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CIMB bank Data Scientist Interview Questions and Answers
CIMB bank Data Scientist Interview Experiences
2 interviews found
Anonymous
Model Training based on the dataset and problem statement provided.
(2 Questions)
- Q1. Asked about Roc Auc
- Ans.
ROC AUC measures the performance of a binary classifier, indicating its ability to distinguish between classes.
ROC (Receiver Operating Characteristic) curve plots True Positive Rate vs. False Positive Rate.
AUC (Area Under the Curve) quantifies the overall ability of the model to discriminate between positive and negative classes.
An AUC of 0.5 indicates no discrimination (random guessing), while an AUC of 1.0 indicates ...
- Q2. What is difference between supervised and unsupervised learning
- Ans.
Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.
Supervised learning requires a target variable to predict, while unsupervised learning does not.
In supervised learning, the model learns from labeled examples, while in unsupervised learning, the model finds patterns in data.
Examples of supervised learning include regression and classification tasks, while clusteri...
Skills evaluated in this interview
I applied via Approached by Company and was interviewed in May 2024. There was 1 interview round.
(2 Questions)
- Q1. Xgboost in-depth
- Ans.
Xgboost is a popular machine learning algorithm known for its speed and performance in handling large datasets.
Xgboost stands for eXtreme Gradient Boosting, which is an optimized implementation of gradient boosting.
It is widely used in Kaggle competitions and other machine learning tasks due to its high accuracy and efficiency.
Xgboost uses a technique called boosting, where multiple weak learners are combined to create...
- Q2. Type 1 & 2 error.
Skills evaluated in this interview
Top trending discussions
Interview questions from similar companies
I applied via Recruitment Consulltant and was interviewed before Aug 2021. There was 1 interview round.
(1 Question)
- Q1. Difference between CNN and MLP
- Ans.
CNN is used for image recognition while MLP is used for general classification tasks.
CNN uses convolutional layers to extract features from images while MLP uses fully connected layers.
CNN is better suited for tasks that require spatial understanding like object detection while MLP is better for tabular data.
CNN has fewer parameters than MLP due to weight sharing in convolutional layers.
CNN can handle input of varying ...
Interview Preparation Tips
Skills evaluated in this interview
I applied via Walk-in and was interviewed in Mar 2020. There was 1 interview round.
Interview Questionnaire
10 Questions
- Q1. What is R square and how R square is different from Adjusted R square
- Ans.
R square is a statistical measure that represents the proportion of the variance in the dependent variable explained by the independent variables.
R square is a value between 0 and 1, where 0 indicates that the independent variables do not explain any of the variance in the dependent variable, and 1 indicates that they explain all of it.
It is used to evaluate the goodness of fit of a regression model.
Adjusted R square t...
- Q2. Explain what do u understand by the team WOE and IV. What's the importance. Advantages and disadvantages
- Ans.
WOE (Weight of Evidence) and IV (Information Value) are metrics used for feature selection and assessing predictive power in models.
WOE transforms categorical variables into continuous variables, making them more suitable for modeling.
IV quantifies the predictive power of a feature by measuring the separation between the good and bad outcomes.
For example, if a feature has an IV of 0.3, it indicates strong predictive po...
- Q3. What are variable reducing techniques
- Ans.
Variable reducing techniques are methods used to identify and select the most relevant variables in a dataset.
Variable reducing techniques help in reducing the number of variables in a dataset.
These techniques aim to identify the most important variables that contribute significantly to the outcome.
Some common variable reducing techniques include feature selection, dimensionality reduction, and correlation analysis.
Fea...
- Q4. Which test is used in logistic regression to check the significance of the variable
- Ans.
The Wald test is used in logistic regression to check the significance of the variable.
The Wald test calculates the ratio of the estimated coefficient to its standard error.
It follows a chi-square distribution with one degree of freedom.
A small p-value indicates that the variable is significant.
For example, in Python, the statsmodels library provides the Wald test in the summary of a logistic regression model.
- Q5. How to check multicollinearity in Logistic regression
- Ans.
Multicollinearity in logistic regression can be checked using correlation matrix and variance inflation factor (VIF).
Calculate the correlation matrix of the independent variables and check for high correlation coefficients.
