Free Amazon AIF-C01 Exam Questions

Question 1

A retail company is exploring the use of generative AI to improve its customer experience by personalizing recommendations and automating product descriptions. The company is interested in leveraging pre-built, high-performing Foundation Models (FMs) to accelerate development but also needs the ability to customize these models with its proprietary data to meet specific business needs. The company is seeking an AWS service that not only provides access to a variety of Foundation Models but also offers the option to privately fine-tune and customize these models for their unique use case.Which AWS service/feature offers a choice of high-performing Foundation Models (FMs) and the ability to privately customize the FMs with your data?

A : Amazon Q Developer

B : Amazon Q in QuickSight

C : AWS Inferentia

D : Amazon Bedrock

Answer: D

Question 2

Which of the following represents the best-fit use cases for utilizing Retrieval augmented generation (RAG) in Amazon Bedrock? (Select two)

A : Customer service chatbot

B : Original content creation

C : Product recommendations that match shopper preferences

D : Image generation from text prompt

E : Medical queries chatbot

Answer: A,E

Question 3

A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.Given this context, which statement best defines the use of MLflow with Amazon SageMaker?

A : Label data using human-in-the-loop

B : Leverage no-code ML

C : Manage machine learning experiments

D : Perform automatic model tuning

Answer: C

Question 4

A retail company is looking to streamline its machine learning workflows and improve collaboration between data science teams. The team is considering using MLflow with Amazon SageMaker to manage the end-to-end machine learning lifecycle. Understanding how MLflow integrates with Amazon SageMaker will help the team decide if this combination is the right fit for their machine learning project management needs.Given this context, which statement best defines the use of MLflow with Amazon SageMaker?

A : Label data using human-in-the-loop

B : Leverage no-code ML

C : Manage machine learning experiments

D : Perform automatic model tuning

Answer: C

Question 5

A company wants to create a chatbot that answers questions about human resources policies. The company is using a large language model (LLM) and has a large digital documentation base. Which technique should the company use to optimize the generated responses?

A : Use Retrieval Augmented Generation (RAG).

B : Use few-shot prompting.

C : Set the temperature to 1.

D : Decrease the token size.

Answer: A