Top AI Interview Questions (and Answers) for 2024

Given the hype over AI technology, it’s easy to forget that behind every bot lies a human brain. And with 50% of companies reporting difficulties sourcing vital AI skills, algorithm architects are precious.

The global AI market is currently growing by 28% annually, and over 60% of large companies already use AI in everyday operations, meaning employees with top AI skills are only becoming more valuable in the talent market. But how do you source the best talent in such a competitive space?

You could ask ChatGPT about every applicant, but we have a better suggestion: using smart AI interviews and assessment techniques to find the ideal hires.

Let’s unpack that in more detail. Read on to explore popular AI career paths and critical skills, and use our AI interview questions to train your recruiting algorithm.

TL;DR — Key Takeaways

• Artificial intelligence engineers may specialize in different areas of AI. Examples include NLP, machine learning, Deep Learning, Data Science, Image processing, and Continuous Learning.

• Different AI engineering jobs require different skills. Companies should check essential skills for each role and tailor recruitment processes to find candidates matching those abilities.

• Targeted interview questions and answers help find the best candidates. Based on job descriptions, choose a mixture of AI interview questions. Challenge candidates to show expert knowledge and awareness of general AI issues.

• Combine artificial intelligence questions with other assessment techniques for better results. Scenario-based skills assessments, project reviews, and behavioral questions are all worth considering.

• Skills tests are a core part of any AI recruitment process. Toggl Hire’s assessment tools let you customize tests for AI roles. Gather candidate data and use test results with interview feedback to rank candidates accurately.

Most in-demand artificial intelligence skills

Before we get into artificial intelligence interview questions, it’s helpful to know a bit about core AI engineering areas and required skills (feel free to skip this part if you’re a technical recruiter, though). AI is diverse, and few people apply to be just an artificial intelligence engineer.

Different roles require different skills—something for both employers and candidates to think about. Use these sections to narrow your job search or write focused job descriptions and interview scripts.

Natural language processing

Natural Language Processing (NLP) uses AI technologies to understand the meaning of human language. NLP goes beyond detecting keywords. It looks at structure, word choice, and emotional content to derive valuable insights.

Popular applications include:

• Chatbots use NLP to understand customer queries and deliver responses that resemble human intelligence. Understanding sentence structure helps bots identify what information to provide, even if human language inputs aren’t grammatically perfect.

• Sentiment analysis detects whether language is positive, negative, or neutral. Companies can assess vast amounts of social media traffic or customer feedback and tweak their brand according to customer opinions.

• Language translation accurately converts texts to other languages — a huge bonus for companies with multinational audiences. You’ve probably seen it in action via Google Translate or Siri.

These functions seem completely automated — at least from the user’s point of view. But in reality, it takes human engineers to design and maintain NLP-based systems. Critical skills include:

• Knowledge of NLP libraries (for example, NLYK and SPaCY)

• Using Deep Learning Algorithms

• Coding skills (Python and Java in particular)

• Background human language expertise. For example, linguistics knowledge and semantic analysis.

• General text processing knowledge

Machine learning

Machine learning (ML) tools learn from their mistakes to improve performance. In theory, a machine-learning model requires very little human input. Provided the initial conditions are right, machine learning tools operate continuously. With more iterations, ML systems steadily get better at their jobs.

As with NLP, machine learning is a big deal right now. Use cases include:

• Predictive analytics mine vast amounts of historical data to anticipate the rise and fall of markets or changing demographics — an advantage for traders and retailers.

• Recommendation systems. When Amazon delivers a set of recommended products, a machine learning algorithm is at work. ML identifies browsing and purchasing patterns, finding connections faster and more accurately than the human eye.

• Anomaly detection. ML plays a critical cybersecurity role by identifying potential threats and exploits (weak spots in company software). Engineers can plug those gaps to keep data safe.

Vital skills for a Machine Learning expert include:

• Computer science background. MSc. PhD qualifications are ideal.

• Mastery of machine learning algorithm design

• Proficiency in relevant programming languages (e.g. Python and C++)

• Statistical analysis and formal statistical training are desirable

• Experience with ML frameworks like TensorFlow and Scikit-learn

• Evaluating and improving models

Deep learning

Deep Learning uses layered neural networks to analyze large volumes of unstructured data. The concept of Deep Learning mimics the human brain, assessing text, images, or even voice to extract relevant insights.

Applications include:

• Speech recognition. Artificial neural networks don’t just record speech and collect keywords. They use Deep Learning to assess tone of voice and sentence structure. Networks can even divide speech into individual phonemes, achieving extremely high accuracy.

• Driverless vehicles rely on Deep Learning to understand their environment. AI systems interpret road layouts, traffic lights, and moving bodies, finding a safe route to their destination.

• Personalization tools also use Deep Learning to assess customer preferences. Neural networks analyze past activity, time spent on pages, and customer feedback to deliver recommendations.

If you want to excel as a Deep Learning engineer (or are hiring one and want to ensure they have the skills needed to succeed on the job), the following skills are non-negotiable:

• Familiarity with Python and associated frameworks (Keras and PyTorch).

• Knowledge of specialist programming languages. For example, R (for statistical analysis) and Java (for enterprise-wide applications).

• GPU programming experience. Builders often use GPUs to create neural networks. Deep Learning frameworks are optimized for GPU usage.

• General knowledge of neural network design and artificial intelligence. How to connect the input layer and output to achieve desired outcomes.

Continuous learning

Continuous learning is like a more advanced version of machine learning. In continuous learning systems, models adapt in response to new data. Standard machine learning systems generally use the same model and rely on an initial training dataset.

With CL systems, engineers don’t need to reinvent their models. Systems evolve in dynamic environments — they won’t become outdated like ML alternatives. Applications include:

• Fraud detection systems use continuous learning to adapt to changing criminal methods. Models evolve to avoid false positives, and pattern recognition anticipates fraud.

• Stock traders use CL to understand market patterns and buy or sell at the correct moment. Real-time analysis keeps traders ahead of market moves (in theory).

• Digital retailers use CL in recommendation systems. Models change according to consumer tastes, protecting companies against rapid changes in behavior.

Adaptive control systems used in passenger aircraft or advanced vehicles use CL to assess external conditions and factors like weight or fuel.

Skills needed by continuous learning specialists include:

• Model updating techniques. Engineers must know how to set initial conditions and assess model performance.

• Familiarity with CL frameworks like Apache Spark, Vowpal Wabbit, and River.

• Understanding of incremental learning algorithms underlying CL deployments.

Prompt engineering

Prompt engineering involves writing and optimizing prompts that interact with AI models. Companies use prompts to instruct AI models and guide their activities.

Currently, prompting applies mainly to Large Language Models (LLMs) — with GPT-3/4 being the most important.

Prompt engineering serves several roles (it’s not just about creating flashy AI images). Uses include:

• Improving AI performance in natural language processing. Prompts guide NLP systems towards desirable outputs. For example, translation systems rely on specific prompts to understand source and target languages.

• Automated content generation for customer support. Well-written prompts allow bots to field customer queries accurately and in line with customer needs.

• Market research. Prompts steer machine learning tools by asking the right questions. Companies may write prompts for different audience segments or products. Correct wording ensures useful, relevant responses.

Skills required by elite prompt engineers include:

• Writing. AI models need clear instructions, so prompt engineers must write accurately and concisely.

• Understanding LLM systems. Writing correct prompts is only possible when engineers know how AI models work. Without background knowledge, outputs will likely be imprecise or poorly optimized.

• Iteration and analysis. Prompt engineers don’t just write queries. They assess and improve prompts to obtain high-quality outputs. The ability to track and analyze performance is critical.

Data Science

Modern businesses rely on AI to turn raw data into forecasts and strategic guidance. Engineers design systems to deliver these outcomes, often with game-changing consequences.

Examples of AI data science include:

• Business intelligence. AI analyzes market and internal performance trends. Trend data guides decisions about how to reach customers and product placement, not to mention hiring or investment.

• Healthcare analytics. Healthcare generates vast amounts of patient data that, if properly analyzed, could save lives. AI tools detect patterns in patient data, suggesting new treatments or ways to use resources.

• Financial forecasts. AI tools consider historical performance, current cash flow, and market trends — forecasting potential growth scenarios.

Vital skills for an AI data scientist include:

• Data manipulation. AI engineers must understand data structure, data cleaning, and transforming datasets as models require.

• Data visualization. To extract information, engineers also need data visualization and querying expertise. Knowledge of Python libraries like Madplotlib or Seaborn is critical.

• Programming skills. AI data analysts tend to need Python or R experience. Experience with data management tools like Spark or Hadoop is also helpful.

Computer vision

Computer vision refers to AI systems that interpret and understand visual information. AI engineers create systems to detect visual patterns. Their algorithms filter noise, classify objects, and understand context when deciding the right actions.

Applications of computer vision include:

• Object detection in driverless cars or security systems. AI tools must understand relevant objects and avoid false positives.

• Medical imaging. AI algorithms detect anomalies in medical scans. They aid diagnosis, predict disease progression, and clear up medical images to provide more information about potential problems.

• Facial recognition. AI tools verify identities at access points and scan sensitive areas for unauthorized individuals. They even scan faces to detect sentiments like disgust or satisfaction — a handy tool for physical retailers.

Vital skills for an AI computer vision engineer include:

• Image processing. Engineers should be experts in object detection, enhancing images, and extracting relevant features.

• Familiarity with computer vision libraries is essential (OpenCV is the most popular option).

• Knowledge of convolutional neural networks (CNNs) that form the basis of AI image processing systems.

Best AI interview questions to ask (with example answers)

As you can see, that’s a lot of sub-categories for job seekers and recruiters, but you can count on one thing: Whatever AI career you look at, competition will be fierce. Preparing for interviews is all-important (both for employers and candidates).

Skills shortage or not, companies must discard candidates who mix up eigenvalues and eigenvectors. But if you ask poor questions, it’s easy to miss talented hires or choose candidates whose skills don’t match their future roles.

The situation is no different from the other side of the interview table. Well-prepared candidates will impress, while knowledge gaps will instantly eliminate others from the recruitment process.

The good news is that preparation avoids these pitfalls. As our old friend ChatGPT says:

Researching interview questions ensures you’re not just learning, but “machine earning!” Prep builds confidence, impresses employers, and secures your AI career path.

We’ve tailored our best AI interview questions for both employers and candidates. Here we go…

• Question 1: What is the difference (if any) between Artificial Intelligence and machine learning?

  • Answer: Artificial Intelligence (AI) encompasses the broader field of creating intelligent systems capable of tasks that typically require human intelligence. Machine learning is a subset of AI. Machine learning algorithms learn from data to improve performance on specific tasks without explicit programming. AI encompasses machine learning but extends to other techniques like natural language processing and robotics.

• Question 2: What is fuzzy logic, and why is it an important concept in AI?

  • Answer: Fuzzy logic extends traditional binary true/false logic to handle uncertainty by assigning degrees of truth between 0 and 1. In AI, it’s vital for decision-making in complex, ambiguous environments. Fuzzy logic-based machine learning algorithms accommodate imprecise data, making them suitable for applications requiring human-like reasoning and adaptive behavior.

• Question 3: Do you think AI will have a positive social impact?

  • Answer: AI has the potential for positive social impact through advancements in healthcare, education, and accessibility. Machine learning and AI systems can improve diagnostic accuracy, personalize learning experiences, and enhance accessibility for people with disabilities. Ethical implementation and regulation are crucial to maximizing these benefits while addressing potential challenges in employment and privacy.

• Question 4: Why is it critically important to plan a machine-learning model?

  • Answer: A machine learning model is an algorithm designed to learn patterns from data, making predictions or decisions without explicit programming. Planning ensures models align with data structure and business goals, optimizing accuracy and performance.

    Careful consideration of features, model complexity, and training dataset quality mitigates biases and overfitting. Rigorous planning ensures the model’s reliability, interpretability, and suitability for real-world applications in artificial intelligence.

• Question 5: What is AI model training?

  • Answer: AI model training is the process of optimizing algorithms using data to learn patterns and make predictions. It involves preparing a structured training dataset, selecting appropriate features, and iteratively adjusting the model’s parameters to minimize errors. The training process aims to enhance the model’s accuracy and generalization. This ensures effective decision-making in artificial intelligence applications.

• Question 6: What are the main ways to train AI models?

  • Answer: AI models are primarily trained through supervised learning, where models learn from labeled data, predicting outputs based on inputs. Unsupervised learning identifies patterns without labeled data, while reinforcement learning uses rewards and penalties for learning. Transfer learning adapts pre-trained models to new tasks, and self-supervised learning predicts missing parts of data. Each method optimizes models for specific AI tasks.

• Question 7: What is the difference between supervised and unsupervised learning?

  • Answer: Supervised learning involves training a model on labeled data, meaning the input data is paired with the correct output. Examples include classification and regression tasks. Unsupervised learning, on the other hand, works with unlabeled data, seeking to identify patterns or groupings within the data. Examples include clustering and dimensionality reduction.

• Question 8: Can you explain what overfitting is and how to prevent it?

  • Answer: Overfitting occurs when a model learns the noise in the training data rather than the actual signal. This results in poor generalization to unseen data. To prevent overfitting, use techniques such as cross-validation, regularization (L1/L2), pruning in decision trees, dropout in neural networks, and ensuring a sufficient amount of training data.

• Question 9: What is the difference between transfer learning and fine-tuning?

  • Answer: Transfer learning adapts pre-trained models to new tasks with minimal additional training, leveraging learned features. Fine-tuning involves adjusting pre-trained model parameters on new data to improve task-specific performance. Both accelerate AI development by reusing knowledge, with transfer learning focusing on broader adaptation and fine-tuning, refining specific capabilities.

• Question 10: How does machine learning programming differ from standard coding?

  • Answer: Machine learning programming focuses on training models to learn patterns from data, requiring algorithms that adjust and optimize automatically. Standard coding involves writing deterministic instructions for specific tasks. Machine learning involves data preprocessing, model training, and evaluation phases, emphasizing statistical analysis and algorithm selection. It aims for predictive accuracy rather than traditional step-by-step procedural outcomes.

• Question 11: Explain the difference between classification and regression

  • Answer: Classification in artificial intelligence involves predicting discrete categories or labels, such as identifying whether an email is spam. Regression predicts continuous numerical values, like forecasting house prices based on features. Both are machine learning models: classification assigns categories, while regression estimates values within a range.

• Question 12: How can you remove bias from machine learning models?

  • Answer: To remove bias from machine learning models, employ diverse and representative datasets, audit data for biases, and preprocess data to mitigate skewed distributions. Use fairness-aware machine learning techniques and consider ethical implications throughout model development. Incorporate human intelligence to review and interpret model outputs, ensuring fairness and inclusivity in AI applications.

• Question 13: How can companies benefit from an AI-based supply chain optimization algorithm?

  • Answer: Companies benefit from supply chain optimization algorithms due to enhanced efficiency, cost reductions, and more accurate demand forecasting. These algorithms optimize inventory management, streamline logistics, and predict disruptions, leading to better decision-making and increased competitiveness. Enhanced data analysis capabilities enable proactive strategies, ensuring a more resilient and responsive supply chain.

• Question 14: Why is the Turing Test an important concept in AI?

  • Answer: The Turing Test is crucial in AI because it measures a machine’s ability to exhibit intelligent behavior indistinguishable from a human’s. This concept drives advancements in artificial intelligence and machine learning, pushing researchers to develop more sophisticated, human-like systems. It challenges AI to achieve higher levels of natural language understanding and interaction, setting benchmarks for future AI capabilities.

• Question 15: What does AI model explainability mean, and why does it matter?

  • Answer: AI model explainability refers to the clarity with which the workings of an artificial intelligence system can be understood and interpreted. It matters because it ensures transparency, builds trust, facilitates debugging, and aids compliance with regulations. Clear insights into model decisions help stakeholders verify and validate the AI’s reliability and fairness.

• Question 16: What are Deep Learning models, and when should you use them?

  • Answer: Deep Learning models are advanced machine learning systems using deep neural networks to process large datasets and extract complex patterns. Use them for tasks requiring high accuracy in image and speech recognition, natural language processing, and predictive analytics, where traditional methods fall short. Their deep architecture allows them to handle intricate data representations effectively.

• Question 17: What is the difference between machine learning and deep learning?

  • Answer: Machine learning algorithms enable systems to learn from data and make predictions, often using statistical methods. Deep learning is a subset of machine learning utilizing deep neural networks with multiple layers to learn complex representations directly from data, excelling in tasks like image and speech recognition.

• Question 18: What is the difference between Strong AI and Weak AI?

  • Answer: The key differences between Strong AI and Weak AI lie in their capabilities and data structures. Strong AI, or Artificial General Intelligence (AGI), can understand, learn, and apply knowledge across diverse tasks like humans. Weak AI, or Narrow AI, is specialized for specific tasks and relies on predefined data structures. Strong AI aims for true cognition, while Weak AI focuses on practical applications.

• Question 19: How can we ensure privacy and security when operating AI models?

  • Answer: To ensure privacy and security when operating AI models, integrate strong encryption, access controls, and data anonymization techniques. Employ human intelligence to monitor and manage AI technologies, ensuring compliance with regulations and ethical standards. Regularly update systems, conduct audits, and implement robust data governance frameworks to protect sensitive information and maintain trust.

• Question 20: What is Natural Language Processing, and how can it help companies?

  • Answer: Natural Language Processing (NLP) is an AI field focused on enabling computers to understand, interpret, and respond to human language. It helps companies by automating customer service through chatbots, analyzing customer sentiment, improving search engine results, and extracting actionable insights from large volumes of text data, enhancing customer experience and operational efficiency.

• Question 21: What is image recognition, and how can you ensure accurate image processing?

  • Answer: Image recognition identifies objects, patterns, or scenes in images using artificial intelligence. To ensure accurate image processing, employ deep learning models like convolutional neural networks (CNNs), train them with diverse and labeled datasets, optimize hyperparameters, and use data augmentation techniques. Regular validation and fine-tuning enhance model performance and robustness.

• Question 22: Why is TensorFlow important for AI engineers? How does it help you build AI systems?

  • Answer: TensorFlow is vital for AI engineers due to its flexible, scalable ecosystem supporting CPUs, GPUs, and TPUs. It simplifies building complex neural networks with high-level APIs like Keras and low-level APIs for fine control. Tools like TensorFlow Serving and TensorFlow Lite streamline deployment, enabling a seamless transition from research to real-world applications.

• Question 23: What are the business uses of entity recognition?

  • Answer: Entity recognition, a form of artificial intelligence, automates tasks by identifying and extracting key information such as names, dates, and locations from text data. It enhances operational efficiency, improves data accuracy, and supports applications in customer service, finance, healthcare, and beyond.

• Question 24: Briefly explain Markov’s decision process

  • Answer: The Markov Decision Process (MDP) is rooted in probability theory and artificial intelligence and models decision-making in stochastic environments. It involves states, actions, transition probabilities, rewards, and policies. Agents use MDPs to optimize sequences of actions, balancing exploration and exploitation to achieve long-term goals in uncertain scenarios.

• Question 25: How can we use MDPs to understand AI model performance?

  • Answer: MDPs help gauge an AI model’s performance by treating it as an agent making decisions in uncertain environments. States represent different model states (e.g., predictions), actions are decisions made (e.g., classifications), rewards are outcomes (e.g., accuracy), and policies optimize decision sequences. This framework provides insights into how well the model navigates various scenarios and achieves goals.

• Question 26: What is Game Theory, and how does it relate to Machine Learning?

  • Answer: Game Theory studies strategic interactions where the decisions of one participant affect outcomes for others. In machine learning, it models scenarios where multiple agents (e.g., algorithms, players) optimize decisions based on others’ actions. Game Theory informs algorithm design in competitive settings (e.g., adversarial networks) and cooperative strategies (e.g., multi-agent reinforcement learning).

• Question 27: What is an artificial neural network? Provide a couple of use cases for artificial neural networks in modern businesses

  • Answer: Artificial neural networks (ANNs) mimic the human brain to process information. Use cases include image and speech recognition (CNNs), natural language processing (RNNs), and financial forecasting. ANN’s layered architecture and learning algorithms enable complex pattern recognition and decision-making in various AI applications.

• Question 28: How do eigenvalues and eigenvectors differ?

  • Answer: In AI, eigenvalues determine the magnitude of data variance in techniques like PCA, aiding in dimensionality reduction. They identify key components, enabling effective dimensionality reduction and data compression in machine learning.

    Eigenvectors identify the principal directions of data spread, which is crucial for feature extraction and data interpretation. They also enhance the efficiency of algorithms such as clustering and classification.

• Question 29: What are the advantages of using ensemble learning to assess AI models?

  • Answer: Ensemble learning combines multiple models to improve overall AI performance and robustness compared to individual models. Techniques like bagging, boosting, and stacking aggregate predictions, reducing variance and bias, and improving accuracy. This approach leverages the strengths of each model, making the final ensemble model more reliable and effective.

How to assess AI skills in interviews

Are interview questions enough to separate AI imposters from genuine talent? Possibly not. Picking the right artificial intelligence interview questions matters when hiring technical talent, but your work doesn’t stop there.

Recruiters can and should use other methods in combination to drill down into specific skills or competencies, assess soft skills, and ensure a good cultural fit. For example:

• Technical assessments: Combine interviews with testing exercises to explore specific skills. Coding tests verify candidate skills and also show they can operate under pressure. They are even more effective if you set tasks related to problems encountered in your regular workflow.

• Project reviews: Experience is a crucial asset for artificial intelligence engineers. Simply showing knowledge isn’t the same as demonstrating a history of applying knowledge. Assess candidates’ track records by asking them to present past projects — how they contributed and what they learned.

• Behavioral questions: Technical questions check concrete knowledge. Behavioral interview questions complement them by probing a candidate’s personality and character. Ask about working with colleagues and facing adversity to find engineers who build teams and efficient AI systems.

Assess candidates with Toggl Hire

Neural networks and machine learning algorithms may tick along in the background, but humans build AI systems, choose training data, and assess a model’s performance. Recruiting ace AI engineers is all about skills. Assessing skills can be tough, but Toggl Hire offers a user-friendly solution.

Our skills tests provide a streamlined pathway for AI recruitment. Users can automate technical assessments and schedule structured interviews (using the questions above). You can customize skills tests for engineer roles, ensuring a perfect skills match every time.

Analytics and automated scoring give you real-time feedback about candidate performance, while our tests integrate with other helpful hiring features, like our talent pool or candidate management tools.

The best part? Transitioning to skills-based AI hiring is simple. Just create a free Toggl Hire account and create custom skills tests to snag the brightest AI talent.