Manage AI/ML Transparency (e.g., Data Selection, Algorithm Selection) PMI-CPMAI Practice Questions (Page 3)

Questions 11–15

1. 11foundation · easy

   When establishing explainability requirements for an AI/ML model, what should be considered?

   • AThe programming language used for implementation
   • BThe cost of the model's training infrastructure
   • COnly the preferences of the data science team
   • DThe needs of different stakeholders, such as technical teams, regulators, and end-users
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2. 12foundation · easy

   What should a transparent report on preprocessing steps include?

   • AOnly the final dataset size after preprocessing
   • BDetails of cleaning, transformation, and augmentation applied to the data
   • CThe model's performance metrics
   • DThe names of the tools used for model training
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4. 13foundation · easy

   What is a key element to include in a transparent report about the data sources used in an AI/ML model?

   • AThe programming language used for data analysis
   • BThe number of rows in the training dataset
   • CThe names of the data engineers who processed the data
   • DThe origin and nature of the data, including how it was collected
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5. 14foundation · easy

   What is the key to communicating model decisions effectively to stakeholders?

   • AUsing explainability methods and levels of detail appropriate for each audience
   • BAvoiding any explanation to protect the model
   • CSharing only the model's accuracy score
   • DProviding the same technical explanation to everyone
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6. 15foundation · easy

   Which of the following is an example of applying an interpretability technique to understand overall model behavior?

   • AExplaining a single prediction for a specific customer
   • BPlotting the model's training loss over epochs
   • CComputing global feature importance to see which features most influence predictions across the dataset
   • DListing the hyperparameters used in the final model
   Reveal Solution Discussion Select an answer first