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Data Governance for AI: Provenance, Rights and Quality Controls Models Depend On

Versioning Datasets and Reproducing a Training Run

Quiz: Versioning Datasets and Reproducing a Training Run

Retention, Deletion and Erasure Requests After Training

Quiz: Retention, Deletion and Erasure Requests After Training

Access Control in Feature Stores and Vector Databases

Quiz: Access Control in Feature Stores and Vector Databases

Synthetic Data: Uses, Limits and Disclosure

Quiz: Synthetic Data: Uses, Limits and Disclosure

Article 10 Data Governance Duties for High Risk Systems

Quiz: Article 10 Data Governance Duties for High Risk Systems

Writing Dataset Documentation That Answers an Audit

Quiz: Writing Dataset Documentation That Answers an Audit

Final exam: Data Governance for AI: Provenance, Rights and Quality Controls Models Depend On

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Quiz: Versioning Datasets and Reproducing a Training Run

Data Governance for AI: Provenance, Rights and Quality Controls Models Depend On
Versioning Datasets and Reproducing a Training Run
Quiz: Versioning Datasets and Reproducing a Training Run

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Question 1 of 5

1. Question

What is a key benefit of versioning datasets in machine learning workflows?

It allows teams to use any dataset without documentation
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It ensures consistency across experiments by tracking changes
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It removes the need for data preprocessing steps
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It makes data access less secure
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Question 2 of 5

2. Question

Which of the following is recommended when assigning version numbers to datasets?

Use random numbers to avoid confusion
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Assign numbers based on team preferences
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Assign numbers that reflect data collection dates or major changes
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Avoid versioning altogether
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Question 3 of 5

3. Question

Why is it important to document data quality and sample sizes in dataset versioning?

To make datasets easier to store
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To ensure that version control includes metadata about data quality, sample sizes, and field definitions
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To reduce computational costs
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To prevent data from being stored in repositories
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Question 4 of 5

4. Question

What must teams capture to reproduce machine learning training runs successfully?

Only the final model output
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The dataset versions, model configurations, and computational environments
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The names of team members who worked on the project
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The cost of computational resources used
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Question 5 of 5

5. Question

What role do storage solutions play in effective dataset versioning?

They are not important for versioning systems
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They must support quick retrieval of versions and provide audit trails
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They should only store data files without metadata
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They should prevent access to older versions
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