o9 Solutions Data Scientist Interview Questions and Answers

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
Ans. 
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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Sql and python questions were there with basic logic check

Q1. Python code with funticion
Ans. 
Python code with function
• Define a function using 'def' keyword

• Include parameters inside parentheses

• Use 'return' statement to return a value from the function
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Q2. Sql with case statwment
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Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
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Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
Ans. 
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
• Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.

• Adam optimizer uses adaptive learning rates for each parameter.

• Gradient Descent optimizer has a fixed learning rate for all parameters.

• Adam optimizer includes momentum to speed up convergence.

• Gradient Descent optimizer updates parameters b...read more
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Q3. When to use Relu and when not?
Ans. 
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
• ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.

• Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.

• Consider using Leaky ReLU or Sigmoid for output layers depending on the task.

• ReLU is computationally efficient and helps in preventing the vanishing gradient prob...read more
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Standard question from sql and python in hackerrank

Q1. Reverse a linked list
Ans. 
Reverse a linked list by changing the direction of pointers
• Start with three pointers: current, previous, and next

• Iterate through the linked list, updating pointers to reverse the direction

• Return the new head of the reversed linked list
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Q2. Question based on joins and subquery
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Q1. More question about project
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Q2. What do you know about genAI
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Q1. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...read more
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