patient data from June in a separate archive folder.
   • DSave the final trained model file from June in a model registry.
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2. 17application · medium

   A retail company's data science team is investigating why a model's accuracy dropped after a recent data update. They need to determine if the issue was caused by a change in the data transformation logic or by a change in the raw data itself. What should they review?

   • AThe raw data files from the most recent upload.
   • BThe model registry entry for the latest model version.
   • CThe data lineage records showing the transformation code version and the raw data version used for the latest training run.
   • DThe model's performance metrics from the last three months.
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4. 18application · medium

   An e-commerce company retrains its recommendation model weekly. The data science team needs to compare the performance of this week's model against last week's, and they also need to know which training dataset version each model used. What is the most effective way to meet both needs?

   • ASave the training dataset version in the model's configuration file.
   • BKeep a spreadsheet that lists model names and their accuracy scores.
   • CUse a model registry that records the model version, training run ID, and the dataset version used for each run.
   • DStore each model file in a folder named with the training date.
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5. 19expert · hard

   A telecom company is deploying a churn prediction model. The audit team requires a complete record of the model's development, including the code, data, and model versions. The team uses a notebook environment for experimentation and a separate model registry for production models. The notebooks are not versioned. What is the most effective way to ensure the audit trail is complete?

   • AStore the notebook files in the model registry alongside the model.
   • BRecord the notebook name and the date it was last run in the model registry.
   • CVersion the notebooks in Git and link the notebook version to the model registry entry for each production model.
   • DExport the notebooks to PDF and store them in a shared folder.
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6. 20expert · hard

   A government agency is using an AI model for fraud detection. They must be able to prove that the model was trained on data that was processed in a specific way. The data pipeline involves multiple steps: ingestion, cleaning, feature engineering, and splitting. The team needs to document the exact state of the data at each step. What is the most robust solution?

   • AStore the raw data and the final model in the same archive.
   • BImplement a data versioning tool that snapshots the data at each pipeline stage and records the transformation script version.
   • CLog the pipeline execution time and the person who ran it.
   • DSave the final training dataset to a shared folder with a descriptive name.
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