Master your AI/ML engineer interviews with this comprehensive guide featuring 100 essential questions.
Each question includes: explanation, memory trick, and how to answer for maximum retention.
Perfect for both beginners and experienced professionals preparing for their next role.
PART A — HR QUESTIONS (30)
Q1. Tell me about yourself.
Explanation:
What you do + confidence + clarity. First impression matters most.
Memory Trick:
"P-P-T" → Past, Present, Target.
How to Answer:- Past: Your degree/skills background
- Present: What you're currently doing
- Target: Why this role fits your goals
Q2. Why do you want to work in AI/ML?
Explanation:
Shows passion and genuine interest in the field.
Memory Trick:
"S.O.L.V.E" → Solve real-world problems.
How to Answer:- Mention specific problems AI solves
- Share a personal story or project
- Connect to future impact you want to create
Q3. What are your strengths?
Explanation:
Highlight skills relevant to AI/ML roles with examples.
Memory Trick:
"S.T.A.R" → Situation, Task, Action, Result.
How to Answer:- Pick 2-3 relevant strengths
- Provide specific examples
- Connect to job requirements
Q4. What are your weaknesses?
Explanation:
Show self-awareness and commitment to improvement.
Memory Trick:
"W.I.P" → Weakness + Improvement Plan.
How to Answer:- Choose a real but manageable weakness
- Explain steps you're taking to improve
- Show progress made so far
Q5. Where do you see yourself in 5 years?
Explanation:
Demonstrates ambition and alignment with company growth.
Memory Trick:
"G.R.O.W" → Goals, Responsibility, Opportunities, Worth.
How to Answer:- Mention technical growth goals
- Include leadership aspirations
- Connect to company's mission
Q6. Why are you leaving your current job?
Explanation:
Focus on positive growth rather than negative experiences.
Memory Trick:
"P.U.L.L" → Positive reasons that Pull you forward.
How to Answer:- Focus on growth opportunities
- Mention learning new technologies
- Avoid negative comments about current employer
Q7. What motivates you?
Explanation:
Reveals what drives your performance and engagement.
Memory Trick:
"L.I.P.S" → Learning, Impact, Problem-solving, Success.
How to Answer:- Mention continuous learning
- Discuss solving complex problems
- Include making positive impact
Q8. How do you handle stress and pressure?
Explanation:
AI/ML projects often have tight deadlines and complex challenges.
Memory Trick:
"C.A.L.M" → Categorize, Analyze, List, Manage.
How to Answer:- Break problems into smaller tasks
- Use time management techniques
- Maintain work-life balance
Q9. Describe a challenging project you worked on.
Explanation:
Shows problem-solving skills and perseverance.
Memory Trick:
"C.A.R.E" → Challenge, Action, Result, Evaluation.
How to Answer:- Describe the specific challenge
- Explain your approach and actions
- Share measurable results
- Include lessons learned
Q10. How do you stay updated with AI/ML trends?
Explanation:
Shows commitment to continuous learning in a rapidly evolving field.
Memory Trick:
"R.E.A.D" → Research, Experiment, Attend, Discuss.
How to Answer:- Mention specific sources (papers, blogs, conferences)
- Include hands-on experimentation
- Discuss community involvement
Q11. What is your greatest professional achievement?
Explanation:
Highlights your ability to deliver significant results.
Memory Trick:
"I.M.P.A.C.T" → Impact, Metrics, Process, Achievement, Collaboration, Time.
How to Answer:- Choose an AI/ML related achievement
- Include quantifiable metrics
- Explain your specific contribution
Q12. How do you work in a team?
Explanation:
AI/ML projects require collaboration between different roles.
Memory Trick:
"T.E.A.M" → Together Everyone Achieves More.
How to Answer:- Emphasize communication skills
- Give examples of successful collaborations
- Mention conflict resolution abilities
Q13. What are your salary expectations?
Explanation:
Tests your market knowledge and negotiation skills.