Calculate the VIF for each independent variable and check for values greater than 5 or 10.
Consider removing one of the highly correlated variables or variables with high VIF to address multicollinear...
- Q6. Difference between bagging and boosting
- Ans.
Bagging and boosting are ensemble methods used in machine learning to improve model performance.
Bagging involves training multiple models on different subsets of the training data and then combining their predictions through averaging or voting.
Boosting involves iteratively training models on the same dataset, with each subsequent model focusing on the samples that were misclassified by the previous model.
Bagging reduc...
- Q7. Explain the logistics regression process
- Ans.
Logistic regression is a statistical method used to analyze and model the relationship between a binary dependent variable and one or more independent variables.
It is a type of regression analysis used for predicting the outcome of a categorical dependent variable based on one or more predictor variables.
It uses a logistic function to model the probability of the dependent variable taking a particular value.
It is commo...
- Q8. Explain Gini coefficient
- Ans.
Gini coefficient measures the inequality among values of a frequency distribution.
Gini coefficient ranges from 0 to 1, where 0 represents perfect equality and 1 represents perfect inequality.
It is commonly used to measure income inequality in a population.
A Gini coefficient of 0.4 or higher is considered to be a high level of inequality.
Gini coefficient can be calculated using the Lorenz curve, which plots the cumulati...
- Q9. Difference between chair and cart
- Ans.
A chair is a piece of furniture used for sitting, while a cart is a vehicle used for transporting goods.
A chair typically has a backrest and armrests, while a cart does not.
A chair is designed for one person to sit on, while a cart can carry multiple items or people.
A chair is usually stationary, while a cart is mobile and can be pushed or pulled.
A chair is commonly found in homes, offices, and public spaces, while a c...
- Q10. How to check outliers in a variable, what treatment should you use to remove such outliers
- Ans.
Outliers can be detected using statistical methods like box plots, z-score, and IQR. Treatment can be removal or transformation.
Use box plots to visualize outliers
Calculate z-score and remove data points with z-score greater than 3
Calculate IQR and remove data points outside 1.5*IQR
Transform data using log or square root to reduce the impact of outliers
Interview Preparation Tips
Skills evaluated in this interview
I applied via Approached by Company and was interviewed before Sep 2021. There were 3 interview rounds.
(1 Question)
- Q1. Projects and Data Science concepts
(1 Question)
- Q1. Python and coding skills
Interview Preparation Tips
DEVANG RATHOD
(1 Question)
- Q1. Central Limit Theorem
- Ans.
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...
I applied via Referral and was interviewed in Nov 2024. There were 2 interview rounds.
(4 Questions)
- Q1. Types of Chunking in data preparation in RAG
- Q2. How Embedding works in Vector Databases
- Ans.
Embeddings in vector databases represent data points as dense vectors for efficient similarity search and retrieval.
Embeddings convert categorical data into continuous vector space, enabling mathematical operations.
For example, words can be represented as vectors in Word2Vec, capturing semantic relationships.
Vector databases store these embeddings, allowing for fast nearest neighbor searches.
Applications include recomm...
- Q3. Explain ARIMA model
- Ans.
ARIMA is a statistical model used for forecasting time series data by capturing trends and seasonality.
ARIMA stands for AutoRegressive Integrated Moving Average.
It combines three components: AR (AutoRegressive), I (Integrated), and MA (Moving Average).
AR component uses past values to predict future values.
I component involves differencing the data to make it stationary.
MA component models the error of the prediction as...
- Q4. How can we decide to choose Linear Regression for a business problem
- Ans.
Linear Regression is chosen for its simplicity, interpretability, and effectiveness in modeling linear relationships in data.
Linear relationship: Use when the relationship between independent and dependent variables is linear, e.g., predicting sales based on advertising spend.
Continuous outcome: Suitable for predicting continuous outcomes, like house prices based on features like size and location.
Interpretability: Pro...
(4 Questions)
- Q1. What is token and it's limit for Open Source LLMs
- Ans.
Tokens are units of text processed by LLMs, with limits varying by model, affecting input/output length.
A token can be as short as one character or as long as one word (e.g., 'cat' is one token, 'chatGPT' is one token).
