Question 1

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?

Options :

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 2

A company wants to use language models to create an application for inference on edge devices. The inference must have the lowest latency possible. Which solution will meet these requirements?

Options :

A : Deploy optimized small language models (SLMs) on edge devices.

B : Deploy optimized large language models (LLMs) on edge devices.

C : Incorporate a centralized small language model (SLM) API for asynchronous communication with edge devices.

D : Incorporate a centralized large language model (LLM) API for asynchronous communication with edge devices.

Answer: A

Question 3

An education provider is building a question and answer application that uses a generative AI model to explain complex concepts. The education provider wants to automatically change the style of the model response depending on who is asking the question. The education provider will give the model the age range of the user who has asked the question. Which solution meets these requirements with the LEAST implementation effort?

Options :

A : Fine-tune the model by using additional training data that is representative of the various age ranges that the application will support.

B : Add a role description to the prompt context that instructs the model of the age range that the response should target.

C : Use chain-of-thought reasoning to deduce the correct style and complexity for a response suitable for that user

D : Summarize the response text depending on the age of the user so that younger users receive shorter responses.

Answer: B

Question 4

A technology consulting firm is advising a client on the use of AI to enhance their business operations, particularly through the implementation of large-scale models that can handle diverse tasks such as text generation, image recognition, and natural language understanding. The firm is evaluating Foundation Models as a potential solution and wants to clarify their capabilities, including their ability to generalize across multiple domains and perform a wide range of tasks with minimal fine-tuning.Which of the following options aptly summarizes the capabilities of Foundation Models?

Options :

A : Foundation models are limited to simple data processing tasks and cannot handle complex operations

B : Foundation models can perform a wide range of tasks across different domains by leveraging their extensive pre-training on large datasets

C : Foundation models can only perform a single task they were specifically trained for

D : Foundation models are designed to work exclusively with structured data and cannot process unstructured data like text or images

Answer: B

Question 5

A company is developing an ML model to predict heart disease risk. The model uses patient data,such as age, cholesterol, blood pressure, smoking status, and exercise habits. The dataset includes atarget value that indicates whether a patient has heart disease.Which ML technique will meet these requirements?

Options :

A : Unsupervised learning

B : Supervised learning

C : Reinforcement learning

D : Semi-supervised learning

Answer: B

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