Google AI-Powered Performance Ads case studies
Google AI-Powered Performance Ads · Put your knowledge to work in context.
This independent study page provides practice for Google AI-Powered Performance Ads. Its editorial learning areas include Performance Max, Search, signals and creative controls; Conversion goals, values, budgets and value-based bidding; Measurement quality, consent, attribution and causal evaluation. The questions develop subject knowledge and application through worked reasoning, distinctions between competing answers, and practical scenarios. Study the relevant guide before attempting a mixed set, then explain the evidence or calculation that supports each answer. Topics on this exam: Performance Max, Search, signals and creative controls, Conversion goals, values, budgets and value-based bidding, and Measurement quality, consent, attribution and causal evaluation. The Google AI-Powered Performance Ads case studies are 4 cases and about 662 words.
1An audience signal is mistaken for an exclusive audience
A fictional retailer supplies a list of previous customers as an audience signal in a Performance Max asset group. The campaign report later includes new customers who are not on that list.
169 words, under 1 min2A revenue ratio is described as net profit
A fictional advertiser reports conversion value of 8000 and advertising cost of 2000, both in the same currency and covering the same supplied period. The value represents recorded sales revenue, not contribution after product, fulfillment or return costs.
159 words, under 1 min3Two actions report the same purchase
A fictional store reports a purchase directly through a Google Ads conversion action and separately imports the corresponding Analytics event as another action. Both actions are included in the relevant bidding goal.
170 words, under 1 min4Attribution credit is presented as incremental lift
A fictional team presents a dashboard showing purchases attributed to a campaign and claims every one is a purchase that would not have occurred without advertising. The supplied dashboard applies an attribution method but contains no randomized holdout, suitable counterfactual comparison or analysis of customers who would have bought anyway.
164 words, under 1 min