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Amazon AIB-C01 Sample Question Answers
Question # 1
A retail executive wants a business intelligence tool that can help non-technical staff ask naturallanguage questions about sales data and generate visual dashboards, in order to more easily communicate the business value being generated by the company's AI initiatives. Which AWS capability is MOST directly suited to this need?
A. Amazon Bedrock, used only for training foundation models from scratch B. AWS Marketplace, used only for purchasing third-party software licenses C. AWS Shared Responsibility Model, used only for defining security obligations D. Amazon Quick, AWS's business intelligence and productivity capability for exploring data and generating insights and dashboards
Answer: D
EXPLANATION
Amazon Quick refers to AWS's business intelligence and productivity capability, designed to help users, including non-technical staff, explore data, ask natural-language questions, and generate visual dashboards and insights. This directly supports the executive's goal of making it easier for nontechnical staff to communicate the business value of AI initiatives through accessible, visual reporting, distinct from services focused on foundation models, software procurement, or security responsibility division.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Amazon Bedrock is used for accessing and building with foundation models, not for business intelligence dashboards, and does not require training models from scratch.
B. AWS Marketplace supports procurement of third-party software broadly, not specifically business intelligence dashboarding.
C. The AWS Shared Responsibility Model addresses security and compliance responsibility division, unrelated to business intelligence reporting.
D. Correct option.
Question # 2
A finance team wants ongoing visibility into how much the company is spending on AI-related cloud services each month, broken down by project, so they can catch unexpected cost increases early.Which AWS tool is MOST directly suited to this need?A. AWS Cost ExplorerB. Amazon SageMaker AIC. Amazon BedrockD. AWS Cloud Adoption Framework
Answer: A
EXPLANATION
AWS Cost Explorer is a tool designed to help organizations visualize, understand, and track their AWS spending over time, including the ability to break down costs by service, project, or other dimensions.
This directly supports the finance team's need for ongoing visibility into AI-related cloud spending by project, allowing early detection of unexpected cost increases, which is distinct from tools focused on building models, accessing foundation models, or assessing organizational readiness.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Correct option.
B. Amazon SageMaker AI is a machine learning development platform, not a cost visibility tool.
C. Amazon Bedrock provides access to foundation models for generative AI applications, not cost tracking.
D. The AWS Cloud Adoption Framework is an organizational readiness framework, not an ongoing cost-monitoring tool.
Question # 3
A subscription media company wants to select a KPI that most directly reflects whether its new AIpowered content recommendation feature is improving the customer experience. Which metric is MOST appropriate for this purpose?
A. The number of engineers who worked on the recommendation feature B. The total number of foundation models evaluated during development C. Customer satisfaction, such as a Net Promoter Score (NPS) or satisfaction survey trend, measured before and after the feature's launch D. The size of the marketing budget for an unrelated advertising campaign
Answer: C
EXPLANATION
When the business objective is improving customer experience, a customer satisfaction metric such as Net Promoter Score or a satisfaction survey trend, tracked before and after the AI feature's launch, most directly measures whether that objective is being achieved. This provides a business-valueoriented KPI directly tied to the stated goal, rather than a metric that reflects effort, process, or unrelated business activity.
WHY THE OTHER OPTIONS ARE INCORRECT
A. The number of engineers involved reflects staffing, not customer experience impact.
B. The number of models evaluated is a development-process detail, not a customer experience outcome.
C. Correct option.
D. An unrelated marketing budget has no direct connection to whether the recommendation
feature improved customer experience
Question # 4
A company deploying a generative AI application built on a foundation model accessed through a managed cloud AI service asks who is responsible for securing the underlying infrastructure versus who is responsible for securing the data and access configurations the company controls. Which concept BEST addresses this division of responsibility?
A. The AWS Shared Responsibility Model, which delineates security and compliance responsibilities between the cloud provider and the customer B. Model fine-tuning C. Token limit configuration D. Seat-based pricing
Answer: A
EXPLANATION
The AWS Shared Responsibility Model defines which security and compliance responsibilities belong to AWS, generally including the security "of" the cloud, such as underlying infrastructure, and which responsibilities remain with the customer, generally including security "in" the cloud, such as data protection, access management, and configuration of the services they use. Understanding this division helps a business strategist correctly scope the organization's own governance and security obligations when using managed AI services, rather than assuming the cloud provider handles all aspects of security.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Correct option.
B. Fine-tuning is a model adaptation technique unrelated to the division of security responsibilities.
C. Token limit configuration is a technical text-processing setting, unrelated to security responsibility division.
D. Seat-based pricing is a cost model, unrelated to security responsibility division.
Question # 5
A telecommunications company implements a policy requiring that any generative AI tool used for business purposes must first be reviewed and added to a centrally maintained, published list before employees may use it, with periodic reviews to keep the list current. Which practice does this scenario BEST represent?
A. Fine-tuning a foundation model on proprietary telecom data B. Calculating the ROI of a completed AI initiative C. Establishing and maintaining an approved AI tool governance process to reduce unauthorized AI use D. Defining a token limit for a customer-facing chatbot
Answer: C
EXPLANATION
Requiring generative AI tools to be reviewed and formally added to a centrally maintained, periodically updated list before employees may use them is a governance process aimed at ensuring only vetted, compliant tools are used for business purposes. This structured approach reduces the risk of shadow
AI by making the approval process for new tools clear and ongoing, rather than a one-time, static determination.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Fine-tuning is a technical model adaptation activity, unrelated to a tool-approval governance process.
B. ROI calculation is a value-measurement activity, unrelated to tool approval governance.
C. Correct option.
D. Token limit definition is a technical model constraint, unrelated to the approval process described.
Question # 6
A large enterprise is beginning a multi-year AI transformation and wants a structured framework to assess organizational readiness across business, people, governance, platