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Questions Amazon AI1-C01 Exam & AI1-C01 Study Reference
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Amazon AWS Certified AI Practitioner Exam Sample Questions (Q14-Q19):
NEW QUESTION # 14
A company uses Amazon SageMaker for its ML pipeline in a production environment. The company has large input data sizes up to 1 GB and processing times up to 1 hour. The company needs near real-time latency.
Which SageMaker inference option meets these requirements?
- A. Batch transform
- B. Asynchronous inference
- C. Real-time inference
- D. Serverless inference
Answer: C
NEW QUESTION # 15
A company is building a customer service chatbot. The company wants the chatbot to improve its responses by learning from past interactions and online resources.
Which AI learning strategy provides this self-improvement capability?
- A. Supervised learning with a continuously updated FAQ database
- B. Unsupervised learning to find clusters of similar customer inquiries
- C. Supervised learning with a manually curated dataset of good responses and bad responses
- D. Reinforcement learning with rewards for positive customer feedback
Answer: D
NEW QUESTION # 16
A company wants to assess the costs that are associated with using a large language model (LLM) to generate inferences. The company wants to use Amazon Bedrock to build generative AI applications.
Which factor will drive the inference costs?
- A. Temperature value
- B. Amount of data used to train the LLM
- C. Number of tokens consumed
- D. Total training time
Answer: C
NEW QUESTION # 17
An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question.
Which solution meets these requirements with the LEAST implementation effort?
- A. Add a role description to the prompt context that instructs the model of the age range that the response should target.
- B. Summarize the response text depending on the age of the user so that younger users receive shorter responses.
- C. Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.
- D. Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user.
Answer: A
NEW QUESTION # 18
A company wants to use large language models (LLMs) with Amazon Bedrock to develop a chat interface for the company's product manuals. The manuals are stored as PDF files.
Which solution meets these requirements MOST cost-effectively?
- A. Upload PDF documents to an Amazon Bedrock knowledge base. Use the knowledge base to provide context when users submit prompts to Amazon Bedrock.
- B. Use prompt engineering to add all the PDF files as context to the user prompt when the prompt is submitted to Amazon Bedrock.
- C. Use prompt engineering to add one PDF file as context to the user prompt when the prompt is submitted to Amazon Bedrock.
- D. Use all the PDF documents to fine-tune a model with Amazon Bedrock. Use the fine-tuned model to process user prompts.
Answer: A
NEW QUESTION # 19
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