Free Amazon AIF-C01 Exam Questions

Question 1

A tech company is developing an AI-powered customer support solution using Retrieval-Augmented Generation (RAG) with Amazon Bedrock to provide more accurate and context-aware responses. To achieve this, the company needs a database that can handle fast index lookups and similarity searches to quickly retrieve the most relevant documents or information from a large dataset. The ideal database solution should efficiently support search queries and rank results based on their relevance to the input provided by the user.Given these requirements, which database solution would be most appropriate for the company to use?

A : The company should use Amazon OpenSearch Service, which is designed to provide fast search capabilities and supports full-text search, indexing, and similarity scoring

B : The company should use Amazon DocumentDB (with MongoDB compatibility), a managed NoSQL document database service designed for storing semi-structured data to facilitate search capabilities

C : The company should use Amazon Aurora, a managed relational database service that is optimized for high-performance transactional workloads that can be useful for search operations

D : The company should use Amazon DynamoDB, a fully managed NoSQL database service that offers low-latency data retrieval to handle fast index lookups as well as search operations

Answer: A

Question 2

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 3

A company wants a unified search solution that can connect the company's multiple data repositories, third-party document repositories, and FAQs to create a new search experience so that the employees can efficiently find the right answers for their queries.

Which ML-powered AWS service offers these search features?

A : Amazon Textract

B : Amazon Kendra

C : Amazon Comprehend

D : Amazon SageMaker Data Wrangler

Answer: B

Question 4

A bank has fine-tuned a large language model (LLM) to expedite the loan approval process. During an external audit of the model, the company discovered that the model was approving loans at a faster pace for a specific demographic than for other demographics. How should the bank fix this issue MOST cost-effectively?

A : Include more diverse training data. Fine-tune the model again by using the new data.

B : Use Retrieval Augmented Generation (RAG) with the fine-tuned model.

C : Use AWS Trusted Advisor checks to eliminate bias.

D : Pre-train a new LLM with more diverse training data.

Answer: A

Question 5

An insurance company is transitioning to AWS Cloud and wants to use Amazon Bedrock for product recommendations. The company wants to supplement organization-specific information to the underlying Foundation Model (FM).

Which of the following represents the best-fit solution for the given use case?

A : Implement Reinforcement Learning from Human Feedback (RLHF) in Amazon Bedrock by leveraging the contextual information from the company's private data

B : Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Reinforcement Learning from Human Feedback (RLHF)

C : Use Knowledge Bases for Amazon Bedrock to supplement contextual information from the company's private data to the FM using Retrieval Augmented Generation (RAG)

D : Fine-tune the base Foundation Model (FM) used by Amazon Bedrock by leveraging the contextual information from the company's private data

Answer: C