20+ Artificial Intelligence Developer Interview Questions and Answers

Arrays store elements in contiguous memory locations, while linked lists store elements in nodes with pointers to the next node.

• Arrays have fixed size, while linked lists can dynamically grow or shrink.

• Accessing elements in arrays is faster (O(1)), while accessing elements in linked lists is slower (O(n)).

• Inserting or deleting elements in arrays can be inefficient as it may require shifting elements, while in linked lists it can be done easily by changing pointers.

• Example: Ar...read more

Designing a database schema for optimal performance involves normalization, indexing, partitioning, and denormalization.

• Normalize the database to reduce redundancy and improve data integrity.

• Index frequently queried columns to speed up search operations.

• Partition large tables to distribute data across multiple storage devices for faster access.

• Consider denormalization for read-heavy applications to reduce join operations.

• Use appropriate data types and constraints to optimize ...read more

String function in structures refers to operations that can be performed on string data within a data structure.

• String functions in structures can include operations like concatenation, substring extraction, searching, and comparison.

• For example, a structure may have a string member representing a person's name, and string functions can be used to manipulate or extract parts of the name.

• These functions can help in organizing and managing string data within a structured format...read more

A stack is a data structure that follows the Last In, First Out (LIFO) principle.

• Stack is a collection of elements with two main operations: push (adds an element to the top) and pop (removes the top element).

• Common implementations of stack include using arrays or linked lists.

• Example: Pushing elements 1, 2, and 3 onto a stack would result in 3 being at the top, followed by 2, and then 1.

I was responsible for developing AI algorithms and models to improve customer experience.

• Developed machine learning models to analyze customer behavior and preferences

• Implemented natural language processing algorithms to improve chatbot interactions

• Collaborated with cross-functional teams to integrate AI solutions into existing systems

Inheritance in OOP allows a class to inherit properties and behaviors from another class.

• Inheritance promotes code reusability by allowing a new class to take on the attributes and methods of an existing class.

• The class that is being inherited from is called the parent class or superclass, while the class that inherits is called the child class or subclass.

• Subclasses can add new methods or override existing methods from the superclass.

• Example: Class Car can inherit from class...read more

A virtual function is a function in a base class that is declared using the keyword 'virtual' and can be overridden by a function in a derived class.

• Virtual functions allow a derived class to provide a specific implementation of a function that is already defined in a base class.

• They enable polymorphism, where a function call is resolved at runtime based on the actual type of object being referred to.

• Virtual functions are used in object-oriented programming to achieve dynamic...read more

My favorite coding language is Python because of its simplicity, readability, and versatility.

• Python is known for its clean and readable syntax, making it easier to write and maintain code.

• Python has a large standard library and many third-party libraries, making it versatile for various applications.

• Python is widely used in AI and machine learning due to libraries like TensorFlow and scikit-learn.

Spacy is an open-source library for natural language processing. Models can be saved using the 'to_disk' method.

• Spacy is a popular Python library for NLP tasks

• It provides pre-trained models for various languages

• To save a model, use the 'to_disk' method of the model object

• Saved models can be loaded later using the 'load' method

Some of the best time series models for non-stationary data include ARIMA, SARIMA, and LSTM.

• ARIMA (AutoRegressive Integrated Moving Average) model is commonly used for non-stationary time series data.

• SARIMA (Seasonal ARIMA) model is an extension of ARIMA that can handle seasonal patterns in the data.

• LSTM (Long Short-Term Memory) neural network model is effective for capturing long-term dependencies in time series data.

RAG stands for Red, Amber, Green. It is a color-coded system used to indicate the status of tasks or projects.

• RAG is commonly used in project management to quickly communicate the status of tasks or projects.

• Red typically indicates that a task or project is behind schedule or facing significant issues.

• Amber signifies that there are some concerns or risks that need to be addressed.

• Green means that a task or project is on track and progressing as planned.

• For example, a project ...read more

RNN architecture is a type of neural network that can remember past information and use it in current predictions.

• RNN stands for Recurrent Neural Network

• It has loops in the network, allowing information to persist

• Useful for sequential data like time series, text, and speech recognition

• Examples include predicting the next word in a sentence or stock market forecasting

List compression in Python is a technique to create a new list by applying an expression to each item in an existing list.

• Use list comprehension syntax: [expression for item in list]

• Example: numbers = [1, 2, 3, 4, 5]; squared_numbers = [x**2 for x in numbers]

• You can also add conditions: [expression for item in list if condition]

• Example: even_numbers = [x for x in numbers if x % 2 == 0]

A nested dictionary is a dictionary within a dictionary, allowing for multiple levels of key-value pairs.

• Nested dictionaries are useful for organizing and storing complex data structures.

• Each key in a nested dictionary can have its own dictionary as a value.

• Accessing values in a nested dictionary requires specifying each key level.

• Example: {'key1': {'key2': 'value'}}

Print prime numbers in a given range

• Iterate through the range of numbers

• Check if each number is prime

• Print the prime numbers

Yes, I have experience with cloud computing.

• I have worked with Amazon Web Services (AWS) and Microsoft Azure.

• I have experience with deploying and managing applications on cloud platforms.

• I am familiar with cloud storage solutions such as Amazon S3 and Azure Blob Storage.

• I have worked with cloud-based machine learning services such as AWS SageMaker and Azure Machine Learning.

• I have experience with serverless computing using AWS Lambda and Azure Functions.

Convolution is a mathematical operation that combines two functions to produce a third function.

• Convolution involves sliding one function over another and multiplying the overlapping values at each point.

• It is commonly used in signal processing, image processing, and neural networks.

• In image processing, convolution is used for tasks like blurring, sharpening, edge detection, etc.

YOLO (You Only Look Once) is a real-time object detection system that uses a single neural network to predict bounding boxes and class probabilities directly from full images.

• YOLO divides the input image into a grid of cells and each cell is responsible for predicting a fixed number of bounding boxes.

• It predicts the class probabilities for each box and rescales the boxes based on the dimensions of the grid cell.

• The final output is a list of bounding boxes with their correspon...read more

NER Model is a machine learning model used to identify and classify named entities in text.

• NER stands for Named Entity Recognition.

• It is a subtask of information extraction that locates and classifies named entities in text into predefined categories.

• Named entities can include names of people, organizations, locations, dates, and more.

• NER models are trained using labeled data to recognize and classify named entities in new text.

• Example: In the sentence 'Apple Inc. is headquar...read more

KNN is a supervised learning algorithm used for classification and regression, while K-means is an unsupervised clustering algorithm.

• KNN is used for classification and regression tasks, where the output is a class label or a continuous value.

• K-means is used for clustering data points into groups based on similarity.

• KNN requires labeled training data, while K-means does not require labeled data.

• KNN calculates the distance between data points to make predictions, while K-means ...read more