Daily Coding Quiz: Wednesday, 28 October 2026

All 10 coding questions with answers and explanations. 1 players took part.

1. Accessing the element at index i of an array takes:

   DSA · easy · 0% of players got it right

   1. O(1)
   2. O(log n)
   3. O(i)
   4. O(n)

   Answer: A. O(1)

   Explanation: Array elements sit in contiguous memory, so the address is base + i × size: one calculation, no matter how large the array.

2. Predicting house prices from size and location is which kind of problem?

   Machine Learning · easy · 100% of players got it right

   1. Classification
   2. Regression
   3. Clustering
   4. Dimensionality reduction

   Answer: B. Regression

   Explanation: The target is a continuous number, so it is regression. Predicting a category (spam / not spam) would be classification.

3. A model scores 99% on training data and 62% on test data. This is most likely:

   Machine Learning · easy · 100% of players got it right

   1. Underfitting
   2. Overfitting
   3. Data leakage
   4. A perfect model

   Answer: B. Overfitting

   Explanation: A big gap between training and test performance means the model memorised the training set instead of learning patterns that generalise.

4. What is the worst-case time complexity of quick sort?

   DSA · medium · 0% of players got it right

   1. O(n)
   2. O(n log n)
   3. O(n²)
   4. O(log n)

   Answer: C. O(n²)

   Explanation: If the pivot is always the smallest or largest element (e.g. first element on sorted input), partitions are n−1 and 0, giving n + (n−1) + … = O(n²).

5. Increasing k in k-NN generally:

   Machine Learning · medium · 0% of players got it right

   1. Increases variance, decreases bias
   2. Decreases variance, increases bias (smoother boundary)
   3. Has no effect
   4. Always improves accuracy

   Answer: B. Decreases variance, increases bias (smoother boundary)

   Explanation: k = 1 follows every noisy point (high variance). Larger k averages over more neighbours, smoothing the boundary but possibly missing real detail.

6. What does an ROC-AUC of 0.5 mean?

   Machine Learning · medium · 0% of players got it right

   1. Perfect classifier
   2. No better than random ranking
   3. 50% accuracy exactly
   4. The model is always wrong

   Answer: B. No better than random ranking

   Explanation: AUC is the chance a random positive is scored above a random negative. 0.5 is coin-flip ranking; 1.0 is perfect.

7. A cancer test flags 100 people; 80 really have cancer. Out of 120 people who have cancer, 80 were flagged. What are precision and recall?

   Machine Learning · medium · 0% of players got it right

   1. Precision 80%, recall 66.7%
   2. Precision 66.7%, recall 80%
   3. Both 80%
   4. Precision 40%, recall 80%

   Answer: A. Precision 80%, recall 66.7%

   Explanation: Precision = TP / flagged = 80/100 = 80%. Recall = TP / actual positives = 80/120 ≈ 66.7%.

8. What does this print?

   DSA · hard · 0% of players got it right

   from functools import lru_cache
   
   @lru_cache(None)
   def ways(n, k):
       # ways to write n as an ordered sum of parts each <= k
       if n == 0:
           return 1
       return sum(ways(n - p, k) for p in range(1, min(n, k) + 1))
   
   print(ways(4, 2))

   1. 3
   2. 4
   3. 5
   4. 8

   Answer: C. 5

   Explanation: Ordered sums of 4 using 1s and 2s: 1111, 112, 121, 211, 22 → 5. With k=2 it is the stair-climbing Fibonacci again: ways(4) = 5.

9. The bias–variance decomposition of expected squared error is:

   Machine Learning · hard · 0% of players got it right

   1. Bias + Variance
   2. Bias² + Variance + irreducible noise
   3. Bias × Variance
   4. Variance − Bias²

   Answer: B. Bias² + Variance + irreducible noise

   Explanation: E[(y − ŷ)²] = Bias[ŷ]² + Var[ŷ] + σ². The noise term σ² is a floor no model can beat.

10. Why does logistic regression use log-loss (cross-entropy) instead of mean squared error?

    Machine Learning · hard · 0% of players got it right

    1. MSE cannot be computed for probabilities
    2. With a sigmoid, MSE gives a non-convex loss with weak gradients, while log-loss is convex and matches maximum likelihood
    3. Log-loss is faster to compute
    4. MSE always overfits

    Answer: B. With a sigmoid, MSE gives a non-convex loss with weak gradients, while log-loss is convex and matches maximum likelihood

    Explanation: Cross-entropy is the negative log-likelihood of a Bernoulli model. Combined with a sigmoid it is convex in the weights, so gradient descent finds the global optimum.

Top 10

1. Vaishanavi Patil: 28 points (2/10)

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