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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 Campus Placement and was interviewed in Jul 2024. There were 2 interview rounds.
Few standard dsa questions were asked.
(2 Questions)
- Q1. Explain projects
- Ans.
Projects are specific tasks or assignments that require a set of skills and resources to achieve a particular goal or outcome.
Projects involve defining objectives and deliverables
They require planning, execution, and monitoring
Projects often have timelines and budgets
Examples: Data analysis project to identify customer trends, Project to implement a new software system
- Q2. Introduce urself
- Ans.
I am a data analyst with a strong background in statistics and data visualization.
I have a Bachelor's degree in Statistics from XYZ University.
I have 3 years of experience working as a data analyst at ABC Company.
Proficient in using tools like Excel, SQL, and Tableau for data analysis.
I have experience in creating reports and dashboards to present data insights to stakeholders.
Interview Preparation Tips
Selected
I applied via LinkedIn and was interviewed before Dec 2023. There was 1 interview round.
Case study about some machine learning project which i did in thr past.
Interview Questionnaire
1 Question
- Q1. Questions about current and past projects and machine learning concepts.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction then interviewer asked me about current project and technologies using for implementation. Interviewer was very calm and friendly so I was relaxed, in the end we discussed about job profile, work environment and their expectation from me.
Tips: Know your projects work in details, answer to asked questions very brief and precise, if you don't know answer then be truthfull about it.
College Name: IIT Guwahati
I was interviewed in Apr 2017.
Interview Questionnaire
2 Questions
- Q1. Past and current projects work, and few basic machine learning concepts
- Q2. Questions about machine learning concepts, E.g. classification, regression, clustering, over fitting vs under fitting, boosting etc.
Interview Preparation Tips
Experience: It was telephonic interview, started with introduction, asked me about projects, technologies.
Tips: Know your projects work in detail.
Round: Technical Interview
Experience: It was video conferencing. Interviewer was very informal that made me relaxed and comfortable, asked me various questions on data science, I tried to answer those with how I used those concept in my projects work. It ended on positive note.
Tips: Be truthful, don't assume anything.
College Name: IIT Guwahati
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Applied Materials Data Scientist Interview Process
based on 3 interviews
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