Why do you want to join KPIT compare to XXX company?
Member Technical Staff Interview Questions
24,240 member technical staff interview questions shared by candidates
Programming question of reverse number using recursion, some medium level Java, selenium questions, collections
What is a good time to talk with you about this position?
You seem quiet....does that mean you're dumb? I'll probably hire you. Do you think you're smart enough?
Iterative method to find the height of a tree
Which of the following have you ran in your youth care experience? face painting, dancing, story telling songs, etc.
Name a weakness?
The interviewer asked me a very pointed question about one of my past experiences listed on my resume. It had been a while since I'd thought about this particular line item, so I struggled to give specific details when relating my answer. Make sure you thoroughly review your resume before going into the interview - this is where many of the behavioral-based questions will come from!
How do you reverse print a string. After answering the same by providing an out of the box API, the next unexpected question was "What if it does NOT exist". While and 2nd alternative was provided, it was pretty much shot down as being not acceptable.
Gaussian linear models are often insufficient in practical applications, where noise can be heavy- tailed. In this problem, we consider a linear model of the form yi = a · xi + b + ei. The (ei) are independent noise from a distribution that depends on x as well as on global parameters; however, the noise distribution has conditional mean zero given x. The goal is to derive a good estimator for the parameters a and b based on a sample of observed (x, y) pairs. 1.1 Instructions: 1. Load the data, which is provided as (x, y) pairs in CSV format. Each file contains a data set generated with different values of a and b. The noise distribution, conditional on x, is the same for all data sets. 2. Formulate a model for the data-generating process. 3. Based on your model, formulate a loss function for all parameters: a, b, and any additional parameters needed for your model. 4. Solve a suitable optimization problem, corresponding to your chosen loss function, to obtain point estimates for the model parameters. 5. Formulate and carry out an assessment of the quality of your parameter estimates. 6. Try additional models if necessary, repeating steps 2 − 5.
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