(a) Accuracy
Accuracy measures how true (correct) the predictions made by a model are — i.e., the total number of predictions a model gets right.
The passage explicitly states: "Accuracy is an evaluation metric that allows you to measure the total number of predictions a model gets right." Examiners expect you to recall this definition directly. Reliability is not a standard ML metric; Recall measures how many actual positives were found; F1 score balances precision and recall — none of these describe "how true" predictions are.