The Turing Test (TT) is an experimental paradigm to test for intelligence, where an entity’s intelligence is inferred from its ability, during a text-based conversation, to be recognized as a human by the human judge. The advantage of this paradigm is that it encourages alternative versions of the test to be designed; and it can include any field of human endeavour. However, it has two major problems: (i) it can be passed by an entity that produces uncooperative but human-like responses (Artificial Stupidity); and (ii) it is not sensitive to how the entity produces the conversation (Blockhead). In light of these two problems, I propose a new version of the TT, the Questioning Turing Test (QTT). In the QTT, the task of the entity is not to hold a conversation, but to accomplish an enquiry with as few human-like questions as possible. The job of the human judge is to provide the answers and, like in the TT, to decide whether the entity is human or machine. The QTT has the advantage of parametrising the entity along two further dimensions in addition to ‘human-likeness’: ‘correctness’, evaluating if the entity accomplishes the enquiry; and ‘strategicness’, evaluating how well the entity carries out the enquiry, in terms of the number of questions asked – the fewer, the better. Moreover, in the experimental design of the QTT, the test is not the enquiry per se, but rather the comparison between the performances of humans and machines. The results gained from the QTT show that its experimental design minimises false positives and negatives; and avoids both Artificial Stupidity and Blockhead.