
Technical Quiz- Round 2
Quiz
•
Engineering
•
University
•
Easy
vinjamuri Kamesh
Used 2+ times
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50 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which AI model architecture, known for its use of an attention mechanism, is the foundation for models like GPT and BERT?
Convolutional Neural Network (CNN)
Recurrent Neural Network (RNN)
Transformer
Generative Adversarial Network (GAN)
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is "Federated Learning"?
A method where a central server collects all user data to train a single massive model.
A machine learning technique that trains an algorithm across multiple decentralized devices holding local data samples, without exchanging the data itself.
A type of reinforcement learning used in robotics.
A technique for compressing large neural networks.
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the context of AI, what does "Explainable AI" (XAI) aim to achieve?
To make AI models 100% accurate.
To allow AI to explain its purpose to other AIs.
To enable human users to understand, trust, and manage the decisions made by AI.
To automatically generate documentation for AI code.
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
A Generative Adversarial Network (GAN) consists of which two competing neural networks?
An Encoder and a Decoder
A Generator and a Discriminator
An Agent and an Environment
A Predictor and a Corrector
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which subfield of AI is concerned with creating agents that can learn optimal actions in an environment to maximize a cumulative reward?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Self-supervised Learning
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary purpose of MLOps (Machine Learning Operations)?
To design new machine learning algorithms.
To sell machine learning models as a service.
To streamline the lifecycle of machine learning models, from development to deployment and maintenance.
To perform mathematical optimization of model parameters.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
"Low-code/No-code" AI platforms are designed to:
Make AI less powerful but easier to build.
Be used only by expert data scientists.
Allow individuals with little to no programming experience to build and deploy AI applications.
Run AI models on low-power hardware.
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