Google Data Science Engineer Interview Questions and Answers

Q1. Asked Algorithms used in the project (No follow-up on mentioned algorithms, cut-off mid-explanation of business problem, scale, and solution wanting to know just the names of the algorithm) - Answered by n...read more
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Q2. Count all pairs of numbers from a list where the ending digit of the ith number equals the starting digit of the jth number. Example [122, 21, 21, 23] should have 5 pairs (122, 21), (122, 21), (122, 23), (...read more
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Count pairs of numbers where ending digit of ith number equals starting digit of jth number.
• Iterate through each pair of numbers in the list

• Check if the ending digit of the ith number equals the starting digit of the jth number

• Increment the count if the condition is met
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Q3. Interpretation of graphs, the first graph had perpendicular lines from the error to the fitted line and the second graph had lines from the error to the fitted line, parallel to the y-axis. - Interpreted t...read more
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Interpretation of graphs in linear regression analysis
• Perpendicular lines from error to fitted line in first graph indicate OLS using projection matrices

• Lines parallel to y-axis from error to fitted line in second graph suggest evaluation of linear regression to y-pred - y-actual method

• PCA could also be a possible interpretation for the second graph
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Q4. What does np.einsum() do
Ans. 
np.einsum() performs Einstein summation on arrays.
• Performs summation over specified indices

• Can also perform other operations like multiplication, contraction, etc.

• Syntax: np.einsum(subscripts, *operands)
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Q5. How to generate random numbers using numpy, what is the difference between numpy.random.rand and numpy.random.randn
Ans. 
numpy.random.rand generates random numbers from a uniform distribution, while numpy.random.randn generates random numbers from a standard normal distribution.
• numpy.random.rand generates random numbers from a uniform distribution between 0 and 1.

• numpy.random.randn generates random numbers from a standard normal distribution with mean 0 and standard deviation 1.

• Example: np.random.rand(3, 2) will generate a 3x2 array of r...read more
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Q6. Difference between logit and probabilities in deep learning
Ans. 
Logit is the log-odds of the probability, while probabilities are the actual probabilities of an event occurring.
• Logit is the natural logarithm of the odds ratio, used in logistic regression.

• Probabilities are the actual likelihood of an event occurring, ranging from 0 to 1.

• In deep learning, logit values are transformed into probabilities using a softmax function.

• Logit values can be negative or positive, while probabili
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Q1. Given a situation how do you handle different cases
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Q2. Given a proab stats question
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Q1. Asked about projects. What is classification? Is knn used for regression?how? decision tree working for regression and classification Is naive Bayes used for regression?how? LLM Docker Aws GenAI Code for ...read more
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Q1. Basic pandas questions on dataframes
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Q2. Some quiz questions
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SQL coding test on HackerRank. Also some questions on previous experience

Case study on a data project

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
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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
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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
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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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