30+ AI Engineer Interview Questions and Answers

Metrics to evaluate after training an AI model

• Accuracy

• Precision

• Recall

• F1 Score

• Confusion Matrix

• ROC Curve

• Loss Function

• Learning Rate

• Training Time

Monitoring model performance post-deployment is crucial for ensuring accuracy and reliability in real-world applications.

• Model Drift Detection: Tools like Alibi Detect can identify when a model's performance degrades due to changes in data distribution.

• Performance Metrics: Use libraries like Scikit-learn to track metrics such as accuracy, precision, recall, and F1 score over time.

• Logging and Monitoring: Implement logging frameworks like ELK Stack (Elasticsearch, Logstash, Kib...read more

Transformer-based LLMs use self-attention and feedforward networks for processing sequential data efficiently.

• Architecture consists of an encoder-decoder structure, though many LLMs use only the decoder.

• Self-attention mechanism allows the model to weigh the importance of different words in a sentence.

• Positional encoding is used to retain the order of words since transformers do not have a built-in sense of sequence.

• Examples include BERT (Bidirectional Encoder Representations ...read more

The best way to store information in a Vector DB is by using a combination of indexing and compression techniques.

• Use indexing to quickly locate specific data points within the Vector DB.

• Implement compression techniques to reduce storage space and improve query performance.

• Consider using a columnar storage format for efficient data retrieval and analysis.

• Utilize vectorized processing for faster query execution on large datasets.

Computer vision architecture involves processing images through layers to extract features and make predictions.

• Input Layer: Receives raw image data, e.g., RGB values from a photo.

• Convolutional Layers: Apply filters to detect features like edges or textures, e.g., using 3x3 kernels.

• Pooling Layers: Reduce dimensionality while retaining important features, e.g., max pooling.

• Fully Connected Layers: Combine features to make final predictions, e.g., classifying images into categor...read more

Optimise retrieval from Vector DB by using indexing, caching, and query optimization techniques.

• Implement indexing on frequently queried fields to speed up retrieval.

• Utilize caching mechanisms to store frequently accessed vectors in memory for faster retrieval.

• Optimize queries by using efficient algorithms and data structures to reduce retrieval time.

• Consider using parallel processing techniques to retrieve vectors in parallel for improved performance.

I am familiar with several AI frameworks including TensorFlow, PyTorch, and Scikit-learn, each serving different purposes.

• TensorFlow: A powerful library for deep learning, widely used for building neural networks.

• PyTorch: Known for its dynamic computation graph, making it popular for research and prototyping.

• Scikit-learn: A versatile library for traditional machine learning algorithms and data preprocessing.

• Keras: A high-level API for building and training deep learning model...read more

Create a program to print a given pattern using inheritance

• Create a base class with a method to print the pattern

• Create a derived class that inherits from the base class and overrides the print method to print the specific pattern

• Use the derived class to print the pattern

Address Ai hallucination by implementing robust validation checks, diverse training data, and regular monitoring.

• Implement robust validation checks to detect and prevent hallucinations during inference.

• Use diverse training data to expose the AI model to a wide range of scenarios and reduce the likelihood of hallucinations.

• Regularly monitor the AI system's performance and behavior to catch any signs of hallucination early on.

• Consider implementing explainable AI techniques to b...read more

Computer vision is a field of artificial intelligence that enables machines to interpret and understand the visual world.

• Computer vision involves tasks such as image recognition, object detection, and image segmentation.

• It uses algorithms to process and analyze visual data from the real world.

• Applications of computer vision include facial recognition, autonomous vehicles, and medical imaging.

• Popular computer vision libraries include OpenCV and TensorFlow.

PyTorch and TensorFlow are popular frameworks for building and training machine learning models, each with unique features.

• Dynamic vs Static Graphs: PyTorch uses dynamic computation graphs, allowing for more flexibility during model development, while TensorFlow uses static graphs, which can optimize performance.

• Ease of Use: PyTorch is often praised for its intuitive and Pythonic interface, making it easier for beginners, whereas TensorFlow has a steeper learning curve but of...read more

RAG system is a project management tool used to track risks, assumptions, and issues.

• Identify and document risks, assumptions, and issues related to the project.

• Assign a RAG status (Red, Amber, Green) to each item based on severity and impact.

• Regularly review and update the RAG status of each item as the project progresses.

• Use the RAG system to prioritize and address high-risk items first.

• Communicate the RAG status to stakeholders to keep them informed.

• Examples: Red - Critica...read more

Generative AI is a type of AI that can create new data, such as images, text, or music, based on patterns it has learned.

• Generative AI uses algorithms to generate new content that is similar to the training data it has been provided.

• Examples of generative AI include deep learning models like GANs (Generative Adversarial Networks) and VAEs (Variational Autoencoders).

• Generative AI can be used in various fields such as art, music, and even in creating realistic deepfake videos.

Retrieval augmented generation is a technique that combines retrieval-based and generation-based approaches in natural language processing.

• Combines the strengths of both retrieval-based and generation-based models

• Retrieval-based model retrieves relevant information from a database or corpus

• Generation-based model generates responses based on the retrieved information

• Used in chatbots, question-answering systems, and dialogue systems

This code finds all prime numbers up to 100 using a simple algorithm in Python.

