A company has deployed an ML model. The company wants to provide external customers with secure access to the model through the customers' own applications.
Which solution will meet these requirements?
A. Use a custom script in the customers' application for authentication.
B. Store model credentials and share them with the customers directly for authentication.
C. Create a secure API endpoint that customers can use.
D. Embed the model directly into the customers' applications.
Show Answer
Correct Answer: C
Explanation: A secure API endpoint is the standard architecture for exposing an ML model to external customer applications. It allows strong authentication and authorization (such as API keys, OAuth, or IAM where appropriate), uses HTTPS for secure transport, and keeps the model hosted and controlled by the company. The other options either expose credentials insecurely, rely on custom authentication without a secure service interface, or distribute the model into customer applications, reducing security and control.
Question 12
A social media company uses an AI-based "Add as Friend" recommendation system. The company wants to ensure that its AI models are transparent and explainable.
Which solution will meet these requirements in the MOST operationally efficient way?
A. Use Amazon Rekognition to analyze user profile pictures to improve model recommendations.
B. Use Amazon SageMaker Clarify to enhance the explainability of model recommendations.
C. Migrate the AI model to Amazon Personalize to improve model recommendations.
D. Use Amazon SageMaker Ground Truth to manually detect model bias.
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker Clarify is designed to improve transparency and explainability of machine learning models by providing feature attribution and bias detection reports. It directly addresses the requirement for explainable AI with minimal operational overhead. Amazon Rekognition is for image analysis, Amazon Personalize focuses on recommendations rather than explainability, and SageMaker Ground Truth is for data labeling, not model explainability.
Question 13
HOTSPOT
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A company is using Amazon Bedrock AgentCore to build, deploy, and implement agents at scale. The company needs to identify which resources to use.
Select the correct AgentCore resource from the following list for each description. Select each resource one time or not at all.
Explanation: Observability provides dashboards and monitoring, Code Interpreter executes code securely across languages, and Browser tool offers a fast, secure, serverless browser runtime for agents.
Question 14
A company wants to develop an AI solution to automatically extract structured and unstructured data from scanned PDF documents and forms.
Which AWS service will meet these requirements?
A. Amazon Polly
B. Amazon Lex
C. Amazon Personalize
D. Amazon Textract
Show Answer
Correct Answer: D
Explanation: Amazon Textract is the AWS service designed to extract text, key-value pairs, tables, and other structured and unstructured content from scanned documents, PDFs, and forms using machine learning. The other options are for speech synthesis (Polly), conversational interfaces (Lex), and recommendations (Personalize).
Question 15
A company wants to call a single Amazon Bedrock endpoint and needs Amazon Bedrock to pick the most suitable foundation model (FM) for each request. The company does not want to hardcode a model.
Which Amazon Bedrock feature or resource will meet these requirements?
A. Amazon Bedrock Guardrails
B. Amazon Bedrock Knowledge Bases
C. Amazon Bedrock prompt router
D. Amazon Bedrock agent memory
Show Answer
Correct Answer: C
Explanation: The Amazon Bedrock prompt router provides a single endpoint that intelligently routes each request to the most appropriate foundation model based on the prompt and optimization criteria, eliminating the need to hardcode model selection. Guardrails enforce safety policies, Knowledge Bases provide RAG capabilities, and agent memory stores conversational context rather than selecting models.
Question 16
A company is using a generative AI (GenAI) system to generate medical summaries from patient records. The system sometimes produces inaccurate information that appears credible.
Which strategy will mitigate the issue?
A. Implement strict input validation and output formatting rules.
B. Implement Retrieval Augmented Generation (RAG) to ground responses.
C. Use smaller, more transparent models for medical applications.
D. Increase computational resources and model size.
Show Answer
Correct Answer: B
Explanation: The issue described is hallucination: the model generates plausible but inaccurate medical information. Retrieval Augmented Generation (RAG) mitigates this by retrieving relevant, trusted information (such as patient records or approved medical knowledge) and grounding the model's response in that context, reducing unsupported or fabricated content. Input validation and formatting do not address hallucinations, smaller models are not inherently more accurate, and more compute or larger models do not reliably eliminate hallucinations.
Question 17
A company is building an agentic AI application by using Model Context Protocol (MCP) servers with Amazon Q Developer CLI. The company wants to follow MCP security practices.
Which solutions will meet these requirements? (Choose two.)
A. Keep MCP servers and the Amazon Q Developer CLI updated.
B. Monitor MCP logs for normal successful activity.
C. Use plain text files to store sensitive configurations.
D. Automatically approve all tools that are provided by MCP servers.
E. Install servers only from trusted sources.
Show Answer
Correct Answer: A, E
Explanation: MCP security best practices include keeping both MCP servers and the Amazon Q Developer CLI up to date to receive security fixes, and installing MCP servers only from trusted sources to reduce supply-chain risk. Monitoring only normal successful activity is insufficient because security monitoring should include anomalous and failed activity. Sensitive configurations should not be stored in plain text, and tools exposed by MCP servers should not be auto-approved without review.
Question 18
An AI practitioner wants to quickly deploy and fine-tune foundation models (FMs) for generative AI (GenAI) tasks. The AI practitioner needs access to pre-trained models, solution templates, and example notebooks that can accelerate the development process.
Which AWS feature or resource meets these requirements?
A. Amazon SageMaker Studio
B. Amazon SageMaker JumpStart
C. Amazon Bedrock model catalog
D. Amazon SageMaker Canvas
Show Answer
Correct Answer: B
Explanation: Amazon SageMaker JumpStart provides access to pre-trained foundation models, solution templates, and example notebooks, and supports quick deployment and fine-tuning of models for generative AI workloads. SageMaker Studio is the IDE, Bedrock model catalog lists available foundation models but does not provide the same collection of templates and notebooks, and SageMaker Canvas is a no-code ML tool.
Question 19
A company wants to use foundation models (FMs) for multiple use cases, including content summarization, code generation, and customer service AI assistants.
Which solution will implement FMs MOST cost-effectively?
A. Select the smallest model that meets the performance requirements for each specific use case.
B. Use the largest and most capable model available for all tasks to ensure quality.
C. Build and train a custom FM for each individual use case.
D. Use batch processing for all inference requests to reduce the costs and latency for each request.
Show Answer
Correct Answer: A
Explanation: Choosing the smallest foundation model that still satisfies the required quality for each use case is the most cost-effective approach. Smaller models generally have lower inference costs and latency. Using the largest model for every task wastes resources, training custom foundation models is extremely expensive, and batch processing is not appropriate for all workloads and does not inherently reduce per-request latency.
Question 20
A company that runs AI workloads on Amazon Bedrock needs to evaluate and document compliance with regulatory standards.
Which AWS service or feature meets these requirements?
A. AWS Audit Manager
B. Amazon SageMaker Model Cards
C. Amazon SageMaker Model Monitor
D. AWS Artifact
Show Answer
Correct Answer: A
Explanation: AWS Audit Manager is designed to continuously assess and document compliance by automating evidence collection, mapping evidence to regulatory frameworks, and producing audit-ready reports. Amazon SageMaker Model Cards document model details rather than regulatory compliance, Model Monitor detects data/model quality issues, and AWS Artifact provides access to compliance reports and agreements but does not evaluate or document an organization's compliance posture.
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