Amazon Web Services Data Scientist and Machine Learning Engineer Interview Questions and Answers

Q1. What is your favourite algorithm and how have you implemented it?
Ans. 
My favorite algorithm is Random Forest, which I have implemented for predicting customer churn in a telecom company.
• Random Forest is an ensemble learning method that builds multiple decision trees and merges them together to get a more accurate and stable prediction.

• I have implemented Random Forest in Python using scikit-learn library for a telecom company to predict customer churn based on various features like call d...read more
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Q1. Screening round: Tell me about your experiences?
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Q2. Tell me Learning and development challenges you faced and improvements you have done
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Genral and technical aptitude test

Q1. Overall experience?
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Q2. What was challenging role in previous org?
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Q3. How you see L&D from your perspective?
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Q4. How you tackle problems in your role?
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Q5. How will you onboard 500 candidates every month?
Ans. 
By creating a structured onboarding process, utilizing technology for efficiency, and leveraging a team of trainers.
• Develop a comprehensive onboarding program with clear objectives and timelines.

• Utilize technology such as online training modules and virtual onboarding sessions.

• Assign a team of trainers to handle different aspects of the onboarding process.

• Implement a buddy system where existing employees mentor new hir...read more
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Q1. Basic Stats questions
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Q2. Basic ML questions
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Q1. Difference between bias and variance
Ans. 
Bias is error due to overly simplistic assumptions, variance is error due to overly complex models.
• Bias is error introduced by approximating a real-world problem, leading to underfitting.

• Variance is error introduced by modeling the noise in the training data, leading to overfitting.

• High bias can cause a model to miss relevant relationships between features and target variable.

• High variance can cause a model to be overl...read more
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Q2. What’s is Learning rate
Ans. 
Learning rate is a hyperparameter that controls how much we are adjusting the weights of our network with respect to the loss gradient.
• Learning rate determines the size of the steps taken during optimization.

• A high learning rate can cause the model to converge too quickly and potentially miss the optimal solution.

• A low learning rate can cause the model to take a long time to converge or get stuck in a local minimum.

• Com...read more
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Q1. Basics of DL and NLP , along with current projects
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Q1. General scenario based on leadership and decision making
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PPT-based Instructional Interview questions

Q1. Why do you want to be an Instructional Designer?
Ans. 
I want to be an Instructional Designer because I am passionate about creating engaging and effective learning experiences.
• Passion for creating engaging learning experiences

• Enjoy designing and developing training materials

• Strong communication and collaboration skills

• Desire to help others learn and grow

• Interest in instructional design theories and methodologies
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Q1. Implementation of end to end to projects What are transformers
Ans. 
Transformers are models that process sequential data by learning contextual relationships between words.
• Transformers are a type of deep learning model commonly used in natural language processing tasks.

• They are based on the attention mechanism, allowing them to focus on different parts of the input sequence.

• Examples of transformer models include BERT, GPT, and TransformerXL.
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Q1. Focus on more situation based questions... Example - how you would handle a xyz situation if you would encounter it.
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Q1. Same kind if round with a different interviewer.
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Maths, language skills, typing skills and situation analysis.