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10+ Cigna Interview Questions and Answers
Q1. Which is the best place you will visit for weekend
The best place to visit for the weekend would be a peaceful beach resort with stunning views and relaxing atmosphere.
Consider visiting a beach resort for a relaxing weekend getaway
Look for a place with beautiful views and peaceful surroundings
Check for activities like water sports or spa treatments to enhance your experience
Q2. What is ai? New technologies useful for image generation
AI, or artificial intelligence, is the simulation of human intelligence processes by machines, especially computer systems.
AI involves the development of algorithms that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.
Machine learning is a subset of AI that focuses on the development of algorithms that allow computers to learn from and make predictions or decisions based on d...read more
Q3. What is your data science experience?
I have 5 years of experience in data science, including working on various machine learning projects.
5 years of experience in data science
Proficient in machine learning techniques
Worked on various projects involving data analysis and modeling
Q4. What is your shirt brand
I prefer to wear casual shirts from various brands like Uniqlo, H&M, and Zara.
Uniqlo
H&M
Zara
Q5. Favorite programming language?
Python is my favorite programming language due to its simplicity, readability, and versatility.
Python is known for its clean and readable syntax, making it easy to learn and use.
It has a large standard library and many third-party libraries for various applications.
Python is versatile and can be used for web development, data analysis, artificial intelligence, and more.
Q6. What is Support vector machine?
Support vector machine is a supervised machine learning algorithm used for classification and regression tasks.
SVM finds the hyperplane that best separates the classes in the feature space
It works well for high-dimensional data and is effective in cases with clear margin of separation
Can handle non-linear data by using kernel trick to map data into higher dimensional space
Popular kernels include linear, polynomial, radial basis function (RBF)
Q7. How do you design a database schema for optimal performance?
Designing a database schema for optimal performance involves normalization, indexing, partitioning, and denormalization.
Normalize the database to reduce redundancy and improve data integrity.
Index frequently queried columns to speed up search operations.
Partition large tables to distribute data across multiple storage devices for faster access.
Consider denormalization for read-heavy applications to reduce join operations.
Use appropriate data types and constraints to optimize ...read more
Q8. Describe your experience with cloud computing platforms.
I have extensive experience working with cloud computing platforms such as AWS, Azure, and Google Cloud.
Developed and deployed AI models on AWS SageMaker
Utilized Azure Machine Learning for model training and deployment
Implemented serverless functions on Google Cloud Platform for AI applications
Q9. Types of ML algorithms
ML algorithms are techniques used to enable machines to learn from data and make predictions or decisions without being explicitly programmed.
Supervised learning: algorithms learn from labeled training data, e.g. linear regression, support vector machines
Unsupervised learning: algorithms find patterns in data without labeled responses, e.g. clustering, dimensionality reduction
Reinforcement learning: algorithms learn to make decisions by interacting with an environment, e.g. Q...read more
Q10. what is the differenece between ml and dl?
ML stands for machine learning, while DL stands for deep learning. DL is a subset of ML that uses neural networks to model and solve complex problems.
ML (Machine Learning) is a broader concept that involves algorithms and models that can learn from and make predictions or decisions based on data.
DL (Deep Learning) is a subset of ML that uses neural networks with multiple layers to model and solve complex problems.
DL requires a large amount of data and computational power comp...read more
Q11. Nave bayes algorithm
Naive Bayes algorithm is a probabilistic classifier based on Bayes' theorem with the assumption of independence between features.
Naive Bayes is commonly used in text classification, spam filtering, and recommendation systems.
It is easy to implement and works well with large datasets.
The algorithm assumes that all features are independent of each other, which is a simplifying but often unrealistic assumption.
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