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Kill the enemy C++ Amazon 1.15 PM #include <vector> #include <algorithm> int solve(std::vector<int> &A, int B) { long long m1 = 0, m2 = 0; for (int val : A) { if (val > m1) { m2 = m1; m1 = val; } else if (val > m2) { m2 = val; } } long long b = B; long long s = m1 + m2; if (s == 0) { return b > 0 ? -1 : 0; } long long k = b / s; int ans = k * 2; long long rem = b % s; if (rem == 0) { return ans; } else if (rem <= m1) { return ans + 1; } else { return ans + 2; } }

M1: Encode sequential order T1: Regularization M16: Likelihood × Prior ML2: SGD can escape local minima due to its noisy updates ML3: Recursive Feature Elimination (RFE) ML4: Gini Index M4: It overfits the training data M7: 0 M2: Strong negative linear relationship S13: No real solution S17: Local minimum S23: Converges by Limit Comparison with 1/n² S30: Collect recent user data and evaluate model drift S31: Data leakage inflated model performance S39: 6/216 Amazon ML School MCQ Answers 1:15 PM

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S2: 4xy S7: 6 S12: 0 Q4: 1/2 S22: (2, 3) S28: Target/Mean Encoding S29: TimeSeriesSplit S38: 15 S43: 2/5 S59: Mode > Median > Mean ML - 2: The data has a Gaussian distribution ML - 7: Updating prior beliefs with observed data using Bayes' theorem ML - 12: The probability distribution over actions given states ML - 17: Internal covariate shift ML - 23: Boosting reduces bias, bagging reduces variance ML - 24: Binary Cross-Entropy S48: 30/84 S60: 150 S53: 2/3 S68: Prior × Likelihood

S2: 4xy S7: 6 S12: 0 Q4: 1/2 S22: (2, 3) S28: Target/Mean Encoding S29: TimeSeriesSplit S38: 15 S43: 2/5 S59: Mode > Median > Mean ML - 2: The data has a Gaussian distribution ML - 7: Updating prior beliefs with observed data using Bayes' theorem ML - 12: The probability distribution over actions given states ML - 17: Internal covariate shift ML - 23: Boosting reduces bias, bagging reduces variance ML - 24: Binary Cross-Entropy S48: 30/84 S60: 150 S53: 2/3 S68: Prior × Likelihood Amazon ML School 100% Correct MCQ Answers 10:30 AM

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