Memory Trick:
"F.L.E.X" → Flexible, Learn more, Explore, eXpectations.
How to Answer:- Research market rates beforehand
- Provide a reasonable range
- Show flexibility for the right opportunity
Q14. Do you have any questions for us?
Explanation:
Shows genuine interest and helps you evaluate the company.
Memory Trick:
"G.R.O.W.T.H" → Growth, Role, Opportunities, Working style, Team, Hurdles.
How to Answer:- Ask about team structure and collaboration
- Inquire about growth opportunities
- Discuss current AI/ML projects
Q15. Why should we hire you?
Explanation:
Your chance to summarize your unique value proposition.
Memory Trick:
"U.N.I.Q.U.E" → Unique skills, Notable experience, Impact driven, Quality work, Understanding, Enthusiasm.
How to Answer:- Combine technical skills with soft skills
- Mention relevant experience
- Show enthusiasm for the role
Q16. How do you handle failure?
Explanation:
AI/ML experiments often fail; resilience is crucial.
Memory Trick:
"L.E.A.R.N" → Learn, Evaluate, Adapt, Retry, Next.
How to Answer:- View failure as learning opportunity
- Analyze root causes
- Apply lessons to future projects
Q17. What drives your passion for data science?
Explanation:
Shows genuine interest beyond just technical skills.
Memory Trick:
"D.A.T.A" → Discover, Analyze, Transform, Act.
How to Answer:- Mention curiosity about patterns
- Discuss impact of data-driven decisions
- Share a specific inspiring example
Q18. How do you prioritize multiple projects?
Explanation:
Tests time management and decision-making skills.
Memory Trick:
"U.R.G.E.N.T" → Urgent vs important matrix thinking.
How to Answer:- Use impact vs effort matrix
- Consider deadlines and dependencies
- Communicate with stakeholders regularly
Q19. Describe your ideal work environment.
Explanation:
Helps determine cultural fit and work preferences.
Memory Trick:
"C.O.D.E" → Collaborative, Open communication, Data-driven, Experimental.
How to Answer:- Emphasize collaborative environment
- Mention learning opportunities
- Include data-driven culture
Q20. How do you explain complex AI concepts to non-technical stakeholders?
Explanation:
Critical skill for AI engineers working with business teams.
Memory Trick:
"S.I.M.P.L.E" → Stories, Images, Metaphors, Plain language, Logic, Examples.
How to Answer:- Use analogies and metaphors
- Focus on business impact
- Provide visual examples
Q21. What was your biggest learning experience?
Explanation:
Shows growth mindset and ability to learn from experiences.
Memory Trick:
"G.R.O.W" → Goal, Reality, Options, Will/Way forward.
How to Answer:- Choose a significant learning moment
- Explain what you learned
- Show how it changed your approach
Q22. How do you ensure code quality in ML projects?
Explanation:
Tests understanding of best practices and quality standards.
Memory Trick:
"T.E.S.T" → Testing, Error handling, Standards, Tracking.
How to Answer:- Mention unit testing and validation
- Discuss code reviews and documentation
- Include version control practices
Q23. What ethical considerations matter in AI?
Explanation:
Shows awareness of AI's societal impact and responsibility.
Memory Trick:
"F.A.I.R" → Fairness, Accountability, Interpretability, Responsibility.
How to Answer:- Discuss bias and fairness
- Mention privacy protection
- Include transparency and explainability
Q24. How do you approach learning new technologies?
Explanation:
AI/ML field evolves rapidly; continuous learning is essential.
Memory Trick:
"L.E.A.R.N" → Look around, Experiment, Apply, Reflect, Network.
How to Answer:- Start with fundamentals
- Build hands-on projects
- Join communities and discussions
Q25. What role does creativity play in AI/ML?
Explanation:
Tests understanding that AI/ML isn't just following procedures.
Memory Trick:
"C.R.E.A.T.E" → Creative problem-solving, Reframe challenges, Experiment, Adapt, Think differently, Evolve.
