AWS Certified AI Practitioner (AIF-C01)

The AWS Certified AI Practitioner (AIF-C01) were last updated on today.

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Topic 1 - Exam A

Question #16 Topic 1

A company is developing an ML model to predict customer churn. The model performs well on the training dataset but does not accurately predict churn for new data. Which solution will resolve this issue?

A Decrease the regularization parameter to increase model complexity.
B Increase the regularization parameter to decrease model complexity.
C Add more features to the input data.
D Train the model for more epochs.

Suggested Answer: B
NOTE: This is a multiple choice question because it states a choice of four options (A, B, C, and D). The correct answer is B because increasing the regularization parameter can help decrease model complexity and reduce overfitting, which appears to be the issue described in the review text.

Question #17 Topic 1

A company wants to use a large language model (LLM) to generate concise, feature-specific descriptions for the company’s products. Which prompt engineering technique meets these requirements?

A Create one prompt that covers all products. Edit the responses to make the responses more specific, concise, and tailored to each product.
B Create prompts for each product category that highlight the key features. Include the desired output format and length for each prompt response.
C Include a diverse range of product features in each prompt to generate creative and unique descriptions.
D Provide detailed, product-specific prompts to ensure precise and customized descriptions.

Suggested Answer: B
NOTE: -

Question #18 Topic 1

A company notices that its foundation model (FM) generates images that are unrelated to the prompts. The company wants to modify the prompt techniques to decrease unrelated images. Which solution meets these requirements?

A Use zero-shot prompts.
B Use negative prompts.
C Use positive prompts.
D Use ambiguous prompts.

Suggested Answer: B
NOTE: 选择'B'是因为使用负面提示（negative prompts）可以帮助模型理解不应该生成哪些内容，从而减少与提示无关的图像。相比之下，正面提示（positive prompts）可能会引入更多不相关的图像，零次学习提示（zero-shot prompts）和模糊提示（ambiguous prompts）则可能不会提供足够的指导来明确地调整模型生成的内容。

Question #19 Topic 1

A company is introducing a mobile app that helps users learn foreign languages. The app makes text more coherent by calling a large language model (LLM). The company collected a diverse dataset of text and supplemented the dataset with examples of more readable versions. The company wants the LLM output to resemble the provided examples. Which metric should the company use to assess whether the LLM meets these requirements?

A Value of the loss function
B Semantic robustness
C Recall-Oriented Understudy for Gisting Evaluation (ROUGE) score
D Latency of the text generation

Suggested Answer: C
NOTE: 在给定的文本中，公司需要确保语言模型（LLM）的输出与提供的示例相似。在这种情况下，ROUGE分数是一种常用的评估指标，用于衡量生成文本与参考文本之间的相似性。ROUGE分数通常用于自动评估文本摘要的质量，但也适用于衡量文本生成任务中的输出质量。

Question #20 Topic 1

A company wants to use large language models (LLMs) to produce code from natural language code comments. Which LLM feature meets these requirements?

A Text summarization
B Text generation
C Text completion
D Text classification

Suggested Answer: B
NOTE: The question asks which feature of large language models (LLMs) can be used to produce code from natural language code comments. Among the options, text generation is the most suitable feature for this task as it involves generating new text based on the input, which in this case would be converting natural language into code.