• A prime number is a natural number greater than 1 that has no positive divisors other than 1 and itself.

• To find prime numbers, iterate through numbers from 2 to 100.

• For each number, check if it is divisible by any number from 2 to its square root.

• If a number is not divisible by any of these, it is prime.

• Example: 2, 3, 5, 7, 11, 13, 17, 19, 23, 29, 31, 37, 41, 43, 47, 53, 59, 61, 67, 71, 73, 79, 83,...read more

AI agents are systems that perform tasks autonomously, while agentic AI possesses self-awareness and decision-making capabilities.

• AI agents are designed to perform specific tasks, like chatbots answering queries.

• Agentic AI can make independent decisions, like an autonomous vehicle navigating traffic.

• AI agents operate within predefined parameters, while agentic AI can adapt and learn from experiences.

• Examples of AI agents include virtual assistants like Siri or Alexa.

• Agentic A...read more

Flow pipelines are structured sequences of data processing steps that automate workflows in AI and data engineering.

• They consist of stages where data is ingested, processed, and outputted.

• Example: A data pipeline might extract data from a database, transform it, and load it into a data warehouse (ETL process).

• Flow pipelines can be used for machine learning model training, where data flows from preprocessing to model evaluation.

• They help in maintaining reproducibility and scal...read more

Calculate the sum of all elements in an array except the current element for each position.

• Use a prefix sum array to store cumulative sums.

• Example: For array [1, 2, 3], prefix sums are [0, 1, 3]. Result is [5, 4, 3].

• Alternatively, calculate total sum and subtract current element.

• Example: For array [4, 5, 6], total is 15. Result is [11, 10, 9].

Classification is the process of categorizing data into predefined classes, while regression is the process of predicting continuous values.

• Classification involves assigning data points to predefined categories or classes

• Regression involves predicting continuous values based on input variables

• Examples of classification include spam detection and image recognition

• Examples of regression include predicting house prices and stock market trends

Prompt engineering involves designing and crafting prompts to elicit specific responses from AI models.

• Prompt engineering is crucial for fine-tuning AI models to generate desired outputs.

• It involves creating prompts that are clear, concise, and tailored to the task at hand.

• Examples include designing prompts for language models to generate coherent text or for image recognition models to identify objects.

This code checks if a given string is a palindrome, meaning it reads the same forwards and backwards.

• A palindrome is a word, phrase, or sequence that reads the same backward as forward. Example: 'radar'.

• To check for a palindrome, we can compare the string to its reverse.

• We can use Python's slicing feature to reverse the string easily.

• Consider ignoring spaces and punctuation for a more robust palindrome check. Example: 'A man, a plan, a canal, Panama!'

Designing a library management system to efficiently organize and track library resources.

• Utilize a database to store information on books, patrons, and transactions

• Implement a user-friendly interface for librarians to add, edit, and delete records

• Incorporate features such as search functionality, book reservations, and overdue notifications

I utilized various algorithms including supervised, unsupervised, and reinforcement learning for diverse AI applications.

• Supervised Learning: Used algorithms like Linear Regression and Decision Trees for predictive modeling.

• Unsupervised Learning: Implemented K-Means Clustering for customer segmentation in marketing.

• Reinforcement Learning: Applied Q-Learning for developing game AI that learns optimal strategies.

• Deep Learning: Leveraged Convolutional Neural Networks (CNNs) for ...read more

A neural network is a computational model inspired by the human brain, used for pattern recognition and machine learning tasks.

• Consists of interconnected nodes (neurons) organized in layers: input, hidden, and output.

• Uses activation functions to determine the output of each neuron, e.g., ReLU, Sigmoid.

• Trained using algorithms like backpropagation to minimize error in predictions.

• Commonly used in image recognition (e.g., CNNs for facial recognition) and natural language proces...read more

AI engineer with a passion for machine learning, data analysis, and developing innovative solutions to complex problems.

• Educational Background: Master's degree in Computer Science with a focus on AI and machine learning.

• Professional Experience: Worked at XYZ Corp, developing predictive models that improved customer retention by 20%.

• Technical Skills: Proficient in Python, TensorFlow, and data visualization tools like Tableau.

• Projects: Developed a chatbot for customer service t...read more

UiPath Automation is a leading Robotic Process Automation (RPA) tool that streamlines repetitive tasks using software robots.

• Automates repetitive tasks like data entry, invoice processing, and report generation.

• Integrates with various applications such as Excel, SAP, and web browsers.

• Uses a visual interface for designing automation workflows, making it user-friendly.

• Supports both attended and unattended automation, allowing flexibility in task execution.

• Can be deployed in clo...read more

My favorite algorithm is the Random Forest, known for its robustness and accuracy in classification tasks.

• Ensemble learning method that combines multiple decision trees.

• Reduces overfitting by averaging predictions from various trees.

• Handles both classification and regression tasks effectively.

• Example: Used in medical diagnosis to predict diseases based on patient data.

• Robust to noise and can handle large datasets with high dimensionality.