Question 1:

What is the primary role of a large language model (LLM) in a generative AI application?

A. Execute SQL queries
B. Generate new content based on input prompts
C. Monitor infrastructure metrics
D. Store user credentials securely

Correct Answer: B
📘 Explanation:
A large language model (LLM) is designed to understand and generate human-like text. Its primary function in generative AI is to create new content such as text, code, summaries, or translations based on the prompt it receives. This makes LLMs the core engine behind many generative AI applications like chatbots, content creation tools, and copilots.

 

Question 2:
What is the primary role of a large language model (LLM) in a generative AI application?

A. Execute SQL queries
B. Generate new content based on input prompts
C. Monitor infrastructure metrics
D. Store user credentials securely

✅ Answer: B. Generate new content based on input prompts
📘 Explanation:
LLMs like GPT or LLaMA are designed to understand and generate human-like text. In generative AI applications, their core function is to produce new content such as text, code, or summaries from given prompts.

Question 3:
 Which technique improves performance by reducing the size of the prompt sent to an LLM?

A. Vectorization
B. Prompt engineering
C. Retrieval-Augmented Generation (RAG)
D. Token truncation

✅ Answer: C. Retrieval-Augmented Generation (RAG)
📘 Explanation:
RAG augments prompts with only the most relevant context retrieved from a knowledge base, which reduces prompt length and enhances the relevance of the response.

Question 4:

In Databricks, which tool allows you to manage and deploy foundation models easily?

A. MLflow
B. Unity Catalog
C. Model Registry
D. Model Serving

Correct Answer: D
📘 Explanation:
Databricks’ Model Serving feature enables users to deploy and manage foundation models seamlessly. It allows for easy integration of models into applications through REST APIs, making it an ideal solution for managing and scaling model inference.

Question 5:

Which of the following describes “prompt engineering”?

A. Building a model from scratch
B. Selecting GPUs for model training
C. Designing effective input prompts to guide model behavior
D. Measuring model latency

Correct Answer: C
📘 Explanation:
Prompt engineering involves crafting the right input prompts that guide the behavior of a model like an LLM. By carefully designing prompts, you can extract more accurate and useful responses from the model without needing to retrain it.

Question 6:

What does vector embedding enable in generative AI systems?

A. Image generation
B. Text translation
C. Similarity search
D. Encryption

Correct Answer: C
📘 Explanation:
Vector embeddings represent data (such as text or images) in a multi-dimensional space. This allows for similarity search, where the model can retrieve information based on semantic relevance rather than exact matching, enhancing the AI’s ability to provide contextually accurate responses.

Question 7:

What is the main function of Unity Catalog in Databricks?

A. Run real-time inference
B. Manage and govern data access and lineage
C. Generate AI-based data visualizations
D. Optimize compute clusters

Correct Answer: B

Question 8:

Which metric is commonly used to evaluate text generation quality?

A. ROC-AUC
B. BLEU
C. MAE
D. R²

Correct Answer: B
📘 Explanation:
The BLEU score (Bilingual Evaluation Understudy) is widely used for evaluating the quality of text generated by machine models. It compares the machine-generated text to a reference text, measuring how similar they are based on precision of n-grams.

Question 9:

What is one advantage of using Databricks-hosted foundation models over external APIs?

A. Lower latency and cost
B. Higher token limits
C. Built-in data anonymization
D. No internet required

Correct Answer: A
📘 Explanation:
Hosting models within Databricks reduces reliance on external APIs, resulting in lower latency and costs. By hosting models internally, you can also avoid the overhead of making frequent API calls, improving both efficiency and performance.

Question 10:

Why is vector search used in Retrieval-Augmented Generation (RAG)?

A. To encrypt user inputs
B. To select the fastest LLM
C. To retrieve semantically relevant documents
D. To rank token frequency

Correct Answer: C
📘 Explanation:
Vector search is used in RAG to retrieve semantically relevant documents from a large dataset. By transforming text into vectors (numerical representations), RAG can find the most contextually appropriate pieces of information to enhance the generative model’s output.

Question 11:

Which technique is commonly used to ensure fairness in generative AI systems?

A. Data augmentation
B. Bias mitigation strategies
C. Token pruning
D. Model ensembling

Correct Answer: B
📘 Explanation:
Bias mitigation strategies are employed to identify and reduce biases in generative AI models. These techniques ensure the generated content is fair and does not reflect harmful stereotypes, promoting ethical AI practices.

Question 12:

What is the purpose of using transfer learning in generative AI?

