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Applied Materials Data Scientist Interview Questions and Answers
Applied Materials Data Scientist Interview Experiences
3 interviews found
Anonymous
Selected
I applied via LinkedIn and was interviewed in Jul 2024. There were 2 interview rounds.
(2 Questions)
- Q1. Explain about the project
- Ans.
Developed a machine learning model to predict customer churn for a telecom company
Used historical customer data to train the model
Implemented various classification algorithms such as logistic regression, random forest, and XGBoost
Evaluated model performance using metrics like accuracy, precision, recall, and F1 score
- Q2. Basic SQLQuestions
(2 Questions)
- Q1. Intermediate level SQL questions
- Q2. Intermediate level python/machine learning questions
Selected
I applied via Referral and was interviewed before Aug 2023. There were 2 interview rounds.
(2 Questions)
- Q1. Program complexity.
- Q2. Code efficiency
(2 Questions)
- Q1. What do you like about AMAT?
- Ans.
AMAT is a leading provider of semiconductor manufacturing equipment and services.
AMAT is known for its cutting-edge technology and innovation in the semiconductor industry.
I appreciate AMAT's commitment to research and development, constantly pushing the boundaries of what is possible.
The company has a strong global presence and a track record of delivering high-quality products and services.
AMAT's focus on sustainabil...
- Q2. How do you like working?
- Ans.
I enjoy working in a collaborative environment where I can use my analytical skills to solve complex problems.
I thrive in environments where I can work with a team to brainstorm ideas and solutions.
I appreciate opportunities to use data analysis techniques to uncover insights and drive decision-making.
I value a work culture that encourages continuous learning and professional growth.
I find satisfaction in overcoming ch...
Selected
I applied via Referral and was interviewed before May 2023. There were 3 interview rounds.
(1 Question)
- Q1. How Decision tree works
- Ans.
Decision tree is a predictive modeling tool that uses a tree-like graph of decisions and their possible consequences.
Decision tree splits data into subsets based on the value of a certain attribute
It recursively divides data into smaller subsets until a stopping criterion is met
Each internal node represents a decision based on an attribute, and each leaf node represents the outcome
(1 Question)
- Q1. How pressure varies wrt temperature
- Ans.
Pressure generally increases with temperature due to the kinetic energy of gas molecules.
Pressure is directly proportional to temperature in a closed system (Boyle's Law).
As temperature increases, gas molecules move faster and collide with the container walls more frequently, increasing pressure.
For example, a balloon inflated indoors may burst when taken outside on a hot day due to increased pressure from higher tempe
(1 Question)
- Q1. What challenges you faced
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Namita Menon
(1 Question)
- Q1. Dropout in. Deep learning
Selected
I applied via Referral and was interviewed before Oct 2023. There was 1 interview round.
(2 Questions)
- Q1. What is Adaboost?
- Ans.
Adaboost is a machine learning algorithm that combines multiple weak learners to create a strong learner.
Adaboost stands for Adaptive Boosting.
It works by adjusting the weights of incorrectly classified instances so that subsequent weak learners focus more on them.
The final prediction is made by combining the predictions of all the weak learners, weighted by their accuracy.
Example: Adaboost is commonly used in face det
- Q2. Print the binary tree in different order
- Ans.
Printing a binary tree in different orders
Use inorder traversal to print the binary tree in ascending order
Use preorder traversal to print the binary tree in root-left-right order
Use postorder traversal to print the binary tree in left-right-root order
Skills evaluated in this interview
Selected
I applied via Company Website and was interviewed before Jul 2023. There were 3 interview rounds.
Binary tree question was asked
(1 Question)
- Q1. Several tech question related to past projects and random forest etc were asked.
(1 Question)
- Q1. Tell me about yourself and several other questions
I applied via Naukri.com and was interviewed in May 2021. There were 4 interview rounds.
Interview Questionnaire
3 Questions
- Q1. 1.About projects that you did. I was working on ADAS project. I explained those to them
- Q2. Convolution neural network basics like variance, loss function hyperparameter tuning, regularization techniques
- Q3. Logical questions. And, realize them in any programming language
Interview Preparation Tips
In my case Two technical interviews were smooth. The panel got impressed and they even sent connection requests in LinkedIn. They were happy. And, informed me about final round.
The strange part is the HR never called me. And, after couple of days I dropped a mail but no response. It was quite unprofessional.
(1 Question)
- Q1. Dropout in. Deep learning
Selected
I applied via Referral and was interviewed before Oct 2023. There was 1 interview round.
(2 Questions)
- Q1. What is Adaboost?
- Ans.
Adaboost is a machine learning algorithm that combines multiple weak learners to create a strong learner.
Adaboost stands for Adaptive Boosting.
It works by adjusting the weights of incorrectly classified instances so that subsequent weak learners focus more on them.
The final prediction is made by combining the predictions of all the weak learners, weighted by their accuracy.
Example: Adaboost is commonly used in face det
- Q2. Print the binary tree in different order
- Ans.
Printing a binary tree in different orders
Use inorder traversal to print the binary tree in ascending order
Use preorder traversal to print the binary tree in root-left-right order
Use postorder traversal to print the binary tree in left-right-root order
Skills evaluated in this interview
I was interviewed in Nov 2023.
(1 Question)
- Q1. Explain about ROC curve
- Ans.
ROC curve is a graphical representation of the performance of a classification model.
ROC curve stands for Receiver Operating Characteristic curve.
It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings.
The area under the ROC curve (AUC) is a measure of how well the model can distinguish between classes.
A perfect model would have an AUC of 1, while a ra
Skills evaluated in this interview
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Technical Lead
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Senior Software Engineer
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Software Engineer
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