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Top 30 ML Design Patterns Interview Questions
Entry Junior Mid Senior Expert
ML Design Patterns Theoretical Questions
Q1:
What Data Representation techniques do you know?
Junior
Q2:
When would you use the Hashed Feature Design Pattern?
Junior
Q3:
What are the benefits of using the Workflow Pipeline Design Pattern?
Junior
Q4:
Related To: Classification
What's the difference between Multiclass Classification models and Multi Label model?
Related To: Classification Junior
Q5:
Related To: Data Processing, Feature Engineering
What are some recommended choices for Imputation Values?
Related To: Data Processing, Feature Engineering Junior
Q6:
Name some approaches that you can take to implement the Ensemble Design Pattern
Junior
Q7:
Related To: Data Processing
Name some methods you know for Rebalancing a dataset using Rebalancing Design Pattern
Related To: Data Processing Junior
Q8:
Related To: Optimisation, Dimensionality Reduction
When would you use Grid Search vs Random Search for Hyperparameter Tuning?
Related To: Optimisation, Dimensionality Reduction Junior
Q9:
Related To: Model Evaluation, Optimisation, Dimensionality Reduction
What methods of Hyperparameters Tuning do you know?
Related To: Model Evaluation, Optimisation, Dimensionality Reduction Junior
Q10:
What's the difference between the Transform and Feature Store Design Patterns in Machine Learning?
Mid Q11:
What ML Design Patterns can you use to ensure Reproducibility of Machine Learning jobs?
Mid Q12:
When would you use Windowed Inference Design Pattern?
Mid Q13:
What does the Bridged Schema Design Pattern do?
Mid Q14:
Related To: Data Processing
Are there any problems with splitting data randomly into Training, Validation, and Test datasets?
Related To: Data Processing Mid Q15:
What ML problems are solved by the Transformation Design Pattern?
Mid Q16:
When and why would you use Checkpoints for a ML pipeline?
Mid Q17:
For what problems would you use the Neutral Class Design Pattern in Machine Learning?
Mid Q18:
What's the main idea of Reframing Design Pattern for a ML problem?
Mid Q19:
Related To: Curse of Dimensionality
What are some trade-offs when using Embeddings in Machine Learning?
Related To: Curse of Dimensionality Mid Q20:
How does Feature Cross Design Pattern work in Machine Learning?
Mid Q21:
When would you need to implement Transfer Learning Design Pattern?
Senior Q22:
Related To: Model Evaluation
Why would you need to use the Continued Model Evaluation Design Pattern?
Related To: Model Evaluation Senior Q23:
What are the two-phases in the Two-Phase Predictions Design Pattern?
Senior Q24:
When would you need to use the Two-Phases predictions Design Pattern?
Senior Senior Senior Q27:
Explain how would you build a Multi-Label model using Multi-Label Design Pattern?
Senior Q28:
Are there any troubles when using Feature Cross? How can you solve it?
Senior Q29:
Related To: Data Processing, Feature Engineering
When would you use Fine-Tuning vs Feature Extraction in Transfer Learning?
Related To: Data Processing, Feature Engineering Expert Q30:
Related To: Model Evaluation, Data Processing, Neural Networks
Are there any troubles when using Early Stopping?
Related To: Model Evaluation, Data Processing, Neural Networks Expert 
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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...
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 ...
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....
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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