A. To decrease the size of a model
B. To adapt a model trained on one task to another related task
C. To generate random outputs
D. To improve model accuracy on training data

Correct Answer: B
📘 Explanation:
Transfer learning allows a pre-trained model, such as an LLM, to be adapted for a new, related task. This significantly reduces training time and resources by leveraging knowledge from previous tasks instead of starting from scratch.

Question 13:

In Databricks, which service helps monitor model performance during deployment?

A. Model Registry
B. MLflow
C. Delta Lake
D. Unity Catalog

Correct Answer: B
📘 Explanation:
MLflow in Databricks is used to track, manage, and monitor model performance during deployment. It allows users to log metrics, visualize results, and compare different model versions to ensure the deployed model is performing as expected.

Question 14:

What is the role of a generative adversarial network (GAN) in generative AI?

A. To optimize the model training speed
B. To generate synthetic data by using two competing networks
C. To create embeddings for text data
D. To analyze and summarize large datasets

Correct Answer: B
📘 Explanation:
A Generative Adversarial Network (GAN) consists of two neural networks: a generator and a discriminator. The generator creates synthetic data, and the discriminator evaluates it. The two networks compete, leading to improved data generation over time. This technique is widely used in image generation, deepfakes, and other creative applications.

Question 15:

Which of the following is a key challenge when deploying generative AI models at scale?

A. Overfitting to training data
B. Managing large-scale model serving infrastructure
C. Lack of training data
D. Minimizing model size

Correct Answer: B
📘 Explanation:
When deploying generative AI models at scale, a primary challenge is managing the infrastructure needed for model serving. As the model’s user base grows, it requires more computational power, storage, and optimization for efficient performance across multiple instances.

Question 16:

Which method can be used to improve the efficiency of large-scale generative AI models?

A. Early stopping
B. Knowledge distillation
C. Hyperparameter tuning
D. Cross-validation

Correct Answer: B
📘 Explanation:
Knowledge distillation involves transferring knowledge from a large, complex model (teacher) to a smaller, more efficient model (student). This helps improve the efficiency of generative AI models without sacrificing much accuracy, making them easier to deploy and run at scale.

Question 17:

What does the term “fine-tuning” refer to in the context of generative AI?

A. Training a model from scratch on a new dataset
B. Adjusting the weights of a pre-trained model to perform better on a specific task
C. Optimizing a model for speed
D. Adjusting hyperparameters to improve accuracy

Correct Answer: B
📘 Explanation:
Fine-tuning is the process of adjusting the parameters (weights) of a pre-trained model to adapt it to a specific task or dataset. It requires less data and computation than training a model from scratch and can lead to better performance for specialized tasks.

Question 18:

Which of the following is a potential ethical concern with generative AI models?

A. Slow processing time
B. Lack of human oversight
C. Model overfitting
D. Inability to scale

Correct Answer: B
📘 Explanation:
A key ethical concern with generative AI is the lack of human oversight. Since these models can generate content autonomously, without proper monitoring, they may produce biased, misleading, or harmful outputs. Ethical guidelines and oversight are essential to ensure responsible use of generative AI.

Question 19:

In Databricks, which feature enables teams to collaborate on model development and tracking?

A. Model Registry
B. Unity Catalog
C. Collaborative Notebooks
D. Delta Lake

Correct Answer: C
📘 Explanation:
Collaborative Notebooks in Databricks allow data scientists and engineers to work together in real-time on model development, testing, and tracking. Notebooks support code execution, markdown annotations, and visualizations, enhancing team collaboration on AI projects.

Question 20:

What is the main purpose of a foundation model in generative AI?

A. To generate content for specific domains like healthcare or finance
B. To provide a general-purpose model that can be fine-tuned for various tasks
C. To analyze and summarize large datasets
D. To optimize training time for generative models

Correct Answer: B
📘 Explanation:
A foundation model is a large, pre-trained model designed for general tasks. It provides a solid base that can be fine-tuned for specific applications. Foundation models are often used in generative AI, such as language models, that can be adapted for diverse use cases across industries.

Question 21:

What is a key advantage of using reinforcement learning in generative AI?

A. It enables the model to learn from experience and feedback
B. It allows for fast data processing
C. It improves model interpretability
D. It generates deterministic outputs

Correct Answer: A
📘 Explanation:
Reinforcement learning (RL) allows the model to learn by interacting with an environment and receiving feedback (rewards or penalties). This makes RL valuable for tasks requiring sequential decision-making, such as game-playing or generating adaptive content.

Question 22:

Which approach is commonly used to evaluate the performance of a generative AI model in text generation?

