In general, query optimizers rely on a cost model to choose an appropriate query execution plan for the given queries. An important key parameter of the cost estimation is the cardinality of sub-expressions of the queries. Traditionally, the optimizers may use the estimation cardinality techniques, which can lead to the estimation errors, and hence the poor execution plans. The exact cardinality approach can be applied to resolve such problem, though its computational expense can be costly. A possible way to improve the efficiency is the query transformation since it can provide the alternation to the optimizers. In this paper, we focus on investigation at the effects of the query transformation to the exact cardinality computing processes. The query transformation techniques to be considered in our work are the traditional but widely applied techniques, i.e. subquery unnesting, group-by view merging, join factorization, and join predicate pushdown. The experiment results on the real-life datasets have been presented to validate such proposed work. © 2011 IEEE.