Daily Coding Quiz: Sunday, 8 November 2026

All 10 coding questions with answers and explanations. 0 players took part.

1. What does len({"a": 1, "b": 2, "a": 3}) return?

   Python · easy

   1. 1
   2. 2
   3. 3
   4. An error

   Answer: B. 2

   Explanation: Dictionary keys are unique. The second "a" overwrites the first, leaving {"a": 3, "b": 2}.

2. How do you correctly check for missing values in SQL?

   SQL · easy

   1. WHERE salary = NULL
   2. WHERE salary IS NULL
   3. WHERE salary = ''
   4. WHERE salary == NULL

   Answer: B. WHERE salary IS NULL

   Explanation: NULL is not equal to anything, not even NULL: salary = NULL is never true. Use IS NULL / IS NOT NULL.

3. In k-nearest neighbours (k-NN) classification, how is a new point labelled?

   Machine Learning · easy

   1. By the majority label among its k closest training points
   2. By fitting a line through k points
   3. By the label of the farthest point
   4. By averaging all training labels

   Answer: A. By the majority label among its k closest training points

   Explanation: k-NN stores the training data and votes among the k nearest neighbours (by distance) at prediction time.

4. What does a higher “temperature” do when sampling from an LLM?

   AI & GenAI · easy

   1. Makes answers more deterministic
   2. Makes output more random and varied
   3. Makes the model faster
   4. Increases the context window

   Answer: B. Makes output more random and varied

   Explanation: Temperature divides the logits before softmax. Above 1 flattens the distribution (more surprising tokens); near 0 almost always picks the top token.

5. Which task draws a box around every object and names its class?

   Computer Vision · easy

   1. Image classification
   2. Object detection
   3. Semantic segmentation
   4. Image captioning

   Answer: B. Object detection

   Explanation: Detection gives a box and label per object (e.g. YOLO). Classification labels the whole image; segmentation labels every pixel.

6. What is printed?

   Python · medium

   d = {"a": 1}
   print(d.get("b", 0), d.setdefault("b", 5), d)

   1. 0 5 {'a': 1, 'b': 5}
   2. 0 0 {'a': 1}
   3. None 5 {'a': 1, 'b': 5}
   4. KeyError

   Answer: A. 0 5 {'a': 1, 'b': 5}

   Explanation: get returns the default without inserting. setdefault inserts the key with the default when it is missing and returns it.

7. What is printed?

   Python · medium

   funcs = [lambda: i for i in range(3)]
   print([f() for f in funcs])

   1. [0, 1, 2]
   2. [2, 2, 2]
   3. [0, 0, 0]
   4. An error

   Answer: B. [2, 2, 2]

   Explanation: Closures capture the variable, not its value. By the time the lambdas run, i is 2. Bind it now with lambda i=i: i.

8. What is printed?

   Python · hard

   class A:
       def hi(self): return "A"
   class B(A):
       def hi(self): return "B" + super().hi()
   class C(A):
       def hi(self): return "C" + super().hi()
   class D(B, C):
       pass
   print(D().hi())

   1. BA
   2. BCA
   3. CBA
   4. BACA

   Answer: B. BCA

   Explanation: D’s method resolution order is D → B → C → A. super() follows the MRO, not the parent class, so B calls C, which calls A.

9. In MySQL InnoDB, what is a covering index?

   SQL · hard

   1. An index on every column
   2. An index that holds every column a query needs, so the table rows never have to be read
   3. The clustered primary-key index
   4. An index on a JSON column

   Answer: B. An index that holds every column a query needs, so the table rows never have to be read

   Explanation: If the query’s WHERE, SELECT and ORDER BY columns are all in the index, InnoDB answers from the index alone. EXPLAIN shows “Using index”.

10. You tune hyperparameters with cross-validation on the full dataset and report the best CV score as your final result. What is the problem?

    Machine Learning · hard

    1. None
    2. The score is optimistically biased: you selected the best of many tries on the same folds. Use nested CV or a held-out test set
    3. Cross-validation can’t tune hyperparameters
    4. The model will underfit

    Answer: B. The score is optimistically biased: you selected the best of many tries on the same folds. Use nested CV or a held-out test set

    Explanation: Picking the maximum over many configurations overfits to the validation folds. Nested cross-validation, or a final test set untouched during tuning, gives an honest estimate.

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