A. Confusion matrix
B. BLEU score
C. ROC curve
D. F1-score

Correct Answer: B
📘 Explanation:
The BLEU (Bilingual Evaluation Understudy) score is widely used to assess the quality of text generated by AI models. It compares the n-grams in the generated text to those in a reference text, rewarding matches to reflect the model’s accuracy in generating meaningful text.

Question 23:

Which technique in generative AI helps in generating text based on a given context?

A. Embedding-based search
B. Text summarization
C. Conditional generation
D. Transfer learning

Correct Answer: C
📘 Explanation:
Conditional generation involves generating text based on a given context or input, often used in tasks like machine translation or text-based dialogue systems. The model uses the provided context to guide its output generation.

Question 24:

What does the term “zero-shot learning” refer to in the context of generative AI?

A. A model that can generate outputs without any training data
B. A model that performs a task without requiring task-specific data
C. A model that requires large amounts of labeled data to perform well
D. A model trained to perform only one task

Correct Answer: B
📘 Explanation:
Zero-shot learning allows a model to make predictions or generate content for tasks it has never seen during training. It leverages its general knowledge and is typically applied in NLP tasks where the model generates answers to unseen questions.

Question 25:

What is the primary challenge when using GANs (Generative Adversarial Networks) for image generation?

A. Managing computational cost
B. Ensuring diversity in generated images
C. Handling large datasets
D. Controlling the training process to avoid mode collapse

Correct Answer: D
📘 Explanation:
A key challenge in training GANs is avoiding mode collapse, where the generator produces only a limited set of outputs, reducing the diversity and quality of generated images. Proper training techniques and balance between the generator and discriminator are critical.

Question 26:

Which of the following best describes the function of the discriminator in a GAN?

A. To create new images or content
B. To evaluate the quality of the generated content
C. To increase the training speed
D. To reduce model complexity

Correct Answer: B
📘 Explanation:
In a Generative Adversarial Network (GAN), the discriminator evaluates whether the content produced by the generator is real or fake, helping to guide the generator’s learning process.

Question 27:

Which of the following is a key benefit of using Databricks for model training and deployment?

A. Centralized data management with Unity Catalog
B. Direct access to all cloud provider APIs
C. Built-in deployment of custom hardware
D. Automatic model optimization for all frameworks

Correct Answer: A
📘 Explanation:
Databricks offers centralized data management with Unity Catalog, which ensures consistent governance, access control, and lineage tracking across all workspaces, making it an efficient platform for managing large datasets and AI models.

Question 28:

What is the main advantage of using pre-trained models for generative AI tasks?

A. Reduced computational cost and training time
B. Increased model complexity
C. Higher model accuracy
D. Easier model interpretation

Correct Answer: A
📘 Explanation:
Pre-trained models allow you to leverage the knowledge learned from large datasets, significantly reducing both the computational cost and training time required to fine-tune the model for specific tasks.

Question 29:

In which scenario is Retrieval-Augmented Generation (RAG) most beneficial?

A. Generating text from scratch without context
B. Enhancing text generation with context from external documents
C. Summarizing large documents
D. Predicting future data trends

Correct Answer: B
📘 Explanation:
Retrieval-Augmented Generation (RAG) is particularly beneficial when the model needs to generate responses based on external context. It retrieves relevant information from a knowledge base to inform the generation process, improving the accuracy and relevance of the generated text.

Question 30:

What is a common application of generative AI in the creative industry?

A. Automated data encryption
B. Content generation for blogs and social media
C. Image recognition for facial features
D. Network traffic analysis

Correct Answer: B
📘 Explanation:
Generative AI is widely used in the creative industry to generate content like articles, social media posts, music, and artwork. It automates repetitive content creation tasks, enabling faster and more efficient creative workflows.

Question 31:

Which model is widely used for text-based generative AI tasks, such as language translation and question answering?

A. ResNet
B. GPT-3
C. BERT
D. GAN

Correct Answer: B
📘 Explanation:
GPT-3 (Generative Pre-trained Transformer 3) is one of the most advanced models for text-based generative AI tasks, including language translation, question answering, and text generation. Its large scale allows it to produce highly coherent and contextually relevant outputs.

Question 32:

What is the main function of a tokenizer in generative AI?

A. To translate text into numerical data for processing by the model
B. To evaluate the model’s output
C. To categorize tokens into predefined classes
D. To generate model predictions

Correct Answer: A
📘 Explanation:
A tokenizer converts text into smaller units called tokens (such as words or subwords). These tokens are then transformed into numerical representations that the generative model can process to generate output.

