Tata Elxsi Interview Questions, Process, and Tips

Q1. Introduction about yourself
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The aptitude test was the 1st level and it was a Medium level

Q1. The technical round is little bit complex
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Q1. Basic questions about experience
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Q2. Why job switch from current company
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Topic is based on engineering and aptitude

C programming ,control sysytem

Q1. HR round discussion
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Q2. Whisvh is your native?
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Q3. What your family does?
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Q4. Where did completed ?
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Q1. Previous company experience
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Q2. Challenges and achievements
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Q1. Explain Quantization, Convolution
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Quantization is the process of mapping input values from a continuous range to a discrete set of values. Convolution is a mathematical operation that combines two functions to produce a third function.
• Quantization reduces the precision of data by mapping it to a smaller set of values. For example, converting a grayscale image from 256 levels to 8 levels.

• Convolution involves sliding one function over another and multipl...read more
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Q2. Swap without using 3rd variable
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Swapping two numbers without using a third variable
• Use arithmetic operations to swap values

• Example: a = 5, b = 10; a = a + b; b = a - b; a = a - b;

• Ensure no overflow or underflow occurs
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Q3. Program to multiply two matrices using CUDA
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Using CUDA to multiply two matrices efficiently.
• Allocate memory on GPU for matrices A, B, and C

• Copy matrices A and B from host to device

• Launch CUDA kernel to perform matrix multiplication

• Copy result matrix C from device to host

• Free memory on GPU
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Q1. Merge 2 Sorted arrays
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Merge two sorted arrays into a single sorted array.
• Create a new array to store the merged result

• Use two pointers to iterate through the two input arrays and compare elements

• Add the smaller element to the new array and move the pointer for that array forward

• Repeat until all elements from both arrays are merged
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Q2. Explain Transformer architecture
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Transformer architecture is a deep learning model that uses self-attention mechanism to process sequential data.
• Transformer architecture consists of an encoder and a decoder.

• Self-attention mechanism allows the model to weigh the importance of different input tokens when making predictions.

• Transformer models have been widely used in natural language processing tasks such as machine translation and text generation.
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