Describe the Solutions for Code, Data, and Model Traceability CERTIFIED-AI-EXPERT Practice Questions (Page 1)

Questions 1–5

1. 1application · medium

   An autonomous vehicle company must document which model version was tested in a specific simulation run. The simulation uses a specific build of the perception code. What is the best way to record this information for future analysis?

   • AStore the model file in the simulation output folder.
   • BLog the model version and the code commit hash in the simulation run's metadata.
   • CRecord the date and time of the simulation in a shared calendar.
   • DSave the simulation output video in a folder named after the model version.
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2. 2foundation · easy

   What is the purpose of integrating code, data, and model traceability?

   • ATo reduce the storage cost of AI artifacts
   • BTo provide a complete audit trail from source code to deployed model
   • CTo automate model deployment
   • DTo improve model accuracy
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4. 3application · medium

   A marketing analytics team is building a customer segmentation model. They use a data pipeline that pulls data from multiple sources and applies several transformations. They need to document the exact source and transformation for each feature used in the model. What should they use?

   • AA shared folder with the raw data files from each source.
   • BA model registry that stores the model and its performance metrics.
   • CA data dictionary that lists the feature names and their data types.
   • DA data lineage tool that tracks each feature back to its source and the transformation code version.
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5. 4foundation · easy

   Which practice is used to track code versions and commits for an AI pipeline?

   • AStoring all code files in a shared network drive with timestamped filenames
   • BUsing a data catalog to record code execution logs
   • CUsing a version control system like Git to record changes and commit metadata
   • DKeeping a spreadsheet of code changes updated manually
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6. 5expert · hard

   An insurance company is deploying a claims-processing model. The compliance team requires a complete audit trail that links the production model to the code and data used to create it. The company uses a CI/CD pipeline for code, a data lake for raw data, and a model registry for models. However, the pipeline does not automatically record the data version used in each training run. What is the most effective way to close this gap?

   • AModify the CI/CD pipeline to capture the data version and pass it to the model registry as metadata for each training run.
   • BUse the model registry to store the raw data files directly.
   • CAsk the data science team to manually note the data version in the model registry after each training run.
   • DStore the data version in a separate document that is not linked to the model registry.
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