Question 33:

What is the main goal of adversarial training in generative AI?

A. To make the model more interpretable
B. To help the model generate more realistic content by using a discriminator
C. To improve the speed of model inference
D. To reduce the training time of a model

Correct Answer: B
📘 Explanation:
Adversarial training is used to improve a model’s ability to generate realistic content by involving two networks: a generator and a discriminator. The generator creates content, and the discriminator evaluates it, creating a dynamic training process that helps the model improve its output.

Question 34:

What does the term “model drift” refer to in generative AI?

A. Changes in model performance over time due to evolving data distributions
B. Decreasing model accuracy during training
C. A model’s ability to adapt to different data types
D. A model’s speed in generating output

Correct Answer: A
📘 Explanation:
Model drift occurs when the model’s performance degrades over time due to changes in the data distribution. This often happens in real-world applications where the underlying data evolves, causing previously trained models to become less effective.

Question 35:

What is the primary use of embeddings in generative AI?

A. To transform data into high-dimensional vector space for efficient similarity search
B. To measure the accuracy of model outputs
C. To optimize the training speed of models
D. To visualize data in 2D space

Correct Answer: A
📘 Explanation:
Embeddings are used to convert text, images, or other data types into high-dimensional vectors, allowing for efficient similarity searches. These vectors represent the semantic meaning of the data, making it easier to perform tasks like clustering, classification, and search.

Question 36:

What is the main function of a prompt in generative AI?

A. To define the data structure
B. To guide the model’s output based on a specific input
C. To collect feedback from users
D. To monitor model performance

Correct Answer: B
📘 Explanation:
A prompt is the input provided to a generative model to guide its output. The model’s response depends heavily on how the prompt is structured, making prompt design a critical part of achieving desired outcomes in generative AI applications.

Question 37:

What is one challenge when using unsupervised learning for generative AI?

A. Lack of labeled data for training
B. Difficulty in managing large-scale data
C. Slow processing time for data generation
D. Higher risk of overfitting

Correct Answer: A
📘 Explanation:
In unsupervised learning, models are trained without labeled data. The challenge lies in the lack of clear guidance, requiring the model to learn patterns and structures from raw, unstructured data, which can be more difficult and time-consuming than supervised learning.

Question 41:

Which method is often used to ensure the generalization of generative AI models?

A. Overfitting
B. Regularization techniques
C. Increasing model complexity
D. Data augmentation

Correct Answer: B
📘 Explanation:
Regularization techniques help prevent overfitting and ensure that the model generalizes well to unseen data. These techniques impose penalties on overly complex models, promoting simpler, more robust learning.

Question 42:

In Databricks, what does Delta Lake provide for data management?

A. Real-time model training capabilities
B. High-performance query execution
C. Optimized data storage and version control
D. Centralized model governance

Correct Answer: C
📘 Explanation:
Delta Lake offers optimized data storage with ACID transaction support, allowing for version control and high-performance querying. It ensures data reliability, consistency, and efficiency across large-scale machine learning and data engineering workflows.

Question 43:

Which of the following best describes a pre-trained model?

A. A model that is built from scratch for a specific task
B. A model that has been trained on large datasets for general tasks and can be fine-tuned
C. A model that is not capable of learning
D. A model that only works with labeled data

Correct Answer: B
📘 Explanation:
A pre-trained model is trained on large datasets for general tasks and can be fine-tuned for specific use cases. It leverages prior knowledge to perform well across a variety of applications, making it efficient and versatile.

Question 44:

What is the primary goal of prompt engineering in generative AI?

A. To optimize the speed of the model
B. To design effective inputs that guide the model’s output
C. To create new machine learning algorithms
D. To evaluate the model’s performance

Correct Answer: B
📘 Explanation:
Prompt engineering involves designing the input prompts to guide the model’s response effectively. By crafting precise and well-structured prompts, users can achieve more accurate, contextually relevant outputs from generative models.

Question 45:

Which of the following is a potential application of generative AI in the healthcare industry?

A. Predicting patient behavior
B. Generating synthetic medical images for research
C. Tracking medical inventory
D. Predicting disease outbreaks

Correct Answer: B
📘 Explanation:
In healthcare, generative AI is used to create synthetic medical images for training and research purposes. It helps simulate realistic medical conditions, facilitating the development of diagnostic tools without the need for real patient data.

Question 46:

What is the key difference between supervised learning and unsupervised learning in generative AI?

