Advertising supported content sampling is ubiquitous in online markets for digital information goods. Yet, little is known about the profit impact of sampling when it serves the dual purpose of disclosing content quality and generating advertising revenue. This paper proposes an analytical framework to study the optimal content strategy for online publishers and shows how it is determined by characteristics of both the content market and the advertising market. The strategy choice is among a paid content strategy, a sampling strategy, and a free content strategy, which follow from the publisher's decisions concerning the size of the sample and the price of the paid content. We show that a key driver of the strategy choice is how sampling affects the prior expectations of consumers, who learn about content quality from the inspection of the free samples. Surprisingly, we find that it can be optimal for the publisher to generate advertising revenue by offering free samples even when sampling reduces both prior quality expectations and content demand. In addition, we show that it can be optimal for the publisher to refrain from revealing quality through free samples when advertising effectiveness is low and content quality is high. To illustrate, we relate our framework to the newspaper industry, where the sampling strategy is known as the "metered model."