How to Answer:- Creativity in feature engineering
- Novel approaches to problems
- Innovative model architectures
Q26. How do you handle disagreements with team members?
Explanation:
Tests conflict resolution and collaboration skills.
Memory Trick:
"L.I.S.T.E.N" → Listen actively, Identify common ground, Share perspectives, Think solutions, Engage constructively, Navigate forward.
How to Answer:- Listen to understand their perspective
- Focus on data and facts
- Find common ground and solutions
Q27. What interests you about our company?
Explanation:
Shows research and genuine interest in the specific role.
Memory Trick:
"M.A.T.C.H" → Mission alignment, Achievements, Technology, Culture, Hopes.
How to Answer:- Research company's AI initiatives
- Mention specific projects or values
- Connect to your career goals
Q28. How do you balance innovation with practical constraints?
Explanation:
Tests understanding of business realities in AI implementation.
Memory Trick:
"P.R.A.G.M.A" → Practical, Realistic, Achievable, Goals, Measurable, Adaptable.
How to Answer:- Consider time and budget constraints
- Start with MVP approach
- Iterate based on feedback and results
Q29. Describe your communication style.
Explanation:
Important for cross-functional collaboration in AI projects.
Memory Trick:
"C.L.E.A.R" → Concise, Listen actively, Empathetic, Adaptable, Respectful.
How to Answer:- Emphasize clear and concise communication
- Adapt style to different audiences
- Include active listening skills
Q30. What questions do you have about the role or team?
Explanation:
Final chance to show interest and gather important information.
Memory Trick:
"T.E.A.M.S" → Technology stack, Expectations, Advancement, Mentorship, Success metrics.
How to Answer:- Ask about day-to-day responsibilities
- Inquire about team dynamics
- Discuss success metrics and expectations
PART B — TECHNICAL AI/ML QUESTIONS (70)
Q31. What is Machine Learning?
Explanation:
Computer systems learning patterns from data without explicit programming.
Memory Trick:
"D.A.T.A → P.A.T.T.E.R.N.S" → Data helps find patterns.
How to Answer:- Define as algorithms learning from data
- Mention three types: supervised, unsupervised, reinforcement
- Give simple example like email spam detection
Q32. What is Overfitting?
Explanation:
Model memorizes training data instead of learning general patterns.
Memory Trick:
Think "student memorizing answers" instead of understanding concepts.
How to Answer:- Model performs well on training data but poorly on new data
- Prevention: more data, regularization, dropout
- Detection: validation set performance drops
Q33. What is Underfitting?
Explanation:
Model is too simple to capture underlying data patterns.
Memory Trick:
Think "student not studying enough" → poor performance everywhere.
How to Answer:- High error on both training and test data
- Model is too simple for the problem
- Solutions: more complex model, more features
Q34. Explain Bias-Variance Tradeoff.
Explanation:
Balance between model simplicity (bias) and complexity (variance).
Memory Trick:
"Bias = Bullseye miss, Variance = Scattered shots"
How to Answer:- High bias: too simple, underfits
- High variance: too complex, overfits
- Goal: balance both for optimal performance
Q35. What is Cross-Validation?
Explanation:
Technique to evaluate model performance using multiple train/test splits.
Memory Trick:
"K-Fold = K different tests" like taking multiple exams.
How to Answer:- Split data into K folds
- Train on K-1 folds, test on 1 fold
- Repeat K times, average results
Q36. What is Feature Engineering?
Explanation:
Process of creating, selecting, and transforming input variables.
Memory Trick:
"C.R.E.A.T.E" → Create, Remove, Extract, Aggregate, Transform, Encode.
How to Answer:- Creating new features from existing data
- Examples: scaling, encoding, polynomial features
- Goal: improve model performance
Q37. What is Gradient Descent?
Explanation:
Optimization algorithm to minimize cost function by iterative parameter updates.
Memory Trick:
"Rolling ball downhill" → finds lowest point (minimum cost).
