CAMS Data Scientist Interview Questions and Answers

Many Mcq,s.Similar to cat exam

Ml case study . Eg loan default prediction

Medium General Aptitude questions and technical(Big Data, Python etc.)

Q1. ML Algorithms (SVM, Random forest, bagging boosting, ridge, etc)
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Q1. Deep equations and understading of DL and ML Algorithms
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Understanding deep equations and algorithms in DL and ML is crucial for a data scientist.
• Deep learning involves complex neural network architectures like CNNs and RNNs.

• Machine learning algorithms include decision trees, SVM, k-means clustering, etc.

• Understanding the math behind algorithms helps in optimizing model performance.

• Equations like gradient descent, backpropagation, and loss functions are key concepts.

• Practica...read more
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Q1. Asked about ml algos
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Q1. Types of Chunking in data preparation in RAG
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Q2. How Embedding works in Vector Databases
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Q3. Explain ARIMA model
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Q4. How can we decide to choose Linear Regression for a business problem
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Q1. What is token and it's limit for Open Source LLMs
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Q2. Difference of a Regression and Time Series problem
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Q3. Advantage of LSTM over RNN
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Q4. Performance Metrics for Logistic Regression
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Python coding question and ML question

Q1. ML questions from resume + general
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Q1. Techno managerial round
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Assignment on credit risk

Q1. Hyperparameter tuning
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Q1. Case study for problem solving
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Q1. Central Limit Theorem
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Central Limit Theorem states that the sampling distribution of the sample mean approaches a normal distribution as the sample size increases.
• The Central Limit Theorem is essential in statistics as it allows us to make inferences about a population based on a sample.

• It states that regardless of the shape of the population distribution, the sampling distribution of the sample mean will be approximately normally distribut...read more
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Q1. Self Intro and projects discussion
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Q2. Feature selection methods
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Feature selection methods help in selecting the most relevant features for building predictive models.
• Feature selection methods aim to reduce the number of input variables to only those that are most relevant.

• Common methods include filter methods, wrapper methods, and embedded methods.

• Examples include Recursive Feature Elimination (RFE), Principal Component Analysis (PCA), and Lasso regression.
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