Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question - Page 2

Question 4 (Topic 1)

Which option is a use case for generative AI models?

Select an option, then click Submit answer.

○ Improving network security by using intrusion detection systems
○ Creating photorealistic images from text descriptions for digital marketing
○ Enhancing database performance by using optimized indexing
○ Analyzing financial data to forecast stock market trends

Reference / correct answer:

Creating photorealistic images from text descriptions for digital marketing

Most accepted answer: B. Creating photorealistic images from text descriptions for digital marketing

Community votes: B=9, D=1

Selected Answer: B B is the correct answer. upvoted 1 times

Selected Answer: B Generative AI models are designed to generate new content based on input data. A common use case is generating photorealistic images from text descriptions, often used in digital marketing, content creation, and design. Models like DALL·E, Stable Diffusion, or MidJourney can create high-quality images based on user prompts. upvoted 1 times

Selected Answer: B Creating photorealistic images from text descriptions for digital marketing: Generative AI models, such as generative adversarial networks (GANs) and diffusion models, are capable of generating new content (like images, text, and audio) based on input data. In this case, a generative model can create photorealistic images from text descriptions, which is widely used in digital marketing and content creation. upvoted 4 times

Selected Answer: B Generative AI is used to generate new content, images, video, and audio from existing content or new inputs from various data sources. upvoted 1 times

Question 5 (Topic 1)

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?

Select an option, then click Submit answer.

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

Reference / correct answer:

Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Most accepted answer: A. Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified.

Community votes: A=7

Selected Answer: A A: Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified. Explanation: This strategy is known as few-shot prompting, where the prompt includes a few examples of labeled data (text passages with positive or negative sentiment) before asking the model to classify the new text passage. This helps the large language model (LLM) understand the task and align its output with the desired format. Why not the other options? B: Provide a detailed explanation of sentiment analysis and how LLMs work in the prompt: Explaining the concept of sentiment analysis is unnecessary for the model, as it does not improve the model's ability to classify text. C: Provide the new text passage to be classified without any additional context or examples: Without examples, the LLM might not correctly infer the task or format of the output, leading to inconsistent or incorrect results. upvoted 5 times

Selected Answer: A A is the correct answer upvoted 2 times

Selected Answer: A This approach is known as few-shot prompting, where you include a few labeled examples to guide the model on how to classify sentiment. upvoted 2 times

Selected Answer: A Provide examples of text passages with corresponding positive or negative labels in the prompt followed by the new text passage to be classified: This approach uses few-shot learning, where you give the model clear examples of text passages labeled with sentiment (positive or negative). By providing these examples in the prompt, the model is better able to understand the task and generalize it to classify the new text passage correctly. This is a common and effective strategy for tasks like sentiment analysis. upvoted 4 times

Selected Answer: A Set the proper label with a few examples to the prompts upvoted 1 times

Question 6 (Topic 1)

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?

Select an option, then click Submit answer.

○ Supervised learning with a manually curated dataset of good responses and bad responses
○ Reinforcement learning with rewards for positive customer feedback
○ Unsupervised learning to find clusters of similar customer inquiries
○ Supervised learning with a continuously updated FAQ database

Reference / correct answer:

Reinforcement learning with rewards for positive customer feedback

Most accepted answer: B. Reinforcement learning with rewards for positive customer feedback

Community votes: A=1, B=4

Selected Answer: B B is the correct answer upvoted 1 times

Selected Answer: A A is the only option that allows you to use both past interactions and ALSO online resources, which reinforcement training won't touch at all. upvoted 1 times

Selected Answer: B B. Reinforcement learning with rewards for positive customer feedback is the strategy that provides self-improvement capability. In reinforcement learning (RL), an agent (in this case, the chatbot) learns by interacting with its environment and receiving feedback (rewards or penalties). The chatbot can improve its performance over time by adjusting its responses based on positive feedback from users. This allows it to "learn" from past interactions and improve autonomously. upvoted 4 times

Selected Answer: B Reinforcement learning: is the most suitable strategy for a chatbot to continuously improve its responses based on real-time feedback from users. The chatbot can "learn" by receiving positive reinforcement (reward) when it provides a helpful response and negative reinforcement when it doesn't, allowing it to adjust its responses over time to better suit customer needs. Why other options are not suitable: A. While this can provide a good initial training set, it wouldn't allow the chatbot to adapt to new situations or customer feedback without manual intervention. C. This can be helpful in understanding customer patterns but wouldn't directly improve the chatbot's responses without additional training data or feedback mechanisms. D. While updating the FAQ database can be beneficial, it still requires manual effort and wouldn't enable the chatbot to learn from real-time interactions with customers in the same way that reinforcement learning does. upvoted 3 times

Selected Answer: B Reinforcement learning: This method allows the chatbot to learn from the outcomes of its actions, essentially receiving "rewards" for positive customer feedback and adjusting its responses accordingly to maximize those rewards in the future. upvoted 2 times

Free AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 – Amazon AWS-CERTIFIED-AI-PRACTITIONER-AIF-C01 Practice Test Question