How to Answer:- Algorithm to minimize cost function
- Updates parameters in direction of steepest descent
- Learning rate controls step size
Q38. What is Linear Regression?
Explanation:
Algorithm that finds best-fitting line through data points.
Memory Trick:
"Y = mx + b" → just like school math line equation.
How to Answer:- Predicts continuous target variable
- Assumes linear relationship between features and target
- Example: predicting house price from size
Q39. What is Logistic Regression?
Explanation:
Classification algorithm using sigmoid function for probability prediction.
Memory Trick:
"S-curve" → sigmoid squashes output between 0 and 1.
How to Answer:- Used for binary classification
- Uses sigmoid function for probability
- Example: predicting email spam/not spam
Q40. What is Decision Tree?
Explanation:
Tree-like model making decisions through series of yes/no questions.
Memory Trick:
"20 Questions game" → keep asking until you get the answer.
How to Answer:- Makes decisions through series of if/else questions
- Easy to interpret and visualize
- Can be used for both classification and regression
Q41. What is Random Forest?
Explanation:
Ensemble method combining multiple decision trees for better predictions.
Memory Trick:
"Ask multiple experts" → combine their opinions for better decision.
How to Answer:- Combines multiple decision trees
- Uses voting or averaging for final prediction
- Reduces overfitting compared to single tree
Q42. What is K-Means Clustering?
Explanation:
Unsupervised algorithm grouping data into K clusters.
Memory Trick:
"K friends finding their groups" at a party.
How to Answer:- Groups data into K clusters
- Minimizes distance from points to cluster centers
- Example: customer segmentation
Q43. What is SVM (Support Vector Machine)?
Explanation:
Algorithm finding optimal boundary (hyperplane) to separate classes.
Memory Trick:
"Drawing the best line" to separate two groups with maximum margin.
How to Answer:- Finds optimal separating hyperplane
- Maximizes margin between classes
- Uses kernel trick for non-linear data
Q44. What is Neural Network?
Explanation:
Computational model inspired by brain neurons, with interconnected layers.
Memory Trick:
"Brain neurons talking" → layers of nodes passing information.
How to Answer:- Network of interconnected nodes (neurons)
- Information flows through layers (input → hidden → output)
- Learns by adjusting connection weights
Q45. What is Deep Learning?
Explanation:
Neural networks with many hidden layers for complex pattern recognition.
Memory Trick:
"Deep = Many layers" like a tall building with many floors.
How to Answer:- Neural networks with multiple hidden layers (usually >3)
- Can learn complex patterns automatically
- Examples: image recognition, NLP
Q46. What is CNN (Convolutional Neural Network)?
Explanation:
Deep learning architecture designed for processing grid-like data (images).
Memory Trick:
"Scanning images with filters" like using a magnifying glass.
How to Answer:- Uses convolution filters to detect features
- Includes pooling layers to reduce dimensions
- Excellent for image classification
Q47. What is RNN (Recurrent Neural Network)?
Explanation:
Neural network that can process sequences by maintaining memory.
Memory Trick:
"Memory loop" → remembers previous inputs while processing current one.
How to Answer:- Has loops allowing information to persist
- Good for sequential data (text, time series)
- Can suffer from vanishing gradient problem
Q48. What is LSTM (Long Short-Term Memory)?
Explanation:
Special type of RNN designed to remember long-term dependencies.
Memory Trick:
"Smart memory" → knows what to remember and what to forget.
How to Answer:- Solves vanishing gradient problem in RNNs
- Has gates (forget, input, output) to control information flow
- Better for long sequences
Q49. What is Transformer?
Explanation:
Architecture using attention mechanism for parallel sequence processing.
Memory Trick:
"Attention is all you need" → famous paper title.
How to Answer:- Uses self-attention mechanism
- Processes sequences in parallel (faster than RNN)
- Foundation for GPT, BERT models
Q50. What is Attention Mechanism?
Explanation:
Allows model to focus on relevant parts of input sequence.
