Generative AI Concepts Quiz - Competitive Exams AWS_AI_Practitioner

Free practice quiz: Generative AI Concepts (Competitive Exams AWS_AI_Practitioner). 73 multiple-choice questions with answers and explanations. Play online, no sign-up needed.

Sample questions

1. What is the primary purpose of a foundation model (FM) in generative AI?

   1. To store relational data for enterprise applications
   2. To perform only one specific task like translation
   3. To replace all traditional machine learning algorithms
   4. To serve as a large, pre-trained model that can be adapted to many downstream tasks
   Show answer

   Answer: D. To serve as a large, pre-trained model that can be adapted to many downstream tasks

2. What is 'context window' in a large language model?

   1. The time limit for model training
   2. The number of users the model can serve concurrently
   3. The maximum amount of text (tokens) the model can consider at once
   4. The screen size of the user interface
   Show answer

   Answer: C. The maximum amount of text (tokens) the model can consider at once

3. Which term describes a model that has been trained on a large, broad dataset and can be adapted to many downstream tasks?

   1. Rule-based system
   2. Expert system
   3. Narrow AI model
   4. Foundation model
   Show answer

   Answer: D. Foundation model

4. What does 'in-context learning' refer to in large language models?

   1. The model's ability to learn from user feedback
   2. The model's ability to learn from examples provided in the prompt without weight updates
   3. The model's ability to learn from its own generated text
   4. The model's ability to learn from external databases
   Show answer

   Answer: B. The model's ability to learn from examples provided in the prompt without weight updates

5. What is the key function of a cross-encoder reranker in a retrieval-augmented generation system?

   1. To fine-tune the generator on user feedback
   2. To generate the initial vector embeddings for all documents
   3. To rescore retrieved passages against the query for better relevance ranking
   4. To compress the model for edge deployment
   Show answer

   Answer: C. To rescore retrieved passages against the query for better relevance ranking

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