AIF-C01 - New Updated Questions - Part 162-180

Sep 20, 2026

Practice and review questions for AWS Certified AI Practitioner (AIF-C01), covering core AI concepts and Amazon Bedrock use cases. Topics include transitioning between pre-trained LLMs by adapting prompt templates, applying natural language processing for text classification of customer feedback, and fine-tuning ML models with sensitive data that must remain inside a VPC using AWS PrivateLink. Additional items address building conversational assistants with Retrieval Augmented Generation (RAG) and introduce embeddings in the AI context.

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QUESTION 162

Transitioning to a new pre-trained LLM on Amazon Bedrock

QUESTION 163

Natural language processing for text classification

QUESTION 164

Fine-tuning in a VPC with AWS PrivateLink

QUESTION 165

Conversational assistant with least development effort using RAG

QUESTION 166

Embeddings in the context of AI

Braindump2go Guarantee All Exams 100% Pass 􀂾 Vendor: Amazon 􀂾 Exam Code: AIF-C01 􀂾 Exam Name: AWS Certified AI Practitioner AIF-C01 Exam 􀂾 New Updated Questions from Braindump2go (Updated in May/2025) Visit Braindump2go and Download AIF-C01 Exam Dumps QUESTION 162 A company is using a pre-trained large language model (LLM) to extract information from documents. The company noticed that a newer LLM from a different provider is available on Amazon Bedrock. The company wants to transition to the new LLM on Amazon Bedrock. What does the company need to do to transition to the new LLM? A. Create a new labeled dataset B. Perform feature engineering. C. Adjust the prompt template. D. Fine-tune the LLM. Answer: C Explanation: 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. 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. QUESTION 163 A customer service team is developing an application to analyze customer feedback and automatically classify the feedback into different categories. The categories include product quality, customer service, and delivery experience. Which AI concept does this scenario present? A. Computer vision B. Natural language processing (NLP) C. Recommendation systems D. Fraud detection Answer: B Explanation: The scenario involves analyzing customer feedback and automatically classifying it into categories such as product quality, customer service, and delivery experience. This task requires processing and understanding textual data, which is a core application of natural language processing (NLP) . NLP encompasses techniques for analyzing, interpreting, and generating human language, including tasks like text classification, sentiment analysis, and topic modeling, all of which are relevant to this use case. Natural Language Processing (NLP) enables machines to understand and process human language. Common NLP tasks include text classification, sentiment analysis, named entity recognition, and topic modeling. Services like Amazon Comprehend can be used to classify text into predefined categories based on content. QUESTION 164 A company wants to fine-tune an ML model that is hosted on Amazon Bedrock. The company wants to use its Braindump2go Guarantee All Exams 100% Pass own sensitive data that is stored in private databases in a VPC. The data needs to stay within the company's private network. Which solution will meet these requirements? A. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) service role. B. Restrict access to Amazon Bedrock by using an AWS Identity and Access Management (IAM) resource policy. C. Use AWS PrivateLink to connect the VPC and Amazon Bedrock. D. Use AWS Key Management Service (AWS KMS) keys to encrypt the data. Answer: C Explanation: The company wants to fine-tune an ML model on Amazon Bedrock using sensitive data stored in private databases within a VPC, ensuring the data remains within its private network. AWS PrivateLink provides a secure, private connection between a VPC and AWS services like Amazon Bedrock, allowing data to stay within the company's network without traversing the public internet. This meets the requirement f

FAQs

When transitioning to a new pre-trained LLM on Amazon Bedrock, what is the primary thing to change?

Adjust the prompt template (and ensure input compatibility), since different LLMs may expect different prompt formats and behaviors even for similar document extraction tasks.

Which AI concept fits a scenario that classifies customer feedback into categories like product quality, customer service, and delivery experience?

Natural language processing (NLP), because the task requires understanding and classifying textual data.

How can a company keep sensitive data within its private network while fine-tuning an ML model on Amazon Bedrock?

Use AWS PrivateLink to create a secure private connection between the VPC and Amazon Bedrock so the data does not traverse the public internet.

Sep 20, 2026

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