Memory Trick:
"Highlighting important words" when reading a text.
How to Answer:- Assigns weights to different parts of input
- Helps model focus on relevant information
- Improves long-range dependencies
Q51. What is BERT?
Explanation:
Bidirectional Encoder Representations from Transformers for language understanding.
Memory Trick:
"Reads both ways" → bidirectional context understanding.
How to Answer:- Pre-trained bidirectional transformer
- Understands context from both directions
- Can be fine-tuned for specific tasks
Q52. What is GPT?
Explanation:
Generative Pre-trained Transformer for text generation.
Memory Trick:
"Predicts next word" based on previous context.
How to Answer:- Autoregressive language model
- Generates text by predicting next token
- Trained on massive text corpus
Q53. What is Word Embedding?
Explanation:
Dense vector representations of words capturing semantic relationships.
Memory Trick:
"Words as numbers" → similar words have similar numbers.
How to Answer:- Converts words to dense vectors
- Similar words have similar vectors
- Examples: Word2Vec, GloVe
Q54. What is One-Hot Encoding?
Explanation:
Converting categorical variables into binary vectors.
Memory Trick:
"Only one light on" → only one position is 1, rest are 0s.
How to Answer:- Creates binary vector for each category
- Only one position is 1, rest are 0s
- Example: Red=[1,0,0], Blue=[0,1,0], Green=[0,0,1]
Q55. What is Normalization?
Explanation:
Scaling features to similar ranges for better model performance.
Memory Trick:
"Making everyone same height" → scale different ranges to 0-1.
How to Answer:- Scales features to similar ranges (usually 0-1)
- Prevents features with large values dominating
- Formula: (x - min) / (max - min)
Q56. What is Standardization?
Explanation:
Transforming features to have zero mean and unit variance.
Memory Trick:
"Z-score transformation" → centered around 0 with spread of 1.
How to Answer:- Transforms to mean=0, std=1
- Formula: (x - mean) / std
- Better when data follows normal distribution
Q57. What is Principal Component Analysis (PCA)?
Explanation:
Dimensionality reduction technique finding principal directions of variance.
Memory Trick:
"Finding best camera angle" → capture most information in fewer dimensions.
How to Answer:- Reduces dimensionality while preserving variance
- Finds principal components (directions of max variance)
- Useful for visualization and noise reduction
Q58. What is Regularization?
Explanation:
Techniques to prevent overfitting by adding penalty to loss function.
Memory Trick:
"Speed limit for model" → prevents going too complex.
How to Answer:- Adds penalty term to loss function
- Two main types: L1 (Lasso), L2 (Ridge)
- Prevents overfitting by constraining weights
Q59. What is Dropout?
Explanation:
Regularization technique randomly setting some neurons to zero during training.
Memory Trick:
"Randomly skipping class" → forces network to not rely on specific neurons.
How to Answer:- Randomly sets neurons to zero during training
- Prevents co-adaptation of neurons
- Common rate: 0.2-0.5 (20-50% dropout)
Q60. What is Batch Normalization?
Explanation:
Normalizing inputs to each layer for faster and stable training.
Memory Trick:
"Standardizing each layer's input" → keeps values in good range.
How to Answer:- Normalizes inputs to each layer
- Reduces internal covariate shift
- Allows higher learning rates
Q61. What is Learning Rate?
Explanation:
Hyperparameter controlling how much to update model weights during training.
Memory Trick:
"Step size while walking" → too big jumps, too small crawls.
How to Answer:- Controls weight update step size
- Too high: overshooting, too low: slow convergence
- Common values: 0.001, 0.01, 0.1
Q62. What is Activation Function?
Explanation:
Functions that introduce non-linearity to neural networks.
Memory Trick:
"On/off switch" → decides whether neuron should fire.
How to Answer:- Introduces non-linearity to network
- Common types: ReLU, Sigmoid, Tanh
- Without it, network would be just linear regression
Q63. What is ReLU?
Explanation:
Rectified Linear Unit activation function: f(x) = max(0, x).
