An AI project team has prepared the data and is ready to proceed with model development.
Which action should the project manager perform next?

• A.Conduct a final assessment of the data quality
• B.Document the performance metrics for the model
• C.Ensure go/no-go questions have well-defined answers
• D.Prepare a report on the model's scalability

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A team is getting ready to begin working on a machine learning project. They need to build a data preparation pipeline. A team member suggests reusing the same pipeline created for their last project.
What is wrong with this suggestion?

• A.Pipelines are pattern- and model-needs specific.
• B.There is no issue due to the fact that pipelines can be reused as needed between projects.
• C.Pipelines are pattern-needs specific; however, as long as it is the same pattern the pipeline can be reused.
• D.Pipelines are model operationalization-needs specific.

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A company needs to launch an AI application quickly to be the first to the market. The project team has decided to use pretrained models for their current AI project iteration.
What is a key result of leveraging pretrained models?

• A.The team can see a reduction in the overall project timeline.
• B.The team can encounter compatibility issues with existing systems.
• C.The custom project development time can increase due to adjustments.
• D.The project can face unexpected scalability challenges.

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A project team is tasked with ensuring all AI-related decisions and actions are documented comprehensively for future auditing purposes. They need to track the reasons for specific AI choices, their impacts, and any issues encountered during the implementation.
What is represented in this situation?

• A.Operational efficiency
• B.Strategic alignment
• C.Compliance management
• D.Transparency

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A project team is overseeing the data evaluation for an AI model predicting customer churn. They observed that the model ' s predictions are biased toward a particular class.
What is an effective technique to mitigate this bias?

• A.Using synthetic data generation
• B.Implementing stratified sampling
• C.Increasing the batch size
• D.Adjusting the hyperparameters

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