Informatica Business Solutions 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
Ans. 
Adam Optimizer combines momentum and adaptive learning rates for efficient gradient descent.
• Adam stands for Adaptive Moment Estimation.

• It maintains two moving averages: m (first moment) and v (second moment).

• m is the exponentially weighted average of gradients.

• v is the exponentially weighted average of squared gradients.

• Update rules: m_t = beta1 * m_{t-1} + (1 - beta1) * g_t and v_t = beta2 * v_{t-1} + (1 - beta2) * g_...read more
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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
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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Q1. Basicn details to check for qualifications
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Q1. About my projects
Ans. 
I have worked on various data science projects, focusing on predictive modeling and data visualization.
• Developed a predictive model for customer churn using logistic regression.

• Created an interactive dashboard for sales data visualization using Tableau.

• Implemented a recommendation system for an e-commerce platform using collaborative filtering.

• Conducted A/B testing to optimize marketing strategies, resulting in a 15% i...read more
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Q1. More details about ML models
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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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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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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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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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