ResMed Data Scientist Interview Questions and Answers

Q1. About projects and then questions related to ML and DL. Mostly focused on DL part
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Q2. What is the difference between Adam optimizer and Gradient Descent Optimizer?
Ans. 
Adam optimizer is an extension to the Gradient Descent optimizer with adaptive learning rates and momentum.
• Adam optimizer combines the benefits of both AdaGrad and RMSProp optimizers.

• Adam optimizer uses adaptive learning rates for each parameter.

• Gradient Descent optimizer has a fixed learning rate for all parameters.

• Adam optimizer includes momentum to speed up convergence.

• Gradient Descent optimizer updates parameters b...read more
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Q3. When to use Relu and when not?
Ans. 
Use ReLU for hidden layers in deep neural networks, avoid for output layers.
• ReLU is commonly used in hidden layers to introduce non-linearity and speed up convergence.

• Avoid using ReLU in output layers for regression tasks as it can lead to vanishing gradients.

• Consider using Leaky ReLU or Sigmoid for output layers depending on the task.

• ReLU is computationally efficient and helps in preventing the vanishing gradient prob...read more
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Q1. How can Logistic regression be applied for multiclasstext classification
Ans. 
Logistic regression can be applied for multiclasstext classification by using one-vs-rest or softmax approach.
• One-vs-rest approach: Train a binary logistic regression model for each class, treating it as the positive class and the rest as the negative class.

• Softmax approach: Use the softmax function to transform the output of the logistic regression into probabilities for each class.

• Evaluate the model using appropriate...read more
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Q1. Basic DS question like how to handle missing features
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Q1. Case study based questions
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Q1. What are the various projects that have you implemented in your current org ?
Ans. 
I have implemented several projects in my current organization.
• Developed a predictive model to forecast customer churn

• Built a recommendation system to personalize product recommendations

• Created a fraud detection model to identify fraudulent transactions

• Implemented a natural language processing model for sentiment analysis

• Designed an anomaly detection system to detect network intrusions
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Q2. How is y9ur project related to business problem and how you have solved it
Ans. 
Developed a predictive model to identify potential customer churn for a telecom company
• Identified key factors contributing to customer churn through exploratory data analysis

• Built a logistic regression model to predict customer churn with 85% accuracy

• Provided actionable insights to the business team to reduce customer churn and improve customer retention

• Implemented the model in production environment using Python and S
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Q3. Why you are looking out for a change
Ans. 
Seeking new challenges and growth opportunities in the field of data science.
• Looking for a more challenging role to further develop my skills and knowledge in data science.

• Interested in exploring new industries and applying data science techniques to solve different problems.

• Seeking a company with a strong data-driven culture and a focus on innovation.

• Want to work with a diverse team of data scientists and learn from t...read more
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Q1. Define your work
Ans. 
As a Data Scientist, I analyze and interpret complex data to help businesses make informed decisions.
• I collect and clean data from various sources.

• I use statistical techniques and machine learning algorithms to analyze data.

• I develop predictive models and algorithms to solve business problems.

• I communicate findings and insights to stakeholders through visualizations and reports.
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Q2. What motivates you to join our company
Ans. 
I am motivated to join your company because of the challenging and innovative work environment.
• I am excited about the opportunity to work with cutting-edge technologies and tools in data science.

• Your company's reputation for being at the forefront of data-driven decision making is inspiring.

• I am impressed by the collaborative and diverse team culture that fosters continuous learning and growth.

• The company's commitment ...read more
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Q3. Why looking out for a change
Ans. 
Seeking new challenges and growth opportunities in the field of data science.
• Looking for a more challenging role to apply and expand my skills

• Interested in working with cutting-edge technologies and techniques

• Seeking a company with a strong data-driven culture

• Want to work on diverse projects and industries to broaden my experience

• Desire to make a bigger impact and contribute to solving complex problems
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It was related to Aptitude MCQ and 2-coding test

Q1. Palindrome of a number
Ans. 
A palindrome of a number is a number that remains the same when its digits are reversed.
• To check if a number is a palindrome, reverse the number and compare it with the original number.

• Examples: 121 is a palindrome, 123 is not a palindrome.
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Q2. Merge 2linked list
Ans. 
Merging two linked lists involves combining the elements of both lists into a single list.
• Create a new linked list to store the merged elements

• Traverse through both linked lists and add elements to the new list

• Handle cases where one list is longer than the other
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Q3. About projects you have done
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Q1. Explain your resume fully
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Q2. General questions on the behaviour
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Q1. How do you preprocess large/small dataset
Ans. 
Preprocessing large/small datasets involves cleaning, transforming, and organizing data to prepare it for analysis.
• Remove duplicates and missing values

• Normalize or standardize numerical features

• Encode categorical variables

• Feature scaling

• Handling outliers

• Dimensionality reduction techniques like PCA

• Splitting data into training and testing sets
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Q2. Data augmentation
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SQL coding test on HackerRank. Also some questions on previous experience

Case study on a data project

Basic self evaluation test.

Q1. What project I have completed and follow-up questions on that?
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Q2. How to handle class imbalance.
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Q3. Basic Python coding questions.
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Q1. Data-related questions.
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Q2. ML Ops questions.
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