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2103 Curated Machine Learning, Data Science, AI & LLMs Interview Questions
Answered To Get Your Next Six-Figure Job Offer
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Top 35 Model Evaluation Interview Questions

Entry Junior Mid Senior Expert
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Learning Progress:
Model Evaluation Theoretical Questions
Q1:   

What is Underfitting in Machine Learning?

  Related To: Machine Learning
Add to PDF   Junior 
Q2:   

What is a model Learning Rate? Is a high learning rate always good?

  Related To: Machine Learning
Add to PDF   Junior 
Q3:   

How to know whether your model is suffering from the problem of Exploding Gradients?

  Related To: Deep Learning, Neural Networks
Add to PDF   Junior 
Q4:   

What methods of Hyperparameters Tuning do you know?

  Related To: ML Design Patterns, Optimisation, Dimensionality Reduction
Add to PDF   Junior 
Q5:   

How can you evaluate the performance of Language Models?

  Related To: ChatGPT, NLP, LLMs
Add to PDF   Junior 
Q6:   

How would you detect Overfitting in Linear Models?

  Related To: Linear Regression
Add to PDF   Junior 
Q7:   

What is Hyper-Parameters in ML Model?

  Related To: Machine Learning
Add to PDF   Junior 
Q8:   

What is Overfitting in Machine Learning?

  Related To: Machine Learning
Add to PDF   Junior 
Q9:   

How would you fix Logistic Regression Overfitting problem?

  Related To: Cost Function, Linear Regression
 Add to PDF   Mid 
Q10:   

How do you reduce the risk of making a Type I and Type II error?

  Related To: Statistics
 Add to PDF   Mid 
Q11:   

What is the F-Score?

  Related To: Classification, Statistics
 Add to PDF   Mid 
Q12:   

What are some advantages and disadvantages of using AUC to measure the performance of the model?

  Related To: Classification
 Add to PDF   Mid 
Q13:   

How does ROC curve and AUC value help measure how good a model is?

  Related To: Classification
 Add to PDF   Mid 
Q14:   

What performance parameters can be calculated using Confusion Matrix?

  
 Add to PDF   Mid 
Q15:   

What is a Confusion Matrix?

  Related To: Classification, Supervised Learning
 Add to PDF   Mid 
Q16:   

How would you prevent Overfitting when designing an Artificial Neural Network?

  Related To: Neural Networks
 Add to PDF   Mid 
Q17:   

What are the difference between Type I and Type II errors?

  Related To: Data Processing, Machine Learning
 Add to PDF   Mid 
Q18:   

What are some approaches that can be used for monitoring LLMs?

  Related To: LLMOps, LLMs
 Add to PDF   Mid 
Q19:   

Why would you need to use the Continued Model Evaluation Design Pattern?

  Related To: ML Design Patterns
 Add to PDF   Senior 
Q20:   

When Overfitting can be useful?

  Related To: ML Design Patterns
 Add to PDF   Senior 
Q21:   

What are some approaches to get a quantitative estimate of a model's Maximum Predictive Power given a certain level of noise?

  Related To: Data Processing
 Add to PDF   Senior 
Q22:   

How can you evaluate LLMs using other LLMs?

  Related To: LLMOps, LLMs
 Add to PDF   Senior 
Q23:   

What are Concordance and Discordance?

  
 Add to PDF   Senior 
Q24:   

What is MDL?

  
 Add to PDF   Senior 
Q25:   

Compare AIC and BIC methods for model selection

  
 Add to PDF   Senior 
Q26:   

What is BIC?

  
 Add to PDF   Senior 
Q27:   

What is AIC?

  Related To: Classification
 Add to PDF   Senior 
Q28:   

If one algorithm has Higher Precision but Lower Recall than other, how can you tell which algorithm is better?

  
 Add to PDF   Senior 
Q29:   

How would you choose an evaluation metric for an Imbalanced classification?

  Related To: Classification
 Add to PDF   Senior 
Q30:   

How would you use a Confusion Matrix for determining a model performance?

  Related To: Classification, Data Processing
 Add to PDF   Senior 
Q31:   

How is AUC - ROC curve used in classification problems?

  Related To: Classification
 Add to PDF   Senior 
Q32:   

How would you deal with Overfitting in Linear Regression models?

  Related To: Linear Regression
 Add to PDF   Senior 
Q33:   

How to interpret F-measure values?

  Related To: Classification
 Add to PDF   Expert 
Q34:   

Are there any troubles when using Early Stopping?

  Related To: ML Design Patterns, Data Processing, Neural Networks
 Add to PDF   Expert 
Q35:   

What's the difference between ROC and Precision-Recall Curves?

  Related To: Classification
 Add to PDF   Expert 
 
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23 Azure Machine Learning Interview Questions (ANSWERED)
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23 Apache Spark Interview Questions (ANSWERED) To Learn Before ML & Big Data Interview
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Scala is a powerful language with functional programming capabilities that can be a good choice for data science, especially in big data and distributed computing scenarios. As an example, Apache Spark, a popular distributed data processing framework...
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PyTorch popularity as a Deep Learning framework of choice is on the rise. As of December 2022, 62% of the academic papers were implemented in PyTorch whereas only 4% were for TensorFlow. Follow along and prepare effectively with these key 30 PyTorch ...
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23 Common Artificial Intelligence Interview Questions (SOLVED) For Your Next ML & DS Interview
The use of Artificial Intelligence (AI) in machine learning and data science enabled advancements in areas such as natural language processing, computer vision, recommendation systems, fraud detection, predictive analytics, and personalized medicine....
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21 Data Science Optimisation Algorithms Interview Questions (ANSWERED)
Optimization algorithms are extensively used in training machine learning models. Data engineers employ algorithms like gradient descent, stochastic gradient descent, and variants (e.g., Adam, RMSprop) to optimize the model parameters and minimize th...
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