Common token limits for open-source LLMs range from 512 to 4096 tokens, depending on the architecture.
For example, GPT-2 has a limit of 1024 tokens, while GPT-3 can handle up to 4096 tokens.
Exceeding tok...
- Q2. Difference of a Regression and Time Series problem
- Ans.
Regression predicts continuous outcomes; time series analyzes data points over time for trends and patterns.
Regression focuses on relationships between variables (e.g., predicting house prices based on features).
Time series analyzes data collected at regular intervals (e.g., stock prices over time).
Regression can be used for static datasets, while time series requires temporal ordering.
In regression, predictors can be ...
- Q3. Advantage of LSTM over RNN
- Ans.
LSTMs effectively handle long-term dependencies, overcoming RNNs' vanishing gradient problem.
LSTMs use memory cells to store information over long sequences, unlike RNNs which forget earlier data.
They employ gates (input, output, forget) to control the flow of information, enhancing learning.
LSTMs are better suited for tasks like language modeling and time series prediction where context is crucial.
For example, in sent...
- Q4. Performance Metrics for Logistic Regression
Skills evaluated in this interview
I appeared for an interview in May 2022.
Round duration - 60 Minutes
Round difficulty - Easy
(1 Question)
Round duration - 60 Minutes
Round difficulty - Easy
There were 10 MCQs ranging from Aptitude to Programming MCQs to basics of Data Science.
The coding question only the optimized solution was accepted
- Q1.
Special Sum of Array Problem Statement
Given an array 'arr' containing single-digit integers, your task is to calculate the total sum of all its elements. However, the resulting sum must also be a single-...
- Ans.
Calculate the total sum of array elements until a single-digit number is obtained by repeatedly summing digits.
Iterate through the array and calculate the sum of all elements.
If the sum is a single-digit number, return it. Otherwise, repeat the process of summing digits until a single-digit number is obtained.
Return the final single-digit sum.
(1 Question)
Round duration - 45 minutes
Round difficulty - Easy
The interview happened in the evening. It was an online video call.
The interviewer was very cooperative. I would say it was rather a discussion session between us.
- Q1.
Clone a Linked List with Random Pointers
Given a linked list where each node contains two pointers: one pointing to the next node and another random pointer that can point to any node within the list (or ...
- Ans.
Create a deep copy of a linked list with random pointers.
Iterate through the original linked list and create a new node for each node in the list.
Store the mapping of original nodes to new nodes in a hashmap to handle random pointers.
Update the random pointers of new nodes based on the mapping stored in the hashmap.
Return the head of the copied linked list.
Round duration - 10 Minutes
Round difficulty - Easy
It was late night
It was a telephonic call
Interview Preparation Tips
Tip 1 : Start your preparation early. Start from the very basics before directly moving onto DSA. Get a grasp of the basics in each topic. Practice different varieties of questions from each topic. I would recommend at least 200 questions of DSA.
Tip 2 : Revise your projects before you attend any interview. This is extremely important. You must be able to clearly explain your project along with your role in the project in layman terms to the interviewer.
Tip 3 : Grind hard to achieve your goals but don't take much stress. There's a long way to go.
Tip 1 : Never, I say never put false things or your friends project in your resume
Tip 2 : Make a 1 page resume. Make your resume in such a way that the interviewer must be able to see the things you want him to see in the very first scan.
Skills evaluated in this interview
Interview Questionnaire
3 Questions
- Q1. Mainly resume based. In detail from the project.
- Q2. Softmax vs sigmoid
- Ans.
Softmax and sigmoid are both activation functions used in neural networks.
Softmax is used for multi-class classification problems, while sigmoid is used for binary classification problems.
Softmax outputs a probability distribution over the classes, while sigmoid outputs a probability for a single class.
Softmax ensures that the sum of the probabilities of all classes is 1, while sigmoid does not.
Softmax is more sensitiv...
- Q3. Logistics regression (multiclass)
Interview Preparation Tips
Skills evaluated in this interview
I applied via LinkedIn and was interviewed in Jul 2024. There were 3 interview rounds.
Assignment on credit risk
(1 Question)
- Q1. Hyperparameter tuning
(1 Question)
- Q1. Case study for problem solving
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