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Amazon AWS Certified AI Practitioner Sample Questions (Q70-Q75):

NEW QUESTION # 70
Which AWS feature records details about ML instance data for governance and reporting?

A. Amazon SageMaker Debugger
B. Amazon SageMaker JumpStart
C. Amazon SageMaker Model Cards
D. Amazon SageMaker Model Monitor

Answer: C

Explanation:
Amazon SageMaker Model Cards provide a centralized and standardized repository for documenting machine learning models. They capture key details such as the model's intended use, training and evaluation datasets, performance metrics, ethical considerations, and other relevant information. This documentation facilitates governance and reporting by ensuring that all stakeholders have access to consistent and comprehensive information about each model. While Amazon SageMaker Debugger is used for real-time debugging and monitoring during training, and Amazon SageMaker Model Monitor tracks deployed models for data and prediction quality, neither offers the comprehensive documentation capabilities of Model Cards. Amazon SageMaker JumpStart provides pre-built models and solutions but does not focus on governance documentation.

NEW QUESTION # 71
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 temperature value
B. Increase the maximum generation length
C. Increase the temperature value
D. Decrease the length of output tokens

Answer: A

Explanation:
The temperature parameter in a large language model (LLM) controls the randomness of the model's output.
A lower temperature value makes the output more deterministic and consistent, meaning that the model is less likely to produce different results for the same input prompt.
* Option A (Correct): "Decrease the temperature value": This is the correct answer because lowering the temperature reduces the randomness of the responses, leading to more consistent outputs for the same input.
* Option B: "Increase the temperature value" is incorrect because it would make the output more random and less consistent.
* Option C: "Decrease the length of output tokens" is incorrect as it does not directly affect the consistency of the responses.
* Option D: "Increase the maximum generation length" is incorrect because this adjustment affects the output length, not the consistency of the model's responses.
AWS AI Practitioner References:
* Understanding Temperature in Generative AI Models: AWS documentation explains that adjusting the temperature parameter affects the model's output randomness, with lower values providing more consistent outputs.

NEW QUESTION # 72
A company wants to use a large language model (LLM) on Amazon Bedrock for sentiment analysis. The company wants to classify the sentiment of text passages as positive or negative.
Which prompt engineering strategy meets these requirements?

A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.
B. Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt.
C. Provide the new text passage with a few examples of unrelated tasks, such as text summarization or question answering.
D. Provide the new text passage to be classified without any additional context or examples.

Answer: A

NEW QUESTION # 73
A company has a database of petabytes of unstructured data from internal sources. The company wants to transform this data into a structured format so that its data scientists can perform machine learning (ML) tasks.
Which service will meet these requirements?

A. AWS Glue
B. Amazon Rekognition
C. Amazon Lex
D. Amazon Kinesis Data Streams

Answer: A

NEW QUESTION # 74
A company wants to use a pre-trained generative AI model to generate content for its marketing campaigns. The company needs to ensure that the generated content aligns with the company's brand voice and messaging requirements.
Which solution meets these requirements?

A. Increase the model's complexity by adding more layers to the model's architecture.
B. Optimize the model's architecture and hyperparameters to improve the model's overall performance.
C. Select a large, diverse dataset to pre-train a new generative model.
D. Create effective prompts that provide clear instructions and context to guide the model's generation.

Answer: D

NEW QUESTION # 75
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