AI Knowledge Review Quiz¶

This quiz covers AI fundamentals, learning paradigms, model architectures, GPUs, MLOps, RAG systems, and the NVIDIA ecosystem.

You have 90 seconds to answer all 10 questions.
Questions are shuffled automatically on each load.

Time Left: 90s

1. Which neural network architecture introduced the attention mechanism?

CNN
RNN / LSTM
Transformer
SVM

2. Which learning paradigm requires labeled data (x, y)?

Supervised Learning
Unsupervised Learning
Reinforcement Learning
Self-Supervised Learning

3. What is the main purpose of convolution layers in a CNN?

Flattening input data
Extracting spatial features
Applying attention
Increasing memory size

4. What is the core purpose of Retrieval-Augmented Generation (RAG)?

Augment LLMs with external knowledge
Train models faster
Compress model weights
Deploy multiple models

5. Which system stores embeddings and enables similarity search?

Vector Database (Milvus, Pinecone)
Object Storage
SQL Database
Redis Cache

6. Which CNCF tool provides an end-to-end MLOps workflow platform?

Apache Airflow
Kubeflow
Apache Spark
ArgoCD

7. Which tool is primarily used for large-scale distributed data processing?

Apache Spark
Kubeflow Pipelines
NVIDIA Triton
NGINX

8. Which NVIDIA component is used for high-performance inference serving?

CUDA Runtime
NVIDIA Triton Inference Server
TensorRT
NVIDIA NGC

9. Why are GPUs widely used for AI training and inference?

Massive parallel computation
Faster serial operations
Lower network latency
Faster disk I/O

10. What is the primary purpose of NVIDIA TensorRT?

Optimize and accelerate inference
Pretrain models
Visualize neural networks
Augment datasets