10+ 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

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.

LLMS stands for Large Language Models, vector databases are used for efficient storage and retrieval of vectors, RAG stands for Retrieval-Augmented Generation.

• LLMS refers to large language models used in natural language processing tasks.

• Vector databases are designed for storing and querying vector data efficiently.

• RAG is a model that combines retrieval and generation in natural language processing tasks.

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.

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.

ML algorithms vary in depth and complexity, with different use cases in various industries.

• ML algorithms can range from simple linear regression to complex deep learning models like neural networks.

• Use cases for ML algorithms include image recognition in healthcare, fraud detection in finance, and recommendation systems in e-commerce.

• Depth of ML algorithms refers to the complexity and number of layers in the model, with deeper models typically able to capture more intricate p...read more

You can sort a list in Python using the built-in sort() method or the sorted() function.

• Use the sort() method to sort the list in place.

• Use the sorted() function to return a new sorted list without modifying the original list.

• You can specify the reverse parameter as True to sort in descending order.

• Example: list = [3, 1, 4, 1, 5, 9, 2]; list.sort(); sorted_list = sorted(list)

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

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.

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

AI is used in real world scenarios like autonomous vehicles, healthcare diagnosis, fraud detection, and personalized recommendations.

• Autonomous vehicles - AI is used for self-driving cars to navigate and make decisions on the road.

• Healthcare diagnosis - AI helps in analyzing medical images and data to assist doctors in diagnosing diseases.

• Fraud detection - AI algorithms can detect patterns of fraudulent activities in financial transactions.

• Personalized recommendations - AI is...read more

Projects are specific tasks or assignments undertaken to achieve a particular goal or outcome.

• Projects involve a defined scope, timeline, and budget.

• They require a team of individuals with specific skills and expertise.

• Examples include developing a new software application, implementing a machine learning model, or conducting a research study.