Samsung Purchase Engineer Interview Questions and Answers

Discussion about Basic Information

Q1. I was applying for CAD engineer so asking about CAD commands and telecommunications. Prepare with your skills.
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Q1. Discussion about Work experience, CTC, work Location.
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Q1. What is mean by testing
Ans. 
Testing is the process of evaluating a system or component to determine if it satisfies specified requirements.
• Testing is done to identify defects or errors in software or hardware.

• It involves executing a system or component with the intent of finding bugs or verifying its functionality.

• Testing can be performed at various levels such as unit testing, integration testing, system testing, and acceptance testing.

• Different...read more
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Q2. Why we are doing testing
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Testing is done to ensure that the software meets the requirements and is of high quality.
• To identify defects and errors in the software

• To ensure that the software meets the specified requirements

• To improve the quality of the software

• To increase customer satisfaction

• To reduce the risk of software failure

• To ensure that the software is reliable and secure
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Q1. What are your salary expectations?
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Q2. What is your family background?
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Q3. Share details of your previous job.
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Q4. What are your strengths and weaknesses?
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Q5. Tell me about yourself.
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Q1. Explain k means algorithm
Ans. 
K-means algorithm is a clustering algorithm that partitions data into k clusters based on similarity.
• Iteratively assigns data points to the nearest cluster centroid

• Calculates new centroids based on the mean of data points in each cluster

• Repeats until centroids no longer change significantly

• Used in data mining, image segmentation, and customer segmentation
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Q2. What process did you follow for data preprocessing
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I followed a systematic approach for data preprocessing including data cleaning, normalization, encoding, and feature scaling.
• Performed data cleaning to handle missing values and outliers

• Normalized numerical features to bring them to a similar scale

• Encoded categorical variables using techniques like one-hot encoding

• Applied feature scaling to ensure all features contribute equally to the model
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