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Amazon AWS Certified AI Practitioner Exam Sample Questions (Q46-Q51):

NEW QUESTION # 46
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company needs the LLM to produce more consistent responses to the same input prompt.
Which adjustment to an inference parameter should the company make to meet these requirements?

A. Decrease the length of output tokens
B. Increase the maximum generation length
C. Decrease the temperature value
D. Increase the temperature value

Answer: C

NEW QUESTION # 47
A company built a deep learning model for object detection and deployed the model to production.
Which AI process occurs when the model analyzes a new image to identify objects?

A. Inference
B. Bias correction
C. Model deployment
D. Training

Answer: A

NEW QUESTION # 48
Which term describes the numerical representations of real-world objects and concepts that AI and natural language processing (NLP) models use to improve understanding of textual information?

A. Models
B. Tokens
C. Embeddings
D. Binaries

Answer: C

NEW QUESTION # 49
A company wants to make a chatbot to help customers. The chatbot will help solve technical problems without human intervention. The company chose a foundation model (FM) for the chatbot. The chatbot needs to produce responses that adhere to company tone.
Which solution meets these requirements?

A. Experiment and refine the prompt until the FM produces the desired responses.
B. Set a low limit on the number of tokens the FM can produce.
C. Use batch inferencing to process detailed responses.
D. Define a higher number for the temperature parameter.

Answer: A

NEW QUESTION # 50
How can companies use large language models (LLMs) securely on Amazon Bedrock?

A. Design clear and specific prompts. Configure AWS Identity and Access Management (IAM) roles and policies by using least privilege access.
B. Enable Amazon Bedrock automatic model evaluation jobs.
C. Enable AWS Audit Manager for automatic model evaluation jobs.
D. Use Amazon CloudWatch Logs to make models explainable and to monitor for bias.

Answer: A

NEW QUESTION # 51
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