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A dataset may be used for training and evaluation. What must be checked?
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- A dataset may be used for training and evaluation. What must be checked?
- Whether its file opens in the training pipeline
- Whether its permissions cover each intended use separately
- Whether both uses require the same model
- Whether the dataset has a large number of rows
- Why does data governance track embeddings and cached responses?
- They contain no source-related information
- They are always public
- They cannot be deleted
- Derived assets may retain information or dependencies from source data
- A deletion procedure removes the uploaded file but not its index copies. What remains unresolved?
- The model’s user interface
- The source file’s format
- Deletion across relevant derived stores and copies
- The data catalog title
- Who should approve changes to data schema and validation rules?
- An assigned owner responsible for quality and change control
- Any user who uploads a file
- Model AI itu sendiri
- The storage vendor only
- What does lineage provide?
- Proof that data use is permitted
- A guarantee of data quality
- A substitute for retention rules
- A way to trace where data and derived outputs came from
- A dataset was collected for retrieval but is now proposed for model training. What is needed?
- Assume access for one use implies every use
- Review whether its permissions and purpose cover training
- Delete the data immediately
- Change the dataset label only
- What should a correction procedure specify?
- Who handles it, how it propagates, and how completion is verified
- Only where the data catalog is stored
- The model’s average token count
- Whether an error appears in a presentation
- Why revisit data access and retention when integrations change?
- Integrations never affect data flow
- Retention applies only before deployment
- New services can change who sees the data and how long it persists
- A new connector automatically narrows access