Bayesian sampling can be paired with an early stopping policy is correct.

Bayesian sampling means using Bayesian optimization to model the relationship between hyperparameters and the objective and it can be combined with early stopping policies to terminate unpromising trials early and save compute and time. Early stopping policies monitor intermediate metrics and stop trials that are unlikely to outperform the best candidates so the search can focus on more promising configurations.

Bayesian sampling is restricted to only uniform choice and quniform parameter types is incorrect because Bayesian approaches accept a wide range of parameter types including categorical, integer, continuous, log scaled, and conditional parameters and they are not limited to only uniform or quniform choices.

Bayesian sampling always finds the absolute best configuration and is the slowest approach is incorrect because Bayesian optimization is a sample efficient search strategy but it does not guarantee finding the global optimum and runtime depends on how expensive each trial is so it is not inherently the slowest method in all situations.

Vertex AI Vizier is incorrect as an answer in this context because that option is just the name of a service rather than a property of Bayesian sampling and the question asked about what is true about Bayesian sampling rather than about a specific product.

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