Diverse design styles and the associated complex circuit specifications make testing by functional methods prohibitively expensive for most analog and mixed-signal circuits. Hence, fault- oriented test techniques, similar to those for digital circuits, are being actively pursued in the research community. However, the large number of failure mechanisms in analog and mixed-signal circuits presents a major bottleneck in terms of fault simulation effort and in the construction of compact fault dictionaries. In this paper, we propose an efficient fault sampling and clustering methodology to construct compact fault dictionaries for complex analog and mixed- signal circuits, with significantly reduced fault simulation effort. For a given fault universe, we show that only a small fraction of the total faults need to be simulated and stored in the fault dictionary to achieve near-perfect diagnosis. A fault sampling algorithm is proposed to identify the faults that contribute to diagnostic information with minimal simulation effort. A fault clustering algorithm is applied to the resulting fault syndromes to identify equivalent syndromes with maximal diagnostic information content. For complex analog/mixed-signal circuits, the fault sampling and fault clustering algorithms are applied hierarchically to construct the compact fault dictionaries, without sacrificing diagnostic accuracy.