  Multiple choice exams are widely used to assess candidates across a diverse
range of domains and tasks. To moderate question quality, newly proposed
questions often pass through pre-test evaluation stages before being deployed
into real-world exams. Currently, this evaluation process is manually
intensive, which can lead to time lags in the question development cycle.
Streamlining this process via automation can significantly enhance efficiency,
however, there's a current lack of datasets with adequate pre-test analysis
information. In this paper we analyse a subset of the public Cambridge
Multiple-Choice Questions Reading Database released by Cambridge University
Press & Assessment; a multiple-choice comprehension dataset of questions at
different target levels, with corresponding candidate selection distributions.
We introduce the task of candidate distribution matching, propose several
evaluation metrics for the task, and demonstrate that automatic systems trained
on RACE++ can be leveraged as baselines for our task. We further demonstrate
that these automatic systems can be used for practical pre-test evaluation
tasks such as detecting underperforming distractors, where our detection
systems can automatically identify poor distractors that few candidates select.
