While digital twin engineering for manufacturing is an interesting topic for students and trainees, getting access to production lines from industry is more than challenging. Educational institutions could overcome this challenge by using Fischertechnik production lines and their sensory information as original system for creating digital twins. This paper presents a digital twin exemplar for a Fischertechnik-based production line, realizing analysis, simulation, and optimization scenarios that was developed by four students during the fourth semester of their Bachelor studies in Computer Science and Data Science. The exemplar visualizes the recorded MQTT data of a Fischertechnik production line, enables the simulation of different scenarios with additionally developed virtual sensors, e.g., running certain machines faster might cause machines to overheat, and calculates optimizations for running machines, e.g., based on their throughput, or consumed energy. This and similar exemplars enable students to learn early on about the process of engineering digital twins, the use of different technologies, the relationship between data and its internal representation and different forms of analyses, what-if simulations and optimizations.