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Company: PayPal
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Salary : 5 - 19 LPA (Expected)
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#include <bits/stdc++.h>
using namespace std;
int Solution::findRadius(vector<int> &A, vector<int> &B) {
if (B.empty()) return INT_MAX;
sort(B.begin(), B.end());
int radius = 0;
for (int a : A) {
auto it = lower_bound(B.begin(), B.end(), a);
int dist = INT_MAX;
if (it != B.end()) {
dist = min(dist, abs(*it - a));
}
if (it != B.begin()) {
dist = min(dist, abs(*(it - 1) - a));
}
radius = max(radius, dist);
}
return radius;
}
Wifi Router Installation
C++
100% Correct Code
Amazon ML school exam
Telegram:- @allcoding1
using namespace std;
int Solution::findRadius(vector<int> &A, vector<int> &B) {
if (B.empty()) return INT_MAX;
sort(B.begin(), B.end());
int radius = 0;
for (int a : A) {
auto it = lower_bound(B.begin(), B.end(), a);
int dist = INT_MAX;
if (it != B.end()) {
dist = min(dist, abs(*it - a));
}
if (it != B.begin()) {
dist = min(dist, abs(*(it - 1) - a));
}
radius = max(radius, dist);
}
return radius;
}
Wifi Router Installation
C++
100% Correct Code
Amazon ML school exam
Telegram:- @allcoding1
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 Machine Learning Summer School:
Exam Date: 3rd August 2025
Exam Duration: 60 mins
10:30 AM
Test format:
Section 1 - MCQ section - 20 questions
Section 2 - DSA type coding section - 2 questions
Answers available 👇👇👇
Telegram channel:- @allcoding1
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 Machine Learning Summer School:
Exam Date: 3rd August 2025
Exam Duration: 60 mins
10:30 AM
Test format:
Section 1 - MCQ section - 20 questions
Section 2 - DSA type coding section - 2 questions
Answers available 👇👇👇
Telegram channel:- @allcoding1
❤1
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
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
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;
}
}
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;
}
}
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