Axtria Data Scientist Interview Questions and Answers

Mostly data related question and two coding questions which we had to do in python only

Q1. They asked the mathematics behind Adam Optimizer
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Q2. What is attention in term of data science?
Ans. 
Attention in data science refers to the mechanism that allows models to focus on specific parts of the input data.
• Attention mechanisms help models to weigh the importance of different input features.

• They are commonly used in natural language processing tasks such as machine translation and text summarization.

• Attention can improve the performance of models by allowing them to selectively focus on relevant information.

• Ex...read more
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Q3. Questions on whether you would use precision and recall in real life example for measuring accuracy.
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Q1. How would you judge the efficiency of LLMs
Ans. 
Efficiency of LLMs can be judged based on various factors such as accuracy, speed, resource consumption, and interpretability.
• Evaluate accuracy by comparing LLM predictions with ground truth labels

• Assess speed by measuring the time taken for LLM to process data

• Analyze resource consumption in terms of memory and computational power usage

• Consider interpretability by examining how easily LLM decisions can be understood

• Use...read more
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Q2. Questions on CV mostly
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Hacker Rank Test on coding questions

Q1. Whats difference between KNN and Kmeans
Ans. 
KNN is a supervised learning algorithm used for classification and regression, while Kmeans is an unsupervised clustering algorithm.
• KNN is a supervised learning algorithm that classifies a new data point based on the majority class of its k-nearest neighbors.

• Kmeans is an unsupervised clustering algorithm that partitions data into k clusters based on similarity.

• KNN requires labeled training data, while Kmeans does not r...read more
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Q2. Coding a training pipeline
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Coding a training pipeline involves creating a process to train machine learning models efficiently.
• Define the data preprocessing steps

• Split the data into training and validation sets

• Choose a machine learning algorithm to train the model

• Tune hyperparameters to optimize model performance

• Evaluate the model using metrics like accuracy or loss
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Basic leet code questions easy to a bit of medium

Q1. Explain your projects from resume
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Q2. Cross questions on the terms in resume
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Q3. How do you handle class imbalance
Ans. 
Handling class imbalance involves techniques like resampling, using different algorithms, and adjusting class weights.
• Use resampling techniques like oversampling the minority class or undersampling the majority class.

• Try using different algorithms that are less sensitive to class imbalance, such as Random Forest or XGBoost.

• Adjust class weights in the model to give more importance to the minority class.
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Q1. Basic HR round was there
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3 question were asked in 90 min time

Q1. What is precison ?
Ans. 
Precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
• Precision is calculated as TP / (TP + FP), where TP is true positives and FP is false positives.

• It measures the accuracy of positive predictions made by the model.

• A high precision indicates that the model is good at predicting positive cases without many false positives.

• For example, in a binary classificatio...read more
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Q2. What is large lang. model ?
Ans. 
A large language model is a type of artificial intelligence model that is capable of understanding and generating human language at a large scale.
• Large language models use deep learning techniques to process and generate text.

• Examples include GPT-3 (Generative Pre-trained Transformer 3) and BERT (Bidirectional Encoder Representations from Transformers).
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Q1. Basicn details to check for qualifications
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Q1. About my projects
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Q1. More details about ML models
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