Scale AI Quiz

Answers and explanations appear as you take the quiz.

1. What core problem does Scale AI primarily solve for AI developers?
   • Supplying high-quality labeled and curated training data
   • Designing computer chips
   • Building web browsers
   • Manufacturing batteries
2. What kind of data did Scale AI focus on labeling when it first started?
   • Music recordings
   • Images for self-driving cars
   • Stock market tickers
   • Weather satellite data
3. What does RLHF, a service Scale provides for LLMs, stand for?
   • Random Library Hash Function
   • Rapid Logic Hardware Framework
   • Reinforcement Learning from Human Feedback
   • Recursive Language Hosting Format
4. Why can expert-generated training examples help improve a model?
   • They guarantee the model will be correct on every future task
   • They remove the need to evaluate the model
   • They replace all training data with synthetic labels
   • They provide task-specific demonstrations or feedback for capabilities not well represented in available general data
5. Which platform does Scale AI identify as part of its specialized GenAI contributor ecosystem?
   • Outlier
   • MLflow Tracking
   • Unity Catalog
   • EnCodec
6. Beyond labeling, what AI safety service does Scale offer?
   • Selling antivirus software
   • Red-teaming and evaluating models for safety and reliability
   • Repairing smartphones
   • Designing video game graphics
7. What kind of judgment data is commonly used in RLHF?
   • Only the model’s parameter file names
   • A list of GPU serial numbers
   • Human comparisons or rankings of model responses
   • Unlabeled images with no task signal
8. What is the purpose of red-teaming an AI model or system?
   • Guarantee that the system can never fail
   • Replace all model training data
   • Measure only GPU temperature
   • Probe it with adversarial or misuse scenarios to identify safety weaknesses