Which UK visa category for MRCP UK PACES clinical exams

I want to address this question to Gayot Fow. I am a Doctor who has completed the MRCP UK part 1 and 2 exams and now I have secured admissions to sit for the part 2 clinicals in the UK. I have the admission documents and the Visa letter from the royal college of Physicians. My question is which Visa category should I select in order to sit for this exam. Because there is no specific visa in order to enter the UK for an exam. My circumstances in terms of employment and finances are all in order. In addition, as I want to make the most of a trip to the UK I want my 2 kids and husband also to accompany me so that we can do some sightseeing. Hence, please give me your thoughts on which visa category I should apply for all of us. We have traveled to the UK in 2017 for tourism on a 7 day trip. Also have a schengen visa in 2017 and we traveled to 9 European countries. In addition I have traveled to the UK another time before, 19 years ago while I was studying medicine in Moscow. I am a South Asian. We also have a good travel history.

Scraping Clinical Studies database (via ClinicalTrials.Gov)

I have written the following script to scrape data from the U.S. National Library of Medicine website ClinicalTrials.Gov based on an NCTID.

def clinicalTrialsGov (nctid):     data = BeautifulSoup(requests.get("https://clinicaltrials.gov/ct2/show/" + nctid + "?displayxml=true").text, "xml")     subset = ['study_type', 'allocation', 'intervention_model', 'primary_purpose', 'masking', 'enrollment', 'official_title', 'condition', 'minimum_age', 'maximum_age', 'gender', 'healthy_volunteers', 'phase', 'primary_outcome', 'secondary_outcome', 'number_of_arms']     tag_matches = data.find_all(subset)     tag_dict = dict((str('ct' + tag_matches[i].name.capitalize()), tag_matches[i].text) for i in range(0, len(tag_matches)))     tag_dict = multipleFields(data, ['intervention_name'], tag_dict)     tag_dict = multipleFields(data, ['intervention_type'], tag_dict)     tag_dict = multipleFields(data, ['arm_group_type'], tag_dict)     tag_dict['ctID'] = nctid     #for key in tag_dict:         #print(key + ': ' + tag_dict[key])     return removeEmptyKeys(tag_dict)  def multipleFields (data, subset, tagDict):     fields = data.find_all(subset)     field = []     try:         for each in fields:             field.append(each.text)         tagDict[str('ct' + subset[0].capitalize())] = ", ".join(field)         return tagDict     except:         return tagDict  def removeEmptyKeys (dict1):     newDict = {}     for key in dict1:         if str(dict1[key]) is not '':             newDict[key] = dict1[key]     return newDict 

What can I do to make this process more efficent?

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