A. Supervised learning uses labeled data, while unsupervised learning uses unlabeled data
B. Unsupervised learning requires more computation
C. Supervised learning is faster than unsupervised learning
D. Unsupervised learning always leads to better model performance

Correct Answer: A
📘 Explanation:
Supervised learning relies on labeled data to guide the model’s learning process, while unsupervised learning uses unlabeled data to identify patterns or clusters within the data without predefined labels

Question 47:

Which of the following is one of the primary challenges when scaling generative AI models?

A. Limited training data
B. Difficulty in obtaining labeled data
C. Efficient deployment and model serving
D. Lack of computational resources

Correct Answer: C
📘 Explanation:
As generative AI models become more complex, the primary challenge is efficient deployment and model serving. Managing the infrastructure to handle large-scale model inference in real-time requires careful resource allocation and optimization.

Question 48:

Which technique in generative AI is used to control the randomness of output generation?

A. Regularization
B. Sampling strategies
C. Model compression
D. Feature selection

Correct Answer: B
📘 Explanation:
Sampling strategies such as temperature, top-k sampling, and nucleus sampling are used to control the randomness of the output generation in models like GPT. These techniques adjust the likelihood of generating specific tokens or sequences, allowing for more creative or deterministic outputs.

Question 49:

In a GAN, what is the role of the generator?

A. To distinguish between real and fake data
B. To create synthetic data that resembles real data
C. To evaluate the quality of the generated content
D. To optimize model training speed

Correct Answer: B
📘 Explanation:
The generator in a GAN creates synthetic data (such as images, text, or audio) that resembles real data. Its goal is to fool the discriminator into thinking that the generated content is real, thus improving its quality over time.

Question 50:

What is one major advantage of using transformer models for generative AI tasks?

A. They are faster to train than traditional models
B. They can handle long-range dependencies in data efficiently
C. They require less data to achieve high accuracy
D. They produce deterministic outputs

Correct Answer: B
📘 Explanation:
Transformer models excel at handling long-range dependencies in data, making them ideal for tasks like text generation, language translation, and summarization. Their attention mechanism allows them to focus on relevant parts of the input data, regardless of sequence length.

Question 51:

In Databricks, which feature is used to track the parameters, metrics, and artifacts of machine learning experiments?

A. Unity Catalog
B. Delta Lake
C. MLflow
D. Model Registry

Correct Answer: C
📘 Explanation:
MLflow in Databricks is a tool for tracking machine learning experiments. It enables users to log and compare model parameters, metrics, and artifacts, making it easier to manage and optimize model development.

Question 52:

Which of the following best describes the process of model pruning?

A. Reducing the size of the training dataset
B. Simplifying the model by removing unnecessary parameters
C. Adding more layers to the neural network
D. Increasing the complexity of the model for better performance

Correct Answer: B
📘 Explanation:
Model pruning involves removing unnecessary parameters or weights from a model, making it smaller and more efficient without significantly affecting performance. This technique helps reduce the computational cost during deployment.

Question 53:

What does the term “latent space” refer to in generative models?

A. A hidden layer in a neural network
B. The space of potential outputs generated by the model
C. The input data used for model training
D. The loss function used during training

Correct Answer: B
📘 Explanation:
Latent space refers to the space of potential outputs that a generative model can produce. It represents a compressed or abstract representation of data that the model uses to generate diverse, new content.

Question 54:

Which generative model is most commonly used for text-based tasks in natural language processing (NLP)?

A. GAN
B. Transformer
C. CNN
D. RNN

Correct Answer: B
📘 Explanation:
Transformer models are the most commonly used for text-based tasks in NLP due to their ability to handle long-range dependencies and their efficiency in parallel processing. Popular models like GPT and BERT are based on the transformer architecture.

Question 55:

What is a primary benefit of using data augmentation in generative AI?

A. To increase the amount of labeled data
B. To prevent model overfitting
C. To speed up the training process
D. To reduce the size of the dataset

Correct Answer: B
📘 Explanation:
Data augmentation involves creating new data from the existing dataset by applying transformations such as rotations, translations, or changes in color. This helps prevent overfitting by artificially increasing the variety of data the model is trained on.

Question 56:

Which of the following is an advantage of using a foundation model for generative AI tasks?

A. It requires little to no fine-tuning
B. It is designed for a specific domain
C. It is general-purpose and can be adapted to a variety of tasks
D. It always provides deterministic outputs

Correct Answer: C
📘 Explanation:
Foundation models are general-purpose models trained on large datasets and can be adapted to a variety of tasks. Fine-tuning them for specific applications often requires fewer resources compared to training a model from scratch.