Question 1

Which of the following AWS services powers Amazon Q Business?

Options :

A : Amazon Kendra

B : Amazon Q Apps

C : Amazon Bedrock

D : Amazon SageMaker Jumpstart

Answer: C

Question 2

A company is using a pre-trained large language model (LLM) to build a chatbot for product recommendations. The company needs the LLM outputs to be short and written in a specific language. Which solution will align the LLM response quality with the company's expectations?

Options :

A : Adjust the prompt.

B : Choose an LLM of a different size.

C : Increase the temperature.

D : Increase the Top K value.

Answer: A

Question 3

Which of the following are correct regarding model evaluation for Amazon Bedrock? (Select two)

Options :

A : Human model evaluation is valuable for assessing qualitative aspects of the model, whereas, automatic model valuation is valuable for assessing quantitative aspects of the model

B : For human model evaluation, you can use either built-in prompt datasets or your own prompt datasets

C : Human model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

D : Automatic model evaluation is valuable for assessing qualitative aspects of the model, whereas, human model valuation is valuable for assessing quantitative aspects of the model

E : Automatic model evaluation provides model scores that are calculated using various statistical methods such as BERT Score and F1

Answer: A,E

Question 4

A security company is evaluating Amazon Rekognition to enhance its Machine Learning (ML) capabilities. However, the data science team needs to identify scenarios where Amazon Rekognition may not be the most suitable solution. Understanding these limitations will help the team select the right tools for different aspects of their security system.

Given this context, which of the following use cases is NOT the right fit for Amazon Rekognition?

Options :

A : Celebrity recognition

B : Enable multilingual user experiences in your applications

C : Face-based user identity verification

D : Searchable media libraries

Answer: B

Question 5

A company has implemented a chatbot powered by Amazon Bedrock to handle customer inquiries and support requests. While the chatbot is effective at providing automated responses, the company has noticed that some of the replies do not consistently match its desired tone — professional, empathetic, and friendly. To maintain brand consistency and ensure a positive customer experience, the company needs to align the chatbot’s responses with its specific communication style and standards.

Which approach would be most effective for ensuring that the chatbot's responses are consistently aligned with the company's tone and style?

Options :

A : The company should iteratively test and adjust the chatbot prompts to ensure that its outputs consistently reflect the company-s tone and style

B : The company should set a low limit on the number of tokens, which restricts the length of the chatbot-s responses, thereby making the chatbot’s responses consistent

C : The company should use batch inferencing, a method that processes multiple input requests in one go to make the chatbot’s responses consistent

D : The company should adjust the temperature to reduce the randomness and creativity of the chatbot’s responses

Answer: A