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Amazon AWS Certified AI Practitioner Sample Questions:

1. A company wants to display the total sales for its top-selling products across various retail locations in the past 12 months.
Which AWS solution should the company use to automate the generation of graphs?

A) Amazon Q in Amazon EC2
B) Amazon Q in AWS Chatbot
C) Amazon Q Developer
D) Amazon Q in Amazon QuickSight

2. A company is building a chatbot to improve user experience. The company is using a large language model (LLM) from Amazon Bedrock for intent detection. The company wants to use few-shot learning to improve intent detection accuracy.
Which additional data does the company need to meet these requirements?

A) Pairs of user intents and correct chatbot responses
B) Pairs of user messages and correct user intents
C) Pairs of user messages and correct chatbot responses
D) Pairs of chatbot responses and correct user intents

3. A company wants to classify human genes into 20 categories based on gene characteristics.
The company needs an ML algorithm to document how the inner mechanism of the model affects the output.
Which ML algorithm meets these requirements?

A) Decision trees
B) Logistic regression
C) Linear regression
D) Neural networks

4. Which functionality does Amazon SageMaker Clarify provide?

A) Documents critical details about ML models
B) Identifies potential bias during data preparation
C) Integrates a Retrieval Augmented Generation (RAG) workflow
D) Monitors the quality of ML models in production

5. What are tokens in the context of generative AI models?

A) Tokens are the specific prompts or instructions given to a generative AI model to generate output.
B) Tokens are the mathematical representations of words or concepts used in generative AI models.
C) Tokens are the basic units of input and output that a generative AI model operates on, representing words, subwords, or other linguistic units.
D) Tokens are the pre-trained weights of a generative AI model that are fine-tuned for specific tasks.

Solutions: