Generative AI is a newly developed field booming exponentially with job opportunities. Companies are looking for candidates with the necessary technical abilities and real-world experience building AI models. This list of interview questions includes descriptive answer questions, short answer questions, and MCQs that will prepare you well for any generative AI interview. These questions cover everything from the basics of AI to putting complicated algorithms into practice. So let’s get started with Generative AI Interview Questions!
Learn everything there is to know about generative AI and become a GenAI expert with our GenAI Pinnacle Program.
Here’s our comprehensive list of questions and answers on Generative AI that you must know before your next interview.
Answer: A Transformer is a type of neural network architecture introduced in the 2017 paper “Attention Is All You Need” by Vaswani et al. It has become the backbone for many state-of-the-art natural language processing models.
Here are the key points about Transformers:
Transformers have revolutionized NLP and continue to be crucial components in the development of advanced AI models.
Answer: Attention is a technique used in generative AI and neural networks that allows models to focus on specific input areas when generating output. It enables the model to dynamically ascertain the relative importance of each input component in the sequence instead of considering all the input components similarly.
Also referred to as intra-attention, self-attention enables a model to focus on various points within an input sequence. It plays a crucial role in transformer architectures.
How does it work?
Benefits:
This technique enables the model to attend to data from many representation subspaces by executing numerous attention processes simultaneously.
How does it work?
Benefits:
This technique enables the model to process one sequence while attending to information from another and is frequently utilised in encoder-decoder systems.
How does it work?
Benefits:
Also referred to as veiled attention, causal attention is a technique used in autoregressive models to stop the model from focussing on tokens that are presented in the future.
How does it work?
Benefits:
How Does Local Attention Work?
Benefits of Local Attention:
These attention processes have advantages and work best with particular tasks or model architectures. The task’s particular needs, the available processing power, and the intended trade-off between model performance and efficiency are typically factors that influence the choice of attention mechanism.
Answer: Transformers have largely superseded Recurrent Neural Network (RNN) architectures in many natural language processing tasks. Here’s an explanation of how and why transformers are generally considered better than RNNs:
How: Transformers process entire sequences in parallel.
Why better:
How: Transformers use self-attention to directly model relationships between all pairs of tokens in a sequence.
Why better:
How: Transformers use multi-head attention, allowing them to focus on different parts of the input for different purposes simultaneously.
Why better:
How: Transformers use positional encodings to inject sequence order information.
Why better:
How: Transformer architectures can be easily scaled up by increasing the number of layers, attention heads, or model dimensions.
Why better:
How: Pre-trained transformer models can be fine-tuned for various downstream tasks.
Why better:
How: Transformers maintain performance for both short and long sequences.
Why better:
RNNs still have a role, even if transformers have supplanted them in many applications. This is especially true when computational resources are scarce or the sequential character of the data is essential. However, transformers are now the recommended design for most large-scale NLP workloads because of their better performance and efficiency.
Answer: These models are significant advancements in natural language processing, all built on the transformer architecture.
Answer: A large language model (LLM) is a type of artificial intelligence (AI) program that can recognize and generate text, among other tasks. LLMs are trained on huge sets of data — hence the name “large.” LLMs are built on machine learning; specifically, a type of neural network called a transformer model.
To put it more simply, an LLM is a computer program that has been fed enough instances to identify and comprehend complicated data, like human language. Thousands or millions of megabytes of text from the Internet are used to train a large number of LLMs. However, an LLM’s programmers may choose to employ a more carefully selected data set because the caliber of the samples affects how successfully the LLMs learn natural language.
A foundational LLM (Large Language Model) is a pre-trained model trained on a large and diverse corpus of text data to understand and generate human language. This pre-training allows the model to learn the structure, nuances, and patterns of language but in a general sense, without being tailored to any specific tasks or domains. Examples include GPT-3 and GPT-4.
A fine-tuned LLM is a foundational LLM that has undergone additional training on a smaller, task-specific dataset to enhance its performance for a particular application or domain. This fine-tuning process adjusts the model’s parameters to better handle specific tasks, such as sentiment analysis, machine translation, or question answering, making it more effective and accurate.
Answer: Numerous tasks are trainable for LLMs. Their use in generative AI, where they may generate text in response to prompts or questions, is one of its most well-known applications. For example, the publicly accessible LLM ChatGPT may produce poems, essays, and other textual formats based on input from the user.
Any large, complex data set can be used to train LLMs, including programming languages. Some LLMs can help programmers write code. They can write functions upon request — or, given some code as a starting point, they can finish writing a program. LLMs may also be used in:
Examples of real-world LLMs include ChatGPT (from OpenAI), Gemini (Google) , and Llama (Meta). GitHub’s Copilot is another example, but for coding instead of natural human language.
Answer: A key characteristic of LLMs is their ability to respond to unpredictable queries. A traditional computer program receives commands in its accepted syntax or from a certain set of inputs from the user. A video game has a finite set of buttons; an application has a finite set of things a user can click or type, and a programming language is composed of precise if/then statements.
On the other hand, an LLM can utilise data analysis and natural language responses to provide a logical response to an unstructured prompt or query. An LLM might respond to a question like “What are the four greatest funk bands in history?” with a list of four such bands and a passably strong argument for why they are the best, but a standard computer program would not be able to identify such a prompt.
However, the accuracy of the information provided by LLMs is only as good as the data they consume. If they are given erroneous information, they will respond to user enquiries with misleading information. LLMs can also “hallucinate” occasionally, fabricating facts when they are unable to provide a precise response. For instance, the 2022 news outlet Fast Company questioned ChatGPT about Tesla’s most recent financial quarter. Although ChatGPT responded with a comprehensible news piece, a large portion of the information was made up.
Answer: The Transformer architecture is widely used for LLMs due to its parallelizability and capacity, enabling the scaling of language models to billions or even trillions of parameters.
Existing LLMs can be broadly classified into three types: encoder-decoder, causal decoder, and prefix decoder.
Based on the vanilla Transformer model, the encoder-decoder architecture consists of two stacks of Transformer blocks – an encoder and a decoder.
The encoder utilizes stacked multi-head self-attention layers to encode the input sequence and generate latent representations. The decoder performs cross-attention on these representations and generates the target sequence.
Encoder-decoder PLMs like T5 and BART have demonstrated effectiveness in various NLP tasks. However, only a few LLMs, such as Flan-T5, are built using this architecture.
The causal decoder architecture incorporates a unidirectional attention mask, allowing each input token to attend only to past tokens and itself. The decoder processes both input and output tokens in the same manner.
The GPT-series models, including GPT-1, GPT-2, and GPT-3, are representative language models built on this architecture. GPT-3 has shown remarkable in-context learning capabilities.
Various LLMs, including OPT, BLOOM, and Gopher have widely adopted causal decoders.
The prefix decoder architecture, also known as the non-causal decoder, modifies the masking mechanism of causal decoders to enable bidirectional attention over prefix tokens and unidirectional attention on generated tokens.
Like the encoder-decoder architecture, prefix decoders can encode the prefix sequence bidirectionally and predict output tokens autoregressively using shared parameters.
Instead of training from scratch, a practical approach is to train causal decoders and convert them into prefix decoders for faster convergence. LLMs based on prefix decoders include GLM130B and U-PaLM.
All three architecture types can be extended using the mixture-of-experts (MoE) scaling technique, which sparsely activates a subset of neural network weights for each input.
This approach has been used in models like Switch Transformer and GLaM, and increasing the number of experts or the total parameter size has shown significant performance improvements.
The encoder-only architecture uses only the encoder stack of Transformer blocks, focusing on understanding and representing input data through self-attention mechanisms. This architecture is ideal for tasks that require analyzing and interpreting text rather than generating it.
Key Characteristics:
Examples of Encoder-Only Models:
Answer: Large Language Models (LLMs) are known to have “hallucinations.” This is a behavior in that the model speaks false knowledge as if it is accurate. A large language model is a trained machine-learning model that generates text based on your prompt. The model’s training provided some knowledge derived from the training data we provided. It is difficult to tell what knowledge a model remembers or what it does not. When a model generates text, it can’t tell if the generation is accurate.
In the context of LLMs, “hallucination” refers to a phenomenon where the model generates incorrect, nonsensical, or unreal text. Since LLMs are not databases or search engines, they would not cite where their response is based. These models generate text as an extrapolation from the prompt you provided. The result of extrapolation is not necessarily supported by any training data, but is the most correlated from the prompt.
Hallucination in LLMs is not much more complex than this, even if the model is much more sophisticated. From a high level, hallucination is caused by limited contextual understanding since the model must transform the prompt and the training data into an abstraction, in which some information may be lost. Moreover, noise in the training data may also provide a skewed statistical pattern that leads the model to respond in a way you do not expect.
Answer: Hallucinations could be seen as a characteristic of huge language models. If you want the models to be creative, you want to see them have hallucinations. For instance, if you ask ChatGPT or other large language models to provide you with a fantasy story plot, you want it to create a fresh character, scene, and storyline rather than copying an already-existing one. This is only feasible if the models don’t search through the training data.
You could also want hallucinations when seeking diversity, such as when soliciting ideas. It’s similar to asking models to come up with ideas for you. Though not precisely the same, you want to offer variations on the current concepts that you would find in the training set. Hallucinations allow you to consider alternative options.
Many language models have a “temperature” parameter. You can control the temperature in ChatGPT using the API instead of the web interface. This is a random parameter. A higher temperature can introduce more hallucinations.
Answer: Language models are not databases or search engines. Illusions are inevitable. What irritates me is that the models produce difficult-to-find errors in the text.
If the delusion was brought on by tainted training data, you can clean up the data and retrain the model. Nevertheless, the majority of models are too big to train independently. Using commodity hardware can make it impossible to even fine-tune an established model. If something went horribly wrong, asking the model to regenerate and including humans in the outcome would be the best mitigating measures.
Controlled creation is another way to prevent hallucinations. It entails giving the model sufficient information and limitations in the prompt. As such, the model’s ability to hallucinate is restricted. Prompt engineering is used to define the role and context for the model, guiding the generation and preventing unbounded hallucinations.
Also Read: Top 7 Strategies to Mitigate Hallucinations in LLMs
Answer: Prompt engineering is a practice in the natural language processing field of artificial intelligence in which text describes what the AI demands to do. Guided by this input, the AI generates an output. This output could take different forms, with the intent to use human-understandable text conversationally to communicate with models. Since the task description is embedded in the input, the model performs more flexibly with possibilities.
Answer: Prompts are detailed descriptions of the desired output expected from the model. They are the interaction between a user and the AI model. This should give us a better understanding of what engineering is about.
Answer: The quality of the prompt is critical. There are ways to improve them and get your models to improve outputs. Let’s see some tips below:
Also Read: 17 Prompting Techniques to Supercharge Your LLMs
Answer: Different techniques are used in writing prompts. They are the backbone.
Zero-shot provides a prompt that is not part of the training yet still performing as desired. In a nutshell, LLMs can generalize.
For Example: if the prompt is: Classify the text into neutral, negative, or positive. And the text is: I think the presentation was awesome.
Sentiment:
Output: Positive
The knowledge of the meaning of “sentiment” made the model zero-shot how to classify the question even though it has not been given a bunch of text classifications to work on. There might be a pitfall since no descriptive data is provided in the text. Then we can use few-shot prompting.
In an elementary understanding, the few-shot uses a few examples (shots) of what it must do. This takes some insight from a demonstration to perform. Instead of relying solely on what it is trained on, it builds on the shots available.
CoT allows the model to achieve complex reasoning through middle reasoning steps. It involves creating and improving intermediate steps called “chains of reasoning” to foster better language understanding and outputs. It can be like a hybrid that combines few-shot on more complex tasks.
Answer: Retrieval-Augmented Generation (RAG) is the process of optimizing the output of a large language model, so it references an authoritative knowledge base outside of its training data sources before generating a response. Large Language Models (LLMs) are trained on vast volumes of data and use billions of parameters to generate original output for tasks like answering questions, translating languages, and completing sentences. RAG extends the already powerful capabilities of LLMs to specific domains or an organization’s internal knowledge base, all without the need to retrain the model. It is a cost-effective approach to improving LLM output so it remains relevant, accurate, and useful in various contexts.
Answer: Intelligent chatbots and other applications involving natural language processing (NLP) rely on LLMs as a fundamental artificial intelligence (AI) technique. The objective is to develop bots that, through cross-referencing reliable knowledge sources, can respond to user enquiries in a variety of scenarios. Regretfully, LLM replies become unpredictable due to the nature of LLM technology. LLM training data also introduces a cut-off date on the information it possesses and is stagnant.
Known challenges of LLMs include:
The Large Language Model can be compared to an overzealous new hire who refuses to keep up with current affairs but will always respond to enquiries with complete assurance. Unfortunately, you don’t want your chatbots to adopt such a mindset since it might harm consumer trust!
One method for addressing some of these issues is RAG. It reroutes the LLM to obtain pertinent data from reliable, pre-selected knowledge sources. Users learn how the LLM creates the response, and organizations have more control over the resulting text output.
Answer: RAG Technology in Generative AI Implementation
Answer: An open-source framework called LangChain creates applications based on large language models (LLMs). Large deep learning models known as LLMs are pre-trained on vast amounts of data and can produce answers to user requests, such as generating images from text-based prompts or providing answers to enquiries. To increase the relevance, accuracy, and degree of customisation of the data produced by the models, LangChain offers abstractions and tools. For instance, developers can create new prompt chains or alter pre-existing templates using LangChain components. Additionally, LangChain has parts that let LLMs use fresh data sets without having to retrain.
Answer: LangChain: Enhancing Machine Learning Applications
Answer: A data framework for applications based on Large Language Models (LLMs) is called LlamaIndex. Large-scale public datasets are used to pre-train LLMs like GPT-4, which gives them amazing natural language processing skills right out of the box. Nevertheless, their usefulness is restricted in the absence of your personal information.
Using adaptable data connectors, LlamaIndex enables you to import data from databases, PDFs, APIs, and more. Indexing of this data results in intermediate representations that are LLM-optimized. Afterwards, LlamaIndex enables natural language querying and communication with your data through chat interfaces, query engines, and data agents with LLM capabilities. Your LLMs may access and analyse confidential data on a massive scale with it, all without having to retrain the model using updated data.
Answer: LlamaIndex uses Retrieval-Augmented Generation (RAG) technologies. It combines a private knowledge base with massive language models. The indexing and querying stages are typically its two phases.
During the indexing stage, LlamaIndex will effectively index private data into a vector index. This stage aids in building a domain-specific searchable knowledge base. Text documents, database entries, knowledge graphs, and other kind of data can all be entered.
In essence, indexing transforms the data into numerical embeddings or vectors that represent its semantic content. It permits fast searches for similarities throughout the content.
Based on the user’s question, the RAG pipeline looks for the most pertinent data during querying. The LLM is then provided with this data and the query to generate a correct result.
Through this process, the LLM can obtain up-to-date and relevant material not covered in its first training. At this point, the primary problem is retrieving, organising, and reasoning across potentially many information sources.
Answer: While pre-trained language models are prodigious, they are not inherently experts in any specific task. They may have an incredible grasp of language. Still, they need some LLMs fine-tuning, a process where developers enhance their performance in tasks like sentiment analysis, language translation, or answering questions about specific domains. Fine-tuning large language models is the key to unlocking their full potential and tailoring their capabilities to specific applications
Fine-tuning is like providing a finishing touch to these versatile models. Imagine having a multi-talented friend who excels in various areas, but you need them to master one particular skill for a special occasion. You would give them some specific training in that area, right? That’s precisely what we do with pre-trained language models during fine-tuning.
Also Read: Fine-Tuning Large Language Models
Answer: While pre-trained language models are remarkable, they are not task-specific by default. Fine-tuning large language models is adapting these general-purpose models to perform specialized tasks more accurately and efficiently. When we encounter a specific NLP task like sentiment analysis for customer reviews or question-answering for a particular domain, we need to fine-tune the pre-trained model to understand the nuances of that specific task and domain.
The benefits of fine-tuning are manifold. Firstly, it leverages the knowledge learned during pre-training, saving substantial time and computational resources that would otherwise be required to train a model from scratch. Secondly, fine-tuning allows us to perform better on specific tasks, as the model is now attuned to the intricacies and nuances of the domain it was fine-tuned for.
Answer: Fine-tuning is a technique used in model training, distinct from pre-training, which is the initializing model parameters. Pre-training begins with random initialization of model parameters and occurs iteratively in two phases: forward pass and backpropagation. Conventional supervised learning (SSL) is used for pre-training models for computer vision tasks, such as image classification, object detection, or image segmentation.
LLMs are typically pre-trained through self-supervised learning (SSL), which uses pretext tasks to derive ground truth from unlabeled data. This allows for the use of massively large datasets without the burden of annotating millions or billions of data points, saving labor but requiring large computational resources. Fine-tuning entails techniques to further train a model whose weights have been updated through prior training, tailoring it on a smaller, task-specific dataset. This approach provides the best of both worlds, leveraging the broad knowledge and stability gained from pre-training on a massive set of data and honing the model’s understanding of more detailed concepts.
Answer: Fine-tuning Approaches in Generative AI
Parameter-Efficient Fine-Tuning (PEFT) is a method designed to optimize the fine-tuning process of large-scale pre-trained language models by updating only a small subset of parameters. Traditional fine-tuning requires adjusting millions or even billions of parameters, which is computationally expensive and resource-intensive. PEFT techniques, such as low-rank adaptation (LoRA), adapter modules, or prompt tuning, allow for significant reductions in the number of trainable parameters. These methods introduce additional layers or modify specific parts of the model, enabling fine-tuning with much lower computational costs while still achieving high performance on targeted tasks. This makes fine-tuning more accessible and efficient, particularly for researchers and practitioners with limited computational resources.
Supervised Fine-Tuning (SFT) is a critical process in refining pre-trained language models to perform specific tasks using labelled datasets. Unlike unsupervised learning, which relies on large amounts of unlabelled data, SFT uses datasets where the correct outputs are known, allowing the model to learn the precise mappings from inputs to outputs. This process involves starting with a pre-trained model, which has learned general language features from a vast corpus of text, and then fine-tuning it with task-specific labelled data. This approach leverages the broad knowledge of the pre-trained model while adapting it to excel at particular tasks, such as sentiment analysis, question answering, or named entity recognition. SFT enhances the model’s performance by providing explicit examples of correct outputs, thereby reducing errors and improving accuracy and robustness.
Reinforcement Learning from Human Feedback (RLHF) is an advanced machine learning technique that incorporates human judgment into the training process of reinforcement learning models. Unlike traditional reinforcement learning, which relies on predefined reward signals, RLHF leverages feedback from human evaluators to guide the model’s behavior. This approach is especially useful for complex or subjective tasks where it is challenging to define a reward function programmatically. Human feedback is collected, often by having humans evaluate the model’s outputs and provide scores or preferences. This feedback is then used to update the model’s reward function, aligning it more closely with human values and expectations. The model is fine-tuned based on this updated reward function, iteratively improving its performance according to human-provided criteria. RLHF helps produce models that are technically proficient and aligned with human values and ethical considerations, making them more reliable and trustworthy in real-world applications.
Answer: Parameter efficient fine-tuning (PEFT) is a method that reduces the number of trainable parameters needed to adapt a large pre-trained model to specific downstream applications. PEFT significantly decreases computational resources and memory storage needed to yield an effectively fine-tuned model, making it more stable than full fine-tuning methods, particularly for Natural Language Processing (NLP) use cases.
Partial fine-tuning, also known as selective fine-tuning, aims to reduce computational demands by updating only the select subset of pre-trained parameters most critical to model performance on relevant downstream tasks. The remaining parameters are “frozen,” ensuring they will not be changed. Some partial fine-tuning methods include updating only the layer-wide bias terms of the model and sparse fine-tuning methods that update only a select subset of overall weights throughout the model.
Additive fine-tuning adds extra parameters or layers to the model, freezes the existing pre-trained weights, and trains only those new components. This approach helps retain stability of the model by ensuring that the original pre-trained weights remain unchanged. While this can increase training time, it significantly reduces memory requirements because there are far fewer gradients and optimization states to store. Further memory savings can be achieved through quantization of the frozen model weights.
Adapters inject new, task-specific layers added to the neural network and train these adapter modules in lieu of fine-tuning any of the pre-trained model weights. Reparameterization-based methods like Low Rank Adaptation (LoRA) leverage low-rank transformation of high-dimensional matrices to capture the underlying low-dimensional structure of model weights, greatly reducing the number of trainable parameters. LoRA eschews direct optimization of the matrix of model weights and instead optimizes a matrix of updates to model weights (or delta weights), which is inserted into the model.
Answer: Prompt Engineering: Used when you have a small amount of static data and need quick, straightforward integration without modifying the model. It is suitable for tasks with fixed information and when context windows are sufficient.
Retrieval Augmented Generation (RAG): Ideal when you need the model to generate responses based on dynamic or frequently updated data. Use RAG if the model must provide grounded, citation-based outputs.
Fine-Tuning: Choose this when specific, well-defined tasks require the model to learn from input-output pairs or human feedback. Fine-tuning is beneficial for personalized tasks, classification, or when the model’s behavior needs significant customization.
Answer: SLMs are essentially smaller versions of their LLM counterparts. They have significantly fewer parameters, typically ranging from a few million to a few billion, compared to LLMs with hundreds of billions or even trillions. This differ
Answer: Like LLMs, SLMs are trained on massive datasets of text and code. However, several techniques are employed to achieve their smaller size and efficiency:
Answer: Here are some examples of SLMs:
While SLMs typically have a few hundred million parameters, some larger models with 1-3 billion parameters can also be classified as SLMs because they can still be run on standard GPU hardware. Here are some of the examples of such models:
Answer: One benefit of Small Language Models (SLMs) is that they may be trained on relatively small datasets. Their low size makes deployment on mobile devices easier, and their streamlined structures improve interpretability.
The capacity of SLMs to process data locally is a noteworthy advantage, which makes them especially useful for Internet of Things (IoT) edge devices and businesses subject to strict privacy and security requirements.
However, there is a trade-off when using small language models. SLMs have more limited knowledge bases than their Large Language Model (LLM) counterparts because they were trained on smaller datasets. Furthermore, compared to larger models, their comprehension of language and context is typically more restricted, which could lead to less precise and nuanced responses.
Answer: The idea of the diffusion model is not that old. In the 2015 paper called “Deep Unsupervised Learning using Nonequilibrium Thermodynamics”, the Authors described it like this:
The essential idea, inspired by non-equilibrium statistical physics, is to systematically and slowly destroy structure in a data distribution through an iterative forward diffusion process. We then learn a reverse diffusion process that restores structure in data, yielding a highly flexible and tractable generative model of the data.
The diffusion process is split into forward and reverse diffusion processes. The forward diffusion process turns an image into noise, and the reverse diffusion process is supposed to turn that noise into the image again.
Answer: The forward diffusion process is a Markov chain that starts from the original data x and ends at a noise sample ε. At each step t, the data is corrupted by adding Gaussian noise to it. The noise level increases as t increases until it reaches 1 at the final step T.
Answer: The reverse diffusion process aims to convert pure noise into a clean image by iteratively removing noise. Training a diffusion model is to learn the reverse diffusion process to reconstruct an image from pure noise. If you guys are familiar with GANs, we’re trying to train our generator network, but the only difference is that the diffusion network does an easier job because it doesn’t have to do all the work in one step. Instead, it uses multiple steps to remove noise at a time, which is more efficient and easy to train, as figured out by the authors of this paper.
Answer: The noise schedule is a critical component in diffusion models, determining how noise is added during the forward process and removed during the reverse process. It defines the rate at which information is destroyed and reconstructed, significantly impacting the model’s performance and the quality of generated samples.
A well-designed noise schedule balances the trade-off between generation quality and computational efficiency. Too rapid noise addition can lead to information loss and poor reconstruction, while too slow a schedule can result in unnecessarily long computation times. Advanced techniques like cosine schedules can optimize this process, allowing for faster sampling without sacrificing output quality. The noise schedule also influences the model’s ability to capture different levels of detail, from coarse structures to fine textures, making it a key factor in achieving high-fidelity generations.
Answer: Advanced artificial intelligence (AI) systems known as multimodal large language models (LLMs) can interpret and produce various data types, including text, images, and even audio. These sophisticated models combine natural language processing with computer vision and occasionally audio processing capabilities, unlike standard LLMs that only concentrate on text. Their adaptability enables them to carry out various tasks, including text-to-image generation, cross-modal retrieval, visual question answering, and image captioning.
The primary benefit of multimodal LLMs is their capacity to comprehend and integrate data from diverse sources, offering more context and more thorough findings. The potential of these systems is demonstrated by examples such as DALL-E and GPT-4 (which can process images). Multimodal LLMs do, however, have certain drawbacks, such as the demand for more complicated training data, higher processing costs, and possible ethical issues with synthesizing or modifying multimedia content. Notwithstanding these difficulties, multimodal LLMs mark a substantial advancement in AI’s capacity to engage with and comprehend the universe in methods that more nearly resemble human perception and thought processes.
A. Better handling of long-range dependencies
B. Lower computational cost
C. Smaller model size
D. Easier to interpret
Answer: A. Better handling of long-range dependencies
A. Convolution
B. Recurrence
C. Attention
D. Pooling
Answer: C. Attention
A. To normalize the inputs
B. To provide information about the position of words
C. To reduce overfitting
D. To increase model complexity
Answer: B. To provide information about the position of words
A. They have a fixed vocabulary
B. They are trained on a small amount of data
C. They require significant computational resources
D. They are only suitable for translation tasks
Answer: C. They require significant computational resources
A. VGG16
B. GPT-4
C. ResNet
D. YOLO
Answer: B. GPT-4
A. To reduce their size
B. To adapt them to specific tasks
C. To speed up their training
D. To increase their vocabulary
Answer: B. To adapt them to specific tasks
A. To control the randomness of the model’s output
B. To set the model’s learning rate
C. To initialize the model’s parameters
D. To adjust the model’s input length
Answer: A. To control the randomness of the model’s output
A. Zero-shot prompting
B. Few-shot prompting
C. Both A and B
D. None of the above
Answer: C. Both A and B
A. More deterministic output
B. More creative and diverse output
C. Lower computational cost
D. Reduced model accuracy
Answer: B. More creative and diverse output
A. Faster training times
B. Lower memory usage
C. Improved generation quality by leveraging external information
D. Simpler model architecture
Answer: C. Improved generation quality by leveraging external information
A. To generate the final output
B. To retrieve relevant documents or passages from a database
C. To preprocess the input data
D. To train the language model
Answer: B. To retrieve relevant documents or passages from a database
A. Image classification
B. Text summarization
C. Question answering
D. Speech recognition
Answer: C. Question answering
A. Training from scratch on a new dataset
B. Adjusting the model’s architecture
C. Continuing training on a specific task or dataset
D. Reducing the model’s size
Answer: C. Continuing training on a specific task or dataset
A. It requires less data
B. It requires fewer computational resources
C. It leverages previously learned features
D. All of the above
Answer: D. All of the above
A. Overfitting
B. Underfitting
C. Lack of computational power
D. Limited model size
Answer: A. Overfitting
A. To enhance the stability of training deep neural networks
B. To generate high-quality images from text descriptions
C. To compress large models
D. To improve the speed of natural language processing
Answer: B. To generate high-quality images from text descriptions
A. Reducing the noise in input data
B. Iteratively refining the generated image to remove noise
C. Simplifying the model architecture
D. Increasing the noise to improve generalization
Answer: B. Iteratively refining the generated image to remove noise
A. Image classification
B. Text generation
C. Image generation
D. Speech recognition
Answer: C. Image generation
In this article, we have seen different interview questions on generative AI that can be asked in an interview. Generative AI now spans a lot of industries, from healthcare to entertainment to personal recommendations. With a good understanding of the fundamentals and a strong portfolio, you can extract the full potential of generative AI models. Although the latter comes from practice, I’m sure prepping with these questions will make you thorough for your interview. So, all the very best to you for your upcoming GenAI interview!
Want to learn generative AI in 6 months? Check out our GenAI Roadmap to get there!