A company deploys a foundation model (FM). The company notices that the FM is producing answers to user-submitted questions about politics. The company wants to ensure that the model does not send answers to political questions to users.
Which AWS solution will meet this requirement?
A. Amazon Bedrock Guardrails
B. Amazon Bedrock Agents
C. Amazon SageMaker Clarify
D. Amazon SageMaker Model Monitor
Show Answer
Correct Answer: A
Explanation: Amazon Bedrock Guardrails can enforce content policies and deny or filter responses for specified topics, including blocking or refusing political content. Bedrock Agents orchestrate tasks rather than enforce topic restrictions, SageMaker Clarify evaluates bias and explainability, and SageMaker Model Monitor detects data/model quality drift rather than filtering generated outputs.
Question 52
A company maintains a large product catalog with detailed descriptions. The company wants to build an AI assistant to answer customer questions about the products. However, the company’s labeled training data is limited.
Which solution will meet these requirements with the LEAST implementation effort?
A. Use a Retrieval Augmented Generation (RAG) architecture to query the product database at runtime. Provide relevant, unmodified product descriptions as context for the foundation model (FM).
B. Fine-tune a foundation model (FM) on the restricted labeled data. Automatically refresh the model with novel product details weekly for accuracy.
C. Deploy a foundation model (FM) for each product category. Implement a routing layer to direct customer queries to the appropriate specialized model.
D. Create a new custom foundation model (FM) that is trained on the product database. Optimize the FM for minimal token usage during inference.
Show Answer
Correct Answer: A
Explanation: A Retrieval Augmented Generation (RAG) architecture is the best fit because it leverages the existing product catalog as the knowledge source, retrieves relevant product descriptions at query time, and supplies them as context to the foundation model. This requires little to no labeled training data, minimizes implementation effort, and keeps responses current without frequent retraining.
Question 53
A company uses foundation models (FMs) to automate daily tasks. An AI practitioner at the company is creating system instructions that include context that is relevant to the tasks. The AI practitioner wants to save and reuse the instructions in daily interactions with FMs in Amazon Bedrock.
Which Amazon Bedrock solution will meet these requirements?
A. Knowledge Bases
B. Guardrails
C. Playgrounds
D. Prompt management
Show Answer
Correct Answer: D
Explanation: Prompt management in Amazon Bedrock is designed to create, save, version, and reuse prompt templates, including system instructions and contextual prompts, across repeated interactions with foundation models. Knowledge Bases provide retrieval-augmented context from data sources, Guardrails enforce safety and policy controls, and Playgrounds are for experimentation rather than reusable prompt management.
Question 54
What is an example of structured data?
A. A file of text comments from an online forum
B. A compilation of video files that contains news broadcasts
C. A CSV file that consists of measurement data
D. Transcribed conversations between call center agents and customers
Show Answer
Correct Answer: C
Explanation: Structured data is organized into a predefined schema with rows and columns, making it easy to store and query. A CSV file containing measurement data is a classic example of structured data. The other options consist of primarily unstructured text, audio/video, or transcripts.
Question 55
A company wants to use a large language model (LLM) to generate responses based on company data.
Which strategy will meet this requirement?
A. Use first-party pre-trained models from Amazon Bedrock.
B. Use pre-trained open source models.
C. Fine-tune a custom model on company data.
D. Use pre-trained third-party models from Amazon Bedrock.
Show Answer
Correct Answer: C
Explanation: Among the options, fine-tuning a custom model on company data is the strategy that adapts the model to incorporate organization-specific knowledge. Pre-trained first-party, third-party, or open-source models are trained on general data and do not automatically generate responses based on a company's proprietary data. Although retrieval-augmented generation is often preferred in practice, it is not an available option here.
Question 57
A company is deploying an AI-powered loan approval system. The company must comply with data governance regulations for AI.
Which solution will meet these requirements?
A. Modify AI outputs based on user preferences without audit trails.
B. Implement data lifecycle management to track and manage AI training data.
C. Prioritize AI inference time optimization over data residency requirements.
D. Use only synthetic data for model training to avoid compliance risks.
Show Answer
Correct Answer: B
Explanation: Data governance regulations for AI typically require managing and tracking data throughout its lifecycle, including provenance, quality, retention, and auditability of training data. Implementing data lifecycle management supports compliance. The other options either reduce auditability, ignore regulatory requirements, or are not sufficient for compliance.
Question 58
A company has implemented a large language model (LLM) solution by using a pre-trained model. The company needs to ensure that the model’s responses are transparent and accurate. The company wants to ground the model’s responses in factual information from the company’s authoritative data sources.
Which technique should the company use to meet these requirements?
A. Prompt engineering
B. Reinforcement learning
C. Retrieval Augmented Generation (RAG)
D. Static knowledge base
Show Answer
Correct Answer: C
Explanation: Retrieval Augmented Generation (RAG) retrieves relevant information from authoritative company data sources at inference time and uses that retrieved context to ground the LLM's responses. This improves factual accuracy, transparency by tying responses to source material, and keeps knowledge up to date without retraining the model.
Question 60
An AI practitioner is creating prompts for an Amazon Nova Canvas model. The AI practitioner wants to exclude certain characteristics from the model’s generated output.
Which prompting technique will meet this requirement?
A. Mask prompts
B. Negative prompts
C. Inpainting prompts
D. Outpainting prompts
Show Answer
Correct Answer: B
Explanation: Negative prompts are used to specify characteristics or elements that should be excluded from the generated output. Amazon Nova Canvas supports negative prompts to guide image generation away from unwanted features. Mask prompts are not the mechanism for excluding attributes globally, while inpainting and outpainting are image editing techniques for modifying or extending images.
Question 61
A company has developed a generative AI application by using Amazon Bedrock. The application produces catalogs that are updated weekly. The company needs to select a pricing model for weekly catalog updates.
Which Amazon Bedrock pricing model meets these requirements MOST cost-effectively?
A. On-Demand
B. Provisioned Throughput
C. Batch
D. Model evaluation
Show Answer
Correct Answer: C
Explanation: Batch pricing is the most cost-effective choice for scheduled, non-real-time inference workloads such as weekly catalog generation. On-Demand is intended for interactive usage, Provisioned Throughput is for predictable low-latency/high-volume real-time traffic, and Model evaluation is for assessing model quality rather than running production inference.
Question 62
A company has developed an AI assistant application for customers by using Amazon Bedrock Agents. The company notices that the agents require additional context to make informed decisions consistently. The agents are not accessing external services to complete customer travel booking requests.
The company needs to ensure that the agents access additional context when necessary.
Which solution will meet this requirement?
A. Negative prompting
B. Model Context Protocol (MCP)
C. Zero-shot prompting
D. Custom API to provide more context
Show Answer
Correct Answer: B
Explanation: Model Context Protocol (MCP) enables AI agents to connect to external tools, services, and data sources in a standardized way. For Amazon Bedrock Agents, MCP is the appropriate mechanism to provide additional context and access external services, such as travel booking systems, when needed. Negative prompting and zero-shot prompting are prompting techniques that do not enable external context access, and while a custom API could provide context, MCP is the standardized solution designed for this use case.
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