Memory Trick:
"Cut negative, keep positive" → like trimming branches below ground.
How to Answer:- Output is 0 for negative inputs, x for positive
- Solves vanishing gradient problem
- Computationally efficient
Q64. What is Sigmoid Function?
Explanation:
S-shaped activation function mapping inputs to range (0, 1).
Memory Trick:
"S-curve" → smooth transition from 0 to 1.
How to Answer:- Maps inputs to range (0, 1)
- Good for binary classification output layer
- Can suffer from vanishing gradients
Q65. What is Softmax Function?
Explanation:
Converts vector of real numbers into probability distribution.
Memory Trick:
"Soft maximum" → all outputs sum to 1 (probabilities).
How to Answer:- Converts scores to probabilities (sum = 1)
- Used in multi-class classification output layer
- Emphasizes largest values while preserving relative order
Q66. What is Loss Function?
Explanation:
Function measuring difference between predicted and actual values.
Memory Trick:
"Penalty for wrong answers" → higher loss = worse performance.
How to Answer:- Measures prediction errors
- Common types: MSE, Cross-entropy, Hinge loss
- Model learns by minimizing loss
Q67. What is Mean Squared Error (MSE)?
Explanation:
Loss function calculating average of squared differences.
Memory Trick:
"Square the mistakes" → penalizes large errors more.
How to Answer:- Formula: mean((predicted - actual)²)
- Used for regression problems
- Penalizes large errors more heavily
Q68. What is Cross-Entropy Loss?
Explanation:
Loss function for classification measuring probability distribution difference.
Memory Trick:
"How surprised you are by wrong predictions.
How to Answer:- Used for classification problems
- Measures difference between predicted and true probability distributions
- Works well with softmax activation
Q69. What is Precision?
Explanation:
Of all positive predictions, how many were actually correct.
Memory Trick:
"When I say Yes, am I right?" → True Positives / All Positives predicted.
How to Answer:- Formula: TP / (TP + FP)
- High precision = few false positives
- Important when false positives are costly
Q70. What is Recall?
Explanation:
Of all actual positive cases, how many did we correctly identify.
Memory Trick:
"Can I catch all the fish?" → True Positives / All actual positives.
How to Answer:- Formula: TP / (TP + FN)
- High recall = few false negatives
- Important when missing positives is costly (e.g., disease detection)
Q71. What is F1-Score?
Explanation:
Harmonic mean of precision and recall, balancing both metrics.
Memory Trick:
"Best of both worlds" → balances precision and recall.
How to Answer:- Formula: 2 × (Precision × Recall) / (Precision + Recall)
- Single metric combining both precision and recall
- Useful when you need balanced performance
Q72. What is Confusion Matrix?
Explanation:
Table showing correct and incorrect predictions for each class.
Memory Trick:
"Truth vs Prediction grid" → shows where model gets confused.
How to Answer:- 2x2 table for binary classification: TP, TN, FP, FN
- Diagonal shows correct predictions
- Off-diagonal shows errors
Q73. What is ROC Curve?
Explanation:
Receiver Operating Characteristic curve plotting True Positive Rate vs False Positive Rate.
Memory Trick:
"Good classifier hugs top-left corner" → high TPR, low FPR.
How to Answer:- Plots TPR vs FPR at different thresholds
- AUC (Area Under Curve) measures overall performance
- AUC = 0.5 means random guessing
Q74. What is Transfer Learning?
Explanation:
Using pre-trained model as starting point for new related task.
Memory Trick:
"Standing on giant's shoulders" → use existing knowledge for new task.
How to Answer:- Start with pre-trained model
- Fine-tune for specific task
- Saves time and improves performance with less data
Q75. What is Data Augmentation?
Explanation:
Artificially increasing dataset size by creating modified versions of existing data.
Memory Trick:
"Making more training examples" → rotate, flip, crop images.
