🚀 Generative AI Interview Questions with Answers (Part 5)
2️⃣1️⃣ What is Model Quantization?
👉 Model Quantization is a technique that reduces the precision of a model's numerical parameters, which can make the model smaller and faster to run.
Example:
Benefits:
🔹 Lower memory usage
🔹 Faster inference
🔹 Easier deployment on limited hardware
💡 Quantization can involve trade-offs between efficiency and model quality.
2️⃣2️⃣ What is Knowledge Distillation?
👉 Knowledge Distillation is a technique where a smaller student model learns from a larger teacher model.
📌 Basic process:
Benefits:
🔹 Smaller model size
🔹 Faster inference
🔹 Lower computational requirements
💡 It is often used to create more efficient models.
2️⃣3️⃣ What is a Foundation Model?
👉 A Foundation Model is a large, broadly trained AI model that can be adapted to perform many different tasks.
Examples of tasks:
🔹 Text Generation
🔹 Classification
🔹 Summarization
🔹 Question Answering
🔹 Code Generation
📌 Large-Scale Pretraining → Foundation Model → Adaptation → Applications
2️⃣4️⃣ What is Multimodal Generative AI?
👉 Multimodal Generative AI can work with multiple types of information, such as text, images, audio, and video.
Example:
Applications:
🖼 Image Understanding
🎙 Voice Interaction
📄 Document Analysis
🎬 Video Understanding
💻 Code Assistance
2️⃣5️⃣ What is AI Model Deployment?
👉 AI Model Deployment is the process of making a trained AI model available for real-world use through an application, API, cloud service, or device.
📌 Typical workflow:
Deployment may involve:
🔹 APIs
🔹 Cloud Platforms
🔹 Web Applications
🔹 Mobile Applications
🔹 Edge Devices
💡 After deployment, models may need monitoring for performance, reliability, and changes in real-world data.
💬 Save this for your Generative AI interview preparation!
🔥 Next Part will cover 5 questions on AI APIs, Model Serving, Vector Search, Semantic Search & AI Pipelines.
#GenerativeAI #GenAI #AI #LLM #FoundationModel #MultimodalAI #AIModel #ModelDeployment #AIInterview #InterviewQuestions
2️⃣1️⃣ What is Model Quantization?
👉 Model Quantization is a technique that reduces the precision of a model's numerical parameters, which can make the model smaller and faster to run.
Example:
FP32 Model
↓
Quantization
↓
INT8 / Lower-Precision Model
Benefits:
🔹 Lower memory usage
🔹 Faster inference
🔹 Easier deployment on limited hardware
💡 Quantization can involve trade-offs between efficiency and model quality.
2️⃣2️⃣ What is Knowledge Distillation?
👉 Knowledge Distillation is a technique where a smaller student model learns from a larger teacher model.
📌 Basic process:
Large Teacher Model
↓
Knowledge / Soft Targets
↓
Smaller Student Model
Benefits:
🔹 Smaller model size
🔹 Faster inference
🔹 Lower computational requirements
💡 It is often used to create more efficient models.
2️⃣3️⃣ What is a Foundation Model?
👉 A Foundation Model is a large, broadly trained AI model that can be adapted to perform many different tasks.
Examples of tasks:
🔹 Text Generation
🔹 Classification
🔹 Summarization
🔹 Question Answering
🔹 Code Generation
📌 Large-Scale Pretraining → Foundation Model → Adaptation → Applications
2️⃣4️⃣ What is Multimodal Generative AI?
👉 Multimodal Generative AI can work with multiple types of information, such as text, images, audio, and video.
Example:
Image + Text Prompt
↓
AI Model
↓
Text Response
Applications:
🖼 Image Understanding
🎙 Voice Interaction
📄 Document Analysis
🎬 Video Understanding
💻 Code Assistance
2️⃣5️⃣ What is AI Model Deployment?
👉 AI Model Deployment is the process of making a trained AI model available for real-world use through an application, API, cloud service, or device.
📌 Typical workflow:
Train Model
↓
Evaluate Model
↓
Optimize Model
↓
Deploy
↓
Monitor
Deployment may involve:
🔹 APIs
🔹 Cloud Platforms
🔹 Web Applications
🔹 Mobile Applications
🔹 Edge Devices
💡 After deployment, models may need monitoring for performance, reliability, and changes in real-world data.
💬 Save this for your Generative AI interview preparation!
🔥 Next Part will cover 5 questions on AI APIs, Model Serving, Vector Search, Semantic Search & AI Pipelines.
#GenerativeAI #GenAI #AI #LLM #FoundationModel #MultimodalAI #AIModel #ModelDeployment #AIInterview #InterviewQuestions