OK, worked a bit on this today.  I haven't given up (though I did brute force the answers with another account, I still want to get this one legit).

So I still can't get #9 but for #10 I'm getting the correct answer.  This is what I'm using:

def construct_examples(numericalized_sentences, vocabulary, num_examples=int(1e6), n=5, sg=True, k=0):
  examples = []
  while True:
    # TODO: select a random sentence index using random.randint and get that 
    # sentence. Be careful to avoid indexing errors.
    sentence_idx = random.randint(0,len(numericalized_sentences)-1)
    sentence = numericalized_sentences[sentence_idx]
    
    # TODO: Select a random window index using random.randint 
    # and obtain that window of size n. Be careful to avoid indexing errors.
    window_idx = random.randint(0,len(sentence)-1)
    window = sentence[window_idx:(window_idx+n)]
        
    if len(window) <= n//2:
      continue
      
    # TODO: Get the center word and the context words 
    center_word = window[int(len(window)//2)]
    context_words = window
    context_words.remove(center_word)
    
    # TODO: Create examples using the guidelines above
    if sg: # if Skip-Gram
      context_word = context_words[random.randint(0, len(context_words)-1)]
      example = [center_word,context_word]
    else: # if CBOW
      example = [context_words,center_word]
      #if len(window) < n:  Note: I changed this line because this is where I kept hanging up
      if len(window) >= n:
        continue
      
    if k > 0: # if doing negative sampling
      samples = [random.randint(0, len(vocabulary.index_to_word)-1) 
                 for _ in range(k)]
      example.append(samples)
      
    examples.append(example)
    if len(examples) >= num_examples:
      break
  
  return examples

So I'm trying to work the next part (Hands-on: Define the Word2Vec models) but my brain is tired so I'm going to give it a rest until my PyLadies group on Sunday afternoon.