A digital marketing manager has upgraded their keywords to broad match. Which behavior should they build into their regular cadence of optimization to guide machine learning?

The selection is correct because removing negative keywords that may block relevant traffic is a vital optimization behavior when using broad match, as it ensures that the AI has the necessary "breathing room" to identify and capture high-converting queries that might have been unintentionally excluded. While broad match uses machine learning to look at user intent, smart bidding signals, and landing page content to find new opportunities, legacy negative keyword lists—often built for restrictive exact or phrase match strategies—can act as invisible barriers that prevent the algorithm from reaching valuable audiences. By regularly auditing and pruning these lists, a marketer "cleans" the data signals being fed into the AI, allowing the system to fully explore relevant search variations that align with current consumer trends. This proactive maintenance ensures that the broad match strategy remains agile and expansive, rather than being stifled by outdated constraints, thereby maximizing the campaign's ability to drive incremental growth and discover new, profitable search paths.