Pass Using AIF-C01 Exam Dumps

Transitioning to a new large language model (LLM) on Amazon Bedrock typically involves minimal changes when the new model is pre-trained and available as a foundation model. Since the company is moving from one pre-trained LLM to another, the primary task is to ensure compatibility between the new model's input requirements and the existing application. Adjusting the prompt template is often necessary because different LLMs may have varying prompt formats, tokenization methods, or response behaviors, even for similar tasks like document extraction.

Exact Extract from AWS AI Documents:

From the AWS Bedrock User Guide:

"When switching between foundation models in Amazon Bedrock, you may need to adjust the prompt template to align with the new model’s expected input format and optimize its performance for your use case. Prompt engineering is critical to ensure the model understands the task and generates accurate outputs."

(Source: AWS Bedrock User Guide, Prompt Engineering for Foundation Models)

Detailed Explanation:

Option A: Create a new labeled dataset.Creating a new labeled dataset is unnecessary when transitioning to a new pre-trained LLM, as pre-trained models are already trained on large datasets. This option would only be relevant if the company were training a custom model from scratch, which is not the case here.

Option B: Perform feature engineering.Feature engineering is typically associated with traditional machine learning models, not pre-trained LLMs. LLMs process raw text inputs, and transitioning to a new LLM does not require restructuring input features. This option is incorrect.

Option C: Adjust the prompt template.This is the correct approach. Different LLMs may interpret prompts differently due to variations in training data, tokenization, or model architecture. Adjusting the prompt template ensures the new LLM understands the task (e.g., document extraction) and produces the desired output format. AWS documentation emphasizes prompt engineering as a key step when adopting a new foundation model.

Option D: Fine-tune the LLM.Fine-tuning is not required for transitioning to a new pre-trained LLM unless the company needs to customize the model for a highly specific task. Since the question does not indicate a need for customization beyond document extraction (a common LLM capability), fine-tuning is unnecessary.

The retail company wants to build an ML model for product recommendations using responsible practices to decrease model bias. Collecting balanced and diverse data ensures the model does not favor specific groups, reducing bias and promoting fairness, a key responsible AI practice.

Exact Extract from AWS AI Documents:

From the AWS AI Practitioner Learning Path:

"To reduce model bias, it is critical to collect balanced and diverse data that represents various demographics and user groups. This practice ensures fairness and prevents the model from disproportionately favoring certain populations."

(Source: AWS AI Practitioner Learning Path, Module on Responsible AI)

Detailed Explanation:

Option A: Use data from only customers who match the demography of the company's overall customer base.Limiting data to a specific demographic may reinforce existing biases, failing to address underrepresented groups and increasing bias.

Option B: Collect data from customers who have a past purchase history.Focusing only on customers with purchase history may exclude new users, potentially introducing bias, and does not address diversity.

Option C: Ensure that the data is balanced and collected from a diverse group.This is the correct answer. A balanced and diverse dataset reduces bias by ensuring the model learns from a representative sample, aligning with responsible AI practices.

Option D: Ensure that the data is from a publicly available dataset.Public datasets may not be diverse or representative of the company’s customer base and could introduce unrelated biases, failing to address fairness.

AWS PrivateLink provides private connectivity between VPCs, AWS services, and on-premises networks, ensuring traffic does not traverse the public internet.

A is correct:

"AWS PrivateLink provides private connectivity to services across VPCs, keeping API traffic off the public internet."

(Reference: AWS PrivateLink Overview)

B (Amazon Q) is a generative AI assistant, not a network security/control tool.

C (CloudFront) is a CDN, not for private API calls.

D (CloudTrail) is for logging and monitoring, not secure connectivity.

The correct answer is Amazon Bedrock PartyRock, a playground for building and experimenting with generative AI apps in a low-cost, no-code environment. PartyRock is designed for innovation and learning. It enables users to try out prompts, LLM apps, and templates using Amazon Bedrock under a free-tier friendly setup. According to AWS, PartyRock abstracts infrastructure and allows rapid prototyping using models from Bedrock providers. This makes it ideal for early experimentation, especially for non-developers or those not ready to invest in full production pipelines. In contrast, Amazon Q Developer is for software engineering tasks, SageMaker JumpStart focuses on deploying ML models, and Q Business targets enterprise knowledge workers. None of those are as cost-effective and experimental-focused as PartyRock.

Referenced AWS AI/ML Documents and Study Guides:

Amazon Bedrock Documentation – PartyRock Overview

AWS Generative AI Learning Path – Getting Started Tools