Infosys Data Scientist Interview Questions and Answers

Q1. Deployment of RAG
Ans. 
RAG (Retrieval-Augmented Generation) deployment enhances AI models by integrating external data sources for improved responses.
• Integrate RAG with existing NLP models to enhance context understanding.

• Utilize APIs to fetch real-time data, improving response accuracy.

• Example: Using RAG in customer support to pull relevant FAQs from a database.

• Implement caching mechanisms to optimize retrieval speed.

• Monitor and evaluate mo...read more
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Q2. Building of RAG
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RAG (Red, Amber, Green) is a visual tool for assessing project status and risk levels.
• RAG status indicates project health: Red = critical issues, Amber = potential risks, Green = on track.

• Example: A project with budget overruns may be marked Red.

• RAG can be used in dashboards for quick visual assessments.

• Regular updates to RAG status help in proactive risk management.
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Q1. Explain XGBoost algoritm
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XGBoost is a powerful machine learning algorithm known for its speed and performance in handling large datasets.
• XGBoost stands for eXtreme Gradient Boosting, which is an implementation of gradient boosting machines.

• It is widely used in machine learning competitions and is known for its speed and performance.

• XGBoost uses a technique called boosting, where multiple weak learners are combined to create a strong learner.

• It...read more
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Q2. XgBoost algorithm has 10-20 features. How are the splits decided, on which feature are they going to be divided?
Ans. 
XgBoost algorithm uses a greedy approach to determine splits based on feature importance.
• XgBoost algorithm calculates the information gain for each feature to determine the best split.

• The feature with the highest information gain is chosen for the split.

• This process is repeated recursively for each node in the tree.

• Features can be split based on numerical values or categories.

• Example: If a feature like 'age' has the hi...read more
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Q3. Do you have any experience on cloud platform?
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Q4. What is entropy, information gain?
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Entropy is a measure of randomness or uncertainty in a dataset, while information gain is the reduction in entropy after splitting a dataset based on a feature.
• Entropy is used in decision tree algorithms to determine the best feature to split on.

• Information gain measures the effectiveness of a feature in classifying the data.

• Higher information gain indicates that a feature is more useful for splitting the data.

• Entropy ...read more
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Q5. What is hypothesis testing?
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Q6. Explain precision and recall, when are they used in which scenario?
Ans. 
Precision and recall are metrics used in evaluating the performance of classification models.
• Precision measures the accuracy of positive predictions, while recall measures the ability of the model to find all positive instances.

• Precision = TP / (TP + FP)

• Recall = TP / (TP + FN)

• Precision is important when false positives are costly, while recall is important when false negatives are costly.

• For example, in a spam email de...read more
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Q7. What is data imbalance?
Ans. 
Data imbalance refers to unequal distribution of classes in a dataset, where one class has significantly more samples than others.
• Data imbalance can lead to biased models that favor the majority class.

• It can result in poor performance for minority classes, as the model may struggle to accurately predict them.

• Techniques like oversampling, undersampling, and using different evaluation metrics can help address data imbala...read more
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Q8. What is SMOTE? Do you have any experience working on Time Series? Code analysis of global variable?
Ans. 
SMOTE stands for Synthetic Minority Over-sampling Technique, used to balance imbalanced datasets by generating synthetic samples.
• SMOTE is commonly used in machine learning to address class imbalance by creating synthetic samples of the minority class.

• It works by generating new instances of the minority class by interpolating between existing instances.

• SMOTE is particularly useful in scenarios where the minority class i...read more
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Q9. Find 5th highest salary in every department. What are window functions Difference between union and union all Difference between delete and truncate.
Ans. 
Find the 5th highest salary in each department using SQL queries and understand key SQL concepts.
• Use the ROW_NUMBER() window function to rank salaries within each department.

• Example SQL: SELECT department, salary FROM (SELECT department, salary, ROW_NUMBER() OVER (PARTITION BY department ORDER BY salary DESC) AS rank FROM employees) AS ranked WHERE rank = 5;

• Window functions allow calculations across a set of table rows...read more
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Basic operations on dataframe using Pandas and SQL basics.

Q1. Data preprocessing related questions like steps took. Experience about working projects.
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Q2. Random forest and decision tress related questions
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Q1. Knn and logistic regression
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Q2. Correlation vs covariance
Ans. 
Covariance measures the relationship between two variables, while correlation measures the strength and direction of the relationship.
• Covariance can be positive, negative, or zero, indicating the direction of the relationship between variables.

• Correlation is always between -1 and 1, with 1 indicating a perfect positive relationship, -1 indicating a perfect negative relationship, and 0 indicating no relationship.

• Covaria...read more
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