SPRINKLR Data Scientist Interview Questions, Process, and Tips

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 ?
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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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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
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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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