Acmegrade Data Scientist Interview Questions and Answers

Q1. Tell me about yourself
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Q2. Questions from projects which was mentioned on resume
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Q1. Fitment based questions
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Q2. Explain a project
Ans. 
Developed a machine learning model to predict customer churn for a telecom company.
• Collected and cleaned customer data including usage patterns and demographics

• Used classification algorithms like Random Forest and Logistic Regression to build the model

• Evaluated model performance using metrics like accuracy, precision, and recall
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Math, English, reasoning

Q1. Asked me about expected salary
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Q1. What is difference between lstm and rnn
Ans. 
LSTM is a type of RNN that addresses the vanishing gradient problem by using memory cells.
• RNN stands for Recurrent Neural Network, a type of neural network that processes sequential data.

• LSTM stands for Long Short-Term Memory, a type of RNN that includes memory cells to retain information over long sequences.

• LSTM is designed to overcome the vanishing gradient problem, which occurs when training RNNs on long sequences.

• L...read more
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Q1. What are different evaluation matrix ?
Ans. 
Evaluation matrices are used to assess the performance of models in data science.
• Confusion matrix: used to evaluate the performance of classification models.

• Precision, recall, and F1 score: measures for binary classification models.

• Mean squared error (MSE): evaluates the performance of regression models.

• R-squared: assesses the goodness of fit for regression models.

• Area under the ROC curve (AUC-ROC): evaluates the perfo...read more
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Q1. Statistics Probability questions K mean
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Q1. KNN, Regression basics, R Squared, Batch normalization, LSTM vs GRU etc. Describe your present project.
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Q2. Questions on Padding
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Q1. Describe your strength and weakness.
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Q2. Why do you want to join Infosys?
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Q1. Explain difference between Faster-RCNN and Yolo v3.
Ans. 
Faster-RCNN and Yolo v3 are both object detection algorithms, but differ in their approach and performance.
• Faster-RCNN uses a two-stage approach, first generating region proposals and then classifying them.

• Yolo v3 uses a single-stage approach, directly predicting bounding boxes and class probabilities.

• Faster-RCNN is generally more accurate but slower, while Yolo v3 is faster but less accurate.

• Faster-RCNN is better suit...read more
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Q2. How RNN handles exploding/vanishing Gradient?
Ans. 
RNN uses techniques like gradient clipping, weight initialization, and LSTM/GRU cells to handle exploding/vanishing gradients.
• Gradient clipping limits the magnitude of gradients during backpropagation.

• Weight initialization techniques like Xavier initialization help in preventing vanishing gradients.

• LSTM/GRU cells have gating mechanisms that allow the network to selectively remember or forget information.

• Batch normaliza...read more
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Data science For 1 hr

Q1. Project related questions from your CV
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Q1. Question on transformers
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Q2. Comparison of transfer learning and fintuning.
Ans. 
Transfer learning involves using pre-trained models on a different task, while fine-tuning involves further training a pre-trained model on a specific task.
• Transfer learning uses knowledge gained from one task to improve learning on a different task.

• Fine-tuning involves adjusting the parameters of a pre-trained model to better fit a specific task.

• Transfer learning is faster and requires less data compared to training a...read more
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