How to Answer:- Creates variations of existing data
- Examples: rotation, flipping, cropping for images
- Helps prevent overfitting and improves generalization
Q76. What is Ensemble Learning?
Explanation:
Combining multiple models to make better predictions than any single model.
Memory Trick:
"Wisdom of crowds" → many models together perform better.
How to Answer:- Combines multiple models for final prediction
- Methods: voting, averaging, stacking
- Examples: Random Forest, Gradient Boosting
Q77. What is Gradient Boosting?
Explanation:
Ensemble method building models sequentially, each correcting previous errors.
Memory Trick:
"Learning from mistakes" → each new model fixes previous errors.
How to Answer:- Builds models sequentially
- Each new model focuses on previous errors
- Examples: XGBoost, LightGBM
Q78. What is Hyperparameter Tuning?
Explanation:
Process of finding optimal model configuration settings.
Memory Trick:
"Tuning radio" → adjusting dials for best signal (performance).
How to Answer:- Optimizes model configuration parameters
- Methods: Grid search, Random search, Bayesian optimization
- Examples: learning rate, number of trees, regularization
Q79. What is A/B Testing in ML?
Explanation:
Comparing two versions of a model to determine which performs better.
Memory Trick:
"Model A vs Model B" → like testing two medicines.
How to Answer:- Split traffic between two model versions
- Measure performance metrics for each
- Choose version with better results
Q80. What is MLOps?
Explanation:
Machine Learning Operations - practices for deploying and maintaining ML systems.
Memory Trick:
"ML + DevOps" → bringing ML models to production safely.
How to Answer:- Practices for ML model lifecycle
- Includes: CI/CD, monitoring, versioning
- Goal: reliable production ML systems
Q81. What is Model Deployment?
Explanation:
Process of making trained model available for real-world use.
Memory Trick:
"From lab to real world" → putting model in production.
How to Answer:- Making model available for predictions
- Options: API, batch processing, edge deployment
- Considerations: latency, scalability, monitoring
Q82. What is Model Monitoring?
Explanation:
Tracking model performance and data quality in production.
Memory Trick:
"Health check for models" → watching for problems in production.
How to Answer:- Track model performance over time
- Monitor for data drift, concept drift
- Set up alerts for performance degradation
Q83. What is Data Drift?
Explanation:
When input data distribution changes over time compared to training data.
Memory Trick:
"Data moving away" → like river changing course.
How to Answer:- Input features' distribution changes
- Can degrade model performance
- Detection: statistical tests, KL-divergence
Q84. What is Concept Drift?
Explanation:
When relationship between input features and target variable changes.
Memory Trick:
"Rules of the game change" → same input, different output.
How to Answer:- Relationship between X and Y changes
- More serious than data drift
- Solution: retrain model with new data
Q85. What is Docker in ML?
Explanation:
Containerization platform for packaging ML applications with dependencies.
Memory Trick:
"Shipping container for code" → includes everything needed to run.
How to Answer:- Packages application with dependencies
- Ensures consistent environment
- Easier deployment and scaling
Q86. What is API in ML Context?
Explanation:
Application Programming Interface for serving ML model predictions.
Memory Trick:
"Restaurant waiter" → takes your order (data), brings prediction back.
How to Answer:- Interface for sending data and receiving predictions
- Commonly using REST APIs
- Enables real-time model serving
Q87. What is AWS SageMaker?
Explanation:
Amazon's cloud platform for building, training, and deploying ML models.
Memory Trick:
"ML factory in the cloud" → end-to-end ML platform.
How to Answer:- Fully managed ML platform
- Includes: notebooks, training, deployment
- Supports popular ML frameworks
Q88. What is Google Cloud AI Platform?
Explanation:
Google's cloud services for machine learning and artificial intelligence.
Memory Trick:
"Google's ML toolkit" → leverage Google's AI expertise.
How to Answer:- Suite of ML and AI services
- Includes: AutoML, Vertex AI, pre-trained APIs
- Integrated with Google Cloud infrastructure
Q89. What is Feature Store?
