The correct answer is Maximum training duration and target metric threshold.

Azure Automated Machine Learning supports exit criteria that stop the experiment when a set time budget is exhausted and when a target model performance is reached. Setting a Maximum training duration enforces an overall time limit for the AutoML run so it ends when the allotted time elapses. Setting a target metric threshold instructs AutoML to stop the search once a model meets or exceeds the specified primary metric.

Compute resource usage limit and score threshold is incorrect because AutoML exit criteria focus on time and model performance rather than direct resource consumption limits, and the phrase score threshold is ambiguous compared to a configured primary metric goal.

Cluster node quota and error rate limit is incorrect because cluster node quotas are infrastructure constraints and not AutoML early termination settings, and AutoML does not use a generic error rate limit as a standard exit condition.

Maximum concurrent iterations and failure threshold is incorrect because maximum concurrent iterations controls parallelism and does not stop the entire experiment, and a failure threshold is not a standard AutoML exit criterion for ending runs early.

Full AWS Practitioner Certification Question