This interview is based on the real experience of a doctoral student at a top 50 school in North America. The position is ByteDance Research Scientist Intern. Students have a solid ML theoretical foundation, but when facing ByteDance PhD OA for the first time, they are easily stuck on coding and hand calculation questions. With real-time assistance from Programhelp during the interview, he successfully completed the OA screening and mastered strategies for dealing with complex questions and time constraints.
Interview overview
- Total number of questions:10
- Question type distribution:
- 6 basic ML multiple choice questions
- 1 hand calculation question on neural network
- 1 algorithm question
- 2 ML Coding implementation questions
- Time: Approximately 90–120 minutes
- Difficulty: The foundation is relatively stable, but the investigation ideas are clear + hand calculation + coding process understanding + engineering expression
ByteDance North America PhD Internship OA Questions Detailed Explanation
Q1: Confusion Matrix indicator selection
The question requires choosing among multiple models Recall > 0.9 and FPR < 0.1 Model. Examine candidates' understanding of confusion matrix indicators and their ability to weigh indicators in actual scenarios. Students may get stuck in the confusion between the concepts of Recall and FPR, or they may be confused when quickly judging which one of multiple models meets the conditions. The idea of solving the problem is to first clarify the formulas: Recall = TP / (TP + FN), FPR = FP / (FP + TN), then substitute the TP, FP, FN, and TN data of each model in order for calculation, and finally select the models that meet the conditions. The focus is on understanding the meaning of indicators, quick calculation and judgment.
Q2: Advantages of Ensemble
Examine the advantages of ensemble methods such as Bagging and Boosting. Students tend to confuse the core features of the two. The idea of solving the problem is to first clarify that bagging reduces variance and boosting reduces bias. At the same time, both can improve generalization ability. During the interview, you may be asked how to apply it to practical tasks, such as classification problems or regression problems. When answering the question, you can give examples of Bagging's improvement in the stability of decision trees, Boosting's iterative improvement of weak classifiers, and point out that improving the accuracy of the overall model is the core purpose.
Q3: Logistic Regression Loss
The question asks you to choose a loss function suitable for Logistic Regression. Examine understanding of loss functions for regression and classification tasks. Students may get stuck in confusing MSE and cross-entropy. The correct idea is to make it clear that Logistic Regression is a binary classification problem. The most commonly used loss function is cross-entropy loss, also called log loss. When solving the problem, you can explain its applicability to probability prediction, explain why MSE is not suitable for classification problems, and point out that the optimization goal is to minimize the negative log-likelihood.
Q4: Regularization results in a coefficient of 0
The question examines the impact of regularization such as L0, L1, and L2 on parameter sparsity. Where students get stuck may be in confusing the effects of different regularizations. The idea of solving the problem is to understand that L0 and L1 will produce sparse solutions, and L2 only shrinks the parameters without letting them become 0. You can give an example when answering: L1 regularization will make some coefficients exactly 0, thereby achieving feature selection. The inspection point is to understand the actual effects of different norms on model complexity and feature sparsification.
Q5: Reasons why training loss is getting bigger and bigger
Examine the impact of optimization algorithms and parameter settings on training. Students may be stuck in understanding the consequences of too large a learning rate or inappropriate step size. The idea to solve the problem is to analyze the gradient descent process: if the learning rate is too large, it may cause divergence, and if it is too small, it will converge slowly; if the step size is not set appropriately, the loss will also increase. The gradient update method can be explained through formulas or simple diagrams, and the possible root causes of training anomalies can be explained.
Q6: Decision Tree Split indicator
The question requires the selection of split indicators, including Gini Index, Entropy, and Classification Error. Examine candidates' understanding of feature selection and information gain in decision trees. The stuck point often lies in unclear differences in indicators. The idea of solving the problem is clear: Gini measures purity, Entropy measures information gain, and Classification Error measures classification error rate. When answering, you can use examples to explain why features with large information gain are better.
Q7: Three-layer neural network hand calculation questions
Given inputs, weights, and network structure, manual calculation of the output is required. Examine forward propagation understanding and matrix operation abilities. Students may make errors in matrix dimensions, bias addition, or activation function handling. The idea of solving the problem is to calculate layer by layer, the output of each layer = input × weight + bias, and then apply the activation function. The key to hand calculation problems lies in rigorous operation and step-by-step verification to ensure that the final output is consistent with expectations.
Q8: Find local maximum in list
An algorithm question requires finding the local maximum value in an array. Examine traversal logic and boundary processing capabilities. Students tend to ignore the first and last elements or the situation of consecutive equal values. The idea of solving the problem is to linearly scan the array, compare each element with the adjacent values to the left and right, and process the head and tail separately. The complexity O(n) is sufficient, and all local maxima can be found by judging the conditions. The focus is on boundary conditions and equal sign processing.
Q9: Bagging implementation
Coding questions require the implementation of Bagging, including bootstrap sampling and model fitting. Examine candidates' understanding of the integration method process and Python programming ability. Students may make errors in sampling logic, repeated training of models, or prediction aggregation. The idea of solving the problem is three steps: randomly sampling the training set with replacement, training the base model, and summarizing the final prediction results (classification voting or regression average). Pay attention to the data dimensions and repeated sampling in the code.
Q10: Naive Bayes implementation
Coding question requires implementing Naive Bayes classifier and calculating prior probability and conditional probability. Examine probability statistics and coding abilities. Students' stuck points are prone to errors in feature category statistics, probability smoothing, or prediction calculations. The idea of solving the problem is: first count the prior probability and conditional probability of each category in the training set (possibly using Laplace smoothing), then calculate the posterior probability of each category when predicting the test set, and select the category with the highest probability as the prediction. The focus is on formula understanding and implementation details.
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