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Data and Analytics · #90 in series

Applied Scientist interview prep

Top 100 interview questions for Applied Scientist — modeled on real FAANG loops.

100

Questions

12

Topics

20

Free

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Machine Learning Data Science Programming Statistics Problem-Solving Deep Learning Natural Language Processing (NLP)Big Data Optimization A/B Testing Ethics in AI General

Machine Learning

10 questions

Explain the differences between supervised and unsupervised learning.

What is overfitting, and how can you prevent it?

How would you handle an imbalanced dataset?

Can you explain the bias-variance tradeoff?

What are ensemble methods? Give examples.

Describe how a decision tree works.

What is the purpose of cross-validation?

Explain the difference between bagging and boosting.

How does a support vector machine (SVM) work?

What is a confusion matrix, and how is it used?

Data Science

9 questions

How would you approach cleaning a large dataset?

Describe a project where you used data to solve a real-world problem.

What is feature engineering, and why is it important?

How do you determine which features are important in your model?

What is the purpose of a data pipeline?

How do you handle missing data in a dataset?

mediumbehavioral

Explain the importance of exploratory data analysis (EDA).

Describe a time when you had to communicate complex data findings to a non-technical audience.

What are the differences between SQL and NoSQL databases?

Programming

9 questions

Write a function to reverse a string in Python.

How would you implement a binary search algorithm?

Explain the time complexity of quicksort.

What are the differences between procedural and object-oriented programming?

How do you handle exceptions in Python?

Describe the use of decorators in Python.

What are the different ways to optimize a Python program for performance?

Explain the concept of recursion and provide an example.

How would you perform multithreading in a program?

Statistics

9 questions

What is the central limit theorem, and why is it important?

Explain the difference between a t-test and a z-test.

What is hypothesis testing, and how is it performed?

Define and differentiate between Type I and Type II errors.

How do you interpret a p-value?

Explain the concept of confidence intervals.

What is the difference between correlation and causation?

How do you perform linear regression analysis?

What is the purpose of ANOVA?

Problem-Solving

8 questions

Describe a challenging problem you solved using data analysis.

How do you prioritize tasks when handling multiple projects?

What strategies do you employ for debugging a problematic code?

Discuss a time when you had to make a decision with incomplete data.

How do you handle ambiguity in project requirements?

Describe a situation where you had to convince stakeholders to adopt your solution.

Explain how you approach breaking down a complex problem.

Discuss a project where you had a significant impact.

Deep Learning

9 questions

What is a neural network, and how does it work?

Explain the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN).

Describe how backpropagation works.

What are activation functions, and why are they used?

How do you prevent a neural network from overfitting?

Explain the concept of transfer learning.

What are generative adversarial networks (GANs)?

How do you choose the right architecture for a neural network?

What are the advantages and disadvantages of deep learning?

Natural Language Processing (NLP)

9 questions

How do you preprocess text data for NLP tasks?

What are word embeddings, and how are they used?

Explain the difference between TF-IDF and Word2Vec.

How do you handle out-of-vocabulary words in NLP?

What is sentiment analysis, and how is it implemented?

Describe how a transformer model works.

What are the common challenges in NLP?

How do you evaluate the performance of an NLP model?

Explain the concept of attention in NLP models.

Big Data

9 questions

What is Hadoop, and how does it work?

Explain the components of the Hadoop ecosystem.

How do you use Spark for data processing?

What is the difference between batch processing and stream processing?

Describe a situation where you used a big data tool to solve a problem.

How do you handle data storage and retrieval in distributed systems?

Explain the concept of a data lake.

How do you ensure data quality in a big data project?

What are the challenges associated with big data?

Optimization

8 questions

What is gradient descent, and how does it work?

Explain the difference between convex and non-convex optimization.

How do you choose an optimization algorithm for a given problem?

Describe how you would optimize a machine learning model.

What is the purpose of regularization in optimization?

Explain the concept of hyperparameter tuning.

How do you handle constraints in optimization problems?

Discuss a time when you had to improve the performance of a system.

A/B Testing

8 questions

What is A/B testing, and why is it used?

How do you design an A/B test?

What are the common pitfalls in A/B testing?

How do you interpret the results of an A/B test?

Explain the concept of statistical significance in A/B testing.

Describe a successful A/B test you conducted.

How do you handle multiple testing issues?

What metrics do you consider in an A/B test?

Ethics in AI

7 questions

What are the ethical considerations in AI and machine learning?

How do you ensure fairness in AI models?

What is data privacy, and why is it important?

Explain the concept of explainability in AI.

How do you address bias in machine learning models?

Discuss the impact of AI on society.

How do you handle ethical dilemmas in AI projects?

General

5 questions

Describe your experience working in a cross-functional team.

How do you stay updated with the latest developments in data science and machine learning?

What are your long-term career goals as an applied scientist?

Discuss a project where you had to learn a new tool or technology quickly.

How do you manage work-life balance in a fast-paced environment?

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