The problem of real-time remote tracking and reconstruction of a two-state Markov process is considered here. A transmitter sends samples from an observed information source to a remote monitor over an unreliable wireless channel. The receiver, in turn, performs an action according to the state of the reconstructed source. We propose a state-aware randomized stationary sampling and transmission policy which accounts for the importance of different states of the information source, and their impact on the goal of the communication process. We then analyze the performance of the proposed policy, and compare it with existing goal-oriented joint sampling and transmission policies, with respect to a set of performance metrics. Specifically, we study the real-time reconstruction error, the cost of actuation error, the consecutive error, and a new metric, coined importance-aware consecutive error. In addition, we formulate and solve a constrained optimization problem that aims to obtain the optimal sampling probabilities that minimize the average cost of actuation error. Our results show that in the scenario of constrained sampling generation, the optimal state-aware randomized stationary policy outperforms all other sampling policies for fast evolving sources, and, under certain conditions, for slowly varying sources. Otherwise, a semantics-aware policy performs better only when the source is slowly varying.

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