A company is using a pre-trained large language model (LLM). The LLM must perform multiple tasks that require specific domain knowledge. The LLM does not have information about several technical topics in the domain. The company has unlabeled data that the company can use to fine-tune the model.
Which fine-tuning method will meet these requirements?
A. Full training
B. Supervised fine-tuning
C. Continued pre-training
D. Retrieval Augmented Generation (RAG)
Show Answer
Correct Answer: C
Explanation: Continued pre-training adapts a pre-trained LLM by training it further on unlabeled domain-specific text, allowing it to acquire technical knowledge for multiple downstream tasks. Supervised fine-tuning requires labeled data, RAG augments inference rather than fine-tuning, and full training retrains from scratch and is unnecessary.
Question 125
A company wants to implement a generative AI solution to improve its marketing operations. The company wants to increase its revenue in the next 6 months.
Which approach will meet these requirements?
A. Immediately start training a custom FM by using the company's existing data.
B. Conduct stakeholder interviews to refine use cases and set measurable goals.
C. Implement a prebuilt AI assistant solution and measure its impact on customer satisfaction.
D. Analyze industry AI implementations and replicate the most successful features.
Show Answer
Correct Answer: B
Explanation: The best approach is to begin by identifying and refining business use cases with stakeholders and defining measurable goals tied to the desired business outcome (increased revenue within 6 months). Training a custom foundation model is costly and premature, deploying a prebuilt assistant focused on customer satisfaction does not directly align with the stated revenue objective, and copying industry implementations without validating the company's own needs is not a best practice.
Question 126
A company wants to use Amazon Q Business for its data. The company needs to ensure the security and privacy of the data.
Which combination of steps will meet these requirements? (Choose two.)
A. Enable AWS Key Management Service (AWS KMS) keys for the Amazon Q Business Enterprise index.
B. Set up cross-account access to the Amazon Q index.
C. Configure Amazon Inspector for authentication.
D. Allow public access to the Amazon Q index.
E. Configure AWS Identity and Access Management (IAM) for authentication.
Show Answer
Correct Answer: A, E
Explanation: Amazon Q Business supports encryption of enterprise indexes using AWS KMS keys to protect data at rest. Access to Amazon Q Business applications and resources should be controlled through AWS Identity and Access Management (IAM) for authentication and authorization. Cross-account access is not a core security requirement here, Amazon Inspector does not provide authentication, and public access would reduce data privacy.
Question 127
A company runs a website for users to make travel reservations. The company wants an AI solution to help create consistent branding for hotels on the website.
The AI solution needs to generate hotel descriptions for the website in a consistent writing style.
Which AWS service will meet these requirements?
A. Amazon Comprehend
B. Amazon Personalize
C. Amazon Rekognition
D. Amazon Bedrock
Show Answer
Correct Answer: D
Explanation: Amazon Bedrock provides managed access to foundation models capable of generating high-quality text in a consistent style. It is the appropriate AWS service for creating branded hotel descriptions. Amazon Comprehend analyzes text, Amazon Personalize recommends items, and Amazon Rekognition analyzes images and videos.
Question 128
A company plans to use a generative AI model to provide real-time service quotes to users.
Which criteria should the company use to select the correct model for this use case?
A. Model size
B. Training data quality
C. General-purpose use and high-powered GPU availability
D. Model latency and optimized inference speed
Show Answer
Correct Answer: D
Explanation: For a real-time service quote application, the primary selection criterion is low latency and optimized inference speed so responses are generated quickly enough for interactive use. While training data quality and model size can matter, the defining requirement for this use case is real-time responsiveness.
Question 129
An AI practitioner is using Amazon Bedrock Prompt Management to create a reusable prompt. The prompt must be able to interact with external services by calling an external API.
Which solution will meet this requirement?
A. Use special tokens.
B. Use a tools configuration.
C. Use prompt variables.
D. Use a stop sequence.
Show Answer
Correct Answer: B
Explanation: Amazon Bedrock Prompt Management supports tool use through a tools configuration, enabling the model to invoke external functions or APIs during inference. Prompt variables only parameterize prompts, stop sequences control generation termination, and special tokens do not provide external API integration.
Question 130
An airline company wants to use a generative AI model to convert a flight booking system from one coding language into another coding language. The company must select a model for this task.
Which criteria should the company use to select the correct generative AI model for this task?
A. Syntax, semantic understanding, and code optimization capabilities
B. Code generation speed and error handling capabilities
C. Ability to generate creative content
D. Model size and resource requirements
Show Answer
Correct Answer: A
Explanation: For code-to-code translation, the model must accurately understand the source language syntax and semantics, preserve program logic, and generate correct, efficient code in the target language. While speed, model size, and resource requirements can influence deployment, they are not the primary criteria for selecting a model for reliable code migration. Creative content generation is not relevant.
Question 131
A company is building a generative AI tool. The company will use internal documents to customize a foundation model (FM).
Which approach will meet this requirement?
A. Classification
B. Continued pre-training
C. Distillation
D. Regression
Show Answer
Correct Answer: B
Explanation: Continued pre-training further trains an existing foundation model on domain-specific corpora, such as a company's internal documents, to incorporate specialized knowledge and terminology. Classification and regression are predictive task types rather than model customization approaches, and distillation compresses or transfers knowledge from one model to another rather than adapting it to new internal data.
Question 132
A company acquires International Organization for Standardization (ISO) accreditation to manage AI risks and to use AI responsibly.
What does this accreditation reflect about the company?
A. All members of the company are ISO certified.
B. All AI systems that the company uses are ISO certified.
C. All AI application team members are ISO certified.
D. The company’s development framework is ISO certified.
Show Answer
Correct Answer: D
Explanation: ISO accreditation for AI management certifies an organization's management system, processes, and governance framework against ISO standards (such as ISO/IEC 42001), not individual employees, teams, or specific AI systems.
Question 133
A company is using large language models (LLMs) to develop online tutoring applications. The company needs to apply configurable safeguards to the LLMs. These safeguards must ensure that the LLMs follow standard safety rules when creating applications.
Which solution will meet these requirements with the LEAST effort?
A. Amazon Bedrock playgrounds
B. Amazon SageMaker Clarify
C. Amazon Bedrock Guardrails
D. Amazon SageMaker Jumpstart
Show Answer
Correct Answer: C
Explanation: Amazon Bedrock Guardrails is designed to apply configurable safety controls and policies across foundation models, including content filtering, denied topics, sensitive information protection, and other safeguards. It is the managed AWS feature intended for enforcing standard safety rules for LLM applications with minimal implementation effort. The other options are for experimentation (Bedrock playgrounds), model bias/explainability (SageMaker Clarify), or deploying foundation models (SageMaker JumpStart), not configurable runtime safety guardrails.
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