Explanation:
Centralized repository for storing and managing ML features.
Memory Trick:
"Supermarket for features" → organized storage of processed data.
How to Answer:- Centralized feature management system
- Ensures feature consistency across projects
- Enables feature reuse and discovery
Q90. What is Model Versioning?
Explanation:
Tracking different versions of ML models for reproducibility and rollback.
Memory Trick:
"Git for models" → track changes and versions.
How to Answer:- Track model changes over time
- Enable rollback to previous versions
- Includes metadata (performance, training data)
Q91. What is Batch vs Real-time Prediction?
Explanation:
Two approaches for serving model predictions based on timing requirements.
Memory Trick:
"Batch = cooking for many, Real-time = order on demand"
How to Answer:- Batch: Process large datasets periodically
- Real-time: Immediate predictions for single requests
- Choose based on latency requirements
Q92. What is AutoML?
Explanation:
Automated Machine Learning - automating parts of the ML pipeline.
Memory Trick:
"AI building AI" → machines creating ML models automatically.
How to Answer:- Automates model selection and tuning
- Reduces need for ML expertise
- Examples: Google AutoML, H2O.ai
Q93. What is Explainable AI (XAI)?
Explanation:
Making AI model decisions interpretable and understandable to humans.
Memory Trick:
"Show your work" → like math teacher asking for steps.
How to Answer:- Makes model decisions transparent
- Techniques: SHAP, LIME, attention visualization
- Important for trust and compliance
Q94. What is SHAP?
Explanation:
SHapley Additive exPlanations - method for explaining individual predictions.
Memory Trick:
"Feature contribution score" → how much each feature helped the prediction.
How to Answer:- Calculates feature importance for individual predictions
- Based on game theory (Shapley values)
- Provides positive/negative contributions
Q95. What is Model Bias in AI?
Explanation:
Unfair discrimination against certain groups in model predictions.
Memory Trick:
"AI learning human prejudices" → models inherit training data biases.
How to Answer:- Unfair treatment of certain groups
- Sources: biased training data, feature selection
- Mitigation: diverse data, fairness metrics, bias testing
Q96. What is Federated Learning?
Explanation:
Training models across decentralized data without sharing raw data.
Memory Trick:
"Learn together, keep data private" → share learning, not data.
How to Answer:- Training on distributed data without centralization
- Preserves data privacy
- Example: smartphone keyboard predictions
Q97. What is Edge AI?
Explanation:
Running AI models on local devices rather than cloud servers.
Memory Trick:
"AI in your pocket" → processing locally on device.
How to Answer:- AI processing on local devices
- Benefits: low latency, privacy, offline capability
- Challenges: limited compute resources
Q98. How would you handle missing data?
Explanation:
Strategies for dealing with incomplete datasets in ML projects.
Memory Trick:
"Fill, Drop, or Flag" → three main approaches to missing data.
How to Answer:- Remove: drop rows/columns with too many missing values
- Impute: fill with mean, median, mode, or predictive models
- Flag: create indicator variables for missingness
Q99. How would you improve a model's performance?
Explanation:
Systematic approaches to enhance ML model accuracy and efficiency.
Memory Trick:
"M.O.R.E.D.A.T.A" → More data, Optimize features, Regularize, Ensemble, Different algorithms, Tune hyperparameters, Augment.
How to Answer:- Get more/better data
- Feature engineering and selection
- Try different algorithms
- Hyperparameter tuning
- Ensemble methods
Q100. How do you approach a new ML problem?
Explanation:
Systematic methodology for tackling machine learning projects from scratch.
Memory Trick:
"D.A.T.A.S.C.I.E.N.C.E" → Define, Analyze, Transform, Apply models, Select best, Communicate, Implement, Evaluate, Never stop learning, Celebrate success, Evolve.
How to Answer:- Understand the business problem and success metrics
- Explore and analyze data thoroughly
- Start with simple baseline model
- Iterate and improve systematically
- Evaluate and deploy with monitoring
