Compunnel Interview Questions, Process, and Tips

Q1. I applied for a project manager position, my questions were a mix of project manager mentioned and business analysis.
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Q1. Another technical round with a different person. More detailed questions on project management and business analysis with some behavioural questions.
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Q1. This was a Fitment round
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Q1. Questions Related to Previous Experience and tools
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Q1. 1. Measures of Dispersion
Ans. 
Measures of Dispersion are used to describe the spread of data around the central tendency.
• Measures of Dispersion include Range, Variance, Standard Deviation, and Interquartile Range.

• Range is the difference between the maximum and minimum values in a dataset.

• Variance measures how far each value is from the mean.

• Standard Deviation is the square root of the variance.

• Interquartile Range is the difference between the 75th
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Q2. Difference between Linear & Logistic Regression ? Give examples ?
Ans. 
Linear regression is used for continuous data while logistic regression is used for categorical data.
• Linear regression predicts a continuous outcome while logistic regression predicts a probability of an event occurring.

• Linear regression uses a straight line to fit the data while logistic regression uses an S-shaped curve.

• Linear regression is used for predicting values like house prices while logistic regression is use...read more
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Q3. What is Feature Selection & Feature Engineering
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Feature selection is the process of selecting relevant features from a dataset, while feature engineering involves creating new features.
• Feature selection helps to reduce the dimensionality of the dataset and improve model performance.

• Feature engineering involves transforming or combining existing features to create new ones that may be more informative.

• Examples of feature engineering include creating interaction terms...read more
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Q4. What is Gradient Descent and where is it used
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Gradient Descent is an optimization algorithm used to minimize the cost function of a machine learning model.
• Gradient Descent is used in machine learning to find the optimal parameters of a model by minimizing the cost function

• It works by iteratively adjusting the parameters in the direction of steepest descent of the cost function

• There are two types of Gradient Descent: Batch Gradient Descent and Stochastic Gradient D...read more
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Q5. What are different types of machine learning with examples
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There are three types of machine learning: supervised, unsupervised, and reinforcement learning.
• Supervised learning involves training a model on labeled data to make predictions on new data. Example: predicting house prices based on features like location, size, etc.

• Unsupervised learning involves finding patterns in unlabeled data. Example: clustering customers based on their purchasing behavior.

• Reinforcement learning ...read more
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Q1. Experience in US Staffing
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Q2. How do you approach prospects? Sales Strategy
Ans. 
I approach prospects with research, personalized outreach, and a focus on building relationships to drive sales success.
• Conduct thorough research on the prospect's industry and pain points to tailor my approach.

• Utilize social media platforms like LinkedIn to connect and engage with prospects before reaching out.

• Craft personalized emails that address specific needs and demonstrate how our solutions can help.

• Follow up wi...read more
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1 hour assessment and coding test related to Job Duties

Q1. Spring AOP cross cutting concerns implementation
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Q2. Experience with AWS SQS, S3, CloudFront, RDS, Aurora, Lambda.
Ans. 
Experience with various AWS services like SQS, S3, CloudFront, RDS, Aurora, and Lambda.
• Experience setting up and managing SQS for message queuing

• Experience using S3 for scalable storage solutions

• Experience configuring CloudFront for content delivery

• Experience working with RDS and Aurora for database management

• Experience developing serverless applications with Lambda functions
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Q1. Where do you see yourself in the next 5 years in compunnel staffing?
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