Axiata Digital & Analytics Data Scientist Interview Questions and Answers for Experienced

DSA was asked. And general coding language questions were asked. Previous experience based questions were asked.

Machine Learning, Generative AI, Deep learning interview questions. 2 Coding problems based on Algorithms.

Q1. One systems design question was there.
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Q2. Situations based problems was there.
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Q3. Questions on Past experiences.
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Q1. Cosine similarity
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Cosine similarity measures the similarity between two non-zero vectors in an inner product space.
• Cosine similarity ranges from -1 to 1, with 1 indicating identical vectors and -1 indicating opposite vectors.

• It is commonly used in information retrieval, text mining, and recommendation systems.

• Formula: cos(theta) = (A . B) / (||A|| * ||B||)

• Example: Calculating similarity between two documents based on their word frequenc
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Q2. What is difference between recall and precission
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Recall is the ratio of correctly predicted positive observations to the all observations in actual class, while precision is the ratio of correctly predicted positive observations to the total predicted positive observations.
• Recall is about the actual positive instances that were correctly identified by the model.

• Precision is about the predicted positive instances and how many of them were actually positive.

• Recall = Tr...read more
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Q3. How to remove stop words and how it works
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Stop words are common words like 'the', 'is', 'and' that are removed from text data to improve analysis.
• Stop words are commonly removed from text data to improve the accuracy of natural language processing tasks.

• They are typically removed before tokenization and can be done using libraries like NLTK or spaCy.

• Examples of stop words include 'the', 'is', 'and', 'in', 'on', etc.
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Q4. Whats the goal of project
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Q1. Pipeline design
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Q1. Machine learning algorithms.
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Machine learning algorithms are tools used to analyze data, identify patterns, and make predictions without being explicitly programmed.
• Machine learning algorithms can be categorized into supervised, unsupervised, and reinforcement learning.

• Examples of machine learning algorithms include linear regression, decision trees, support vector machines, and neural networks.

• These algorithms require training data to learn patte...read more
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Q2. Credit risk life cycle
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Q3. Pandas related questions
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Q1. Steps of developing a credit risk model
Ans. 
Developing a credit risk model involves several steps to assess the likelihood of a borrower defaulting on a loan.
• 1. Define the problem and objectives of the credit risk model.

• 2. Gather relevant data such as credit history, income, debt-to-income ratio, etc.

• 3. Preprocess the data by handling missing values, encoding categorical variables, and scaling features.

• 4. Select a suitable machine learning algorithm such as logi...read more
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Q2. Pandas related questions
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Q3. Bagging and Boosting
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Q1. Explain AIC and BIC
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AIC and BIC are statistical measures used for model selection in the context of regression analysis.
• AIC (Akaike Information Criterion) is used to compare the goodness of fit of different models. It penalizes the model for the number of parameters used.

• BIC (Bayesian Information Criterion) is similar to AIC but penalizes more heavily for the number of parameters, making it more suitable for model selection when the focus...read more
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Q2. Difference between xgboost and lightgbm
Ans. 
XGBoost is a popular gradient boosting library while LightGBM is a faster and more memory-efficient alternative.
• XGBoost is known for its accuracy and performance on structured/tabular data.

• LightGBM is faster and more memory-efficient, making it suitable for large datasets.

• LightGBM uses a histogram-based algorithm for splitting whereas XGBoost uses a level-wise tree growth strategy.
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Q3. Bagging and boosting
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Q1. Pandas basics and SQL joins nlp
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Q1. Explain a project written in resume.
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Q1. Explain a project from you resume.
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Q1. What are your strengths?
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Q1. Difference between supervised and unsupervised learning
Ans. 
Supervised learning uses labeled data to train a model, while unsupervised learning uses unlabeled data.
• Supervised learning requires a target variable for training the model.

• Examples of supervised learning include classification and regression.

• Unsupervised learning finds patterns and relationships in data without a target variable.

• Examples of unsupervised learning include clustering and dimensionality reduction.
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Q2. What is sigmoid function
Ans. 
Sigmoid function is a mathematical function that maps any real value to a value between 0 and 1.
• Used in machine learning for binary classification problems to produce probabilities

• Commonly used in logistic regression

• Has an S-shaped curve

• Equation: f(x) = 1 / (1 + e^(-x))
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Q1. Discussion on projects mentioned in the Resume
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Q2. Two Python live coding questions
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Q3. Few DL, ML, Transformers, and NLP questions Few questions on deployment tools mentioned in the resume
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Q1. General HR discussion
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