ML enhanced software development tooling is changing the way software engineers develop code. While the development of these tools continues to rise, studies have primarily focused on the accuracy and performance of underlying models, rather than the user experience. Understanding how engineers interact with ML enhanced tooling can help us define what successful interactions with ML based assistance look like. We therefore build upon prior research, by comparing software engineers' perceptions of two types of ML enhanced tools, (1) code completion and (2) code example suggestions. We then use our findings to inform design guidance for ML enhanced software development tooling. This research is intended to spark a growing conversation about the future of ML in software development and guide the design of developer tooling.