Evertz Microsystems Data Science Intern Interview Questions and Answers

Q1. Discussion regarding expectations
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Q1. Ai and networks
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Q1. Joining and stripend negotiations
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Q1. Sales pitch for the product
Ans. 
Our product is a revolutionary software that streamlines sales processes and boosts productivity.
• Increase sales efficiency by automating repetitive tasks

• Improve customer engagement with personalized communication

• Track sales performance with real-time analytics

• Easy integration with existing CRM systems

• Cost-effective solution for small and large businesses
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Q1. Strong basics required
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Q1. Behavioural questions
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Q1. Will you relocate to Krishnagiri
Ans. 
Yes, I am willing to relocate to Krishnagiri.
• I am open to new opportunities and experiences in different locations.

• I have researched about Krishnagiri and I find it an interesting place to work and live.

• I am ready to adapt to a new environment and make the most out of the opportunity.

• I believe relocating to Krishnagiri will enhance my professional growth and broaden my horizons.
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Q2. Asked about family background
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Q1. What metrics can be used to evaluate the performance of a regression model?
Ans. 
Various metrics like Mean Squared Error, R-squared, Mean Absolute Error can be used to evaluate regression model performance.
• Mean Squared Error (MSE) - measures the average of the squares of the errors or deviations

• R-squared (R2) - indicates the proportion of the variance in the dependent variable that is predictable from the independent variables

• Mean Absolute Error (MAE) - measures the average of the absolute errors b...read more
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Q2. What methods can be used to remove a specific type of noise from an image?
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Methods like median filtering, Gaussian filtering, and wavelet denoising can be used to remove noise from an image.
• Median filtering replaces each pixel's value with the median value of its neighborhood.

• Gaussian filtering uses a weighted average of neighboring pixels to smooth out noise.

• Wavelet denoising decomposes the image into different frequency bands and removes noise from each band separately.
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Machine learning-based problem.