Alphago and AlphaGo zero use random play to generate data and use the data to train DNN. "Random play" means that there is a positive probability for AlphaGo to play some suboptimal moves based on the current DNN; this is for exploring and learning purposes (please correct me if my understanding is wrong).
In the real tournament, does AlphaGo still play the random moves? Is the random play feature only used in the training phase?
If AlphaGo does not play a random move in the real competition, then I think AlphaGo is not learning in that competition. Human players do similar "random play": they usually play some random moves or strange moves in minor contests, just to test out new strategies; in major tournaments, they will be more serious and play less unprepared moves.
So, a related and broader question is: does AlphaGo learn from the game it is playing with the human in real-time?
I think the second question is less important because AlphaGo's learning curve is extremely flat compared to humans: AlphaGo learns epsilon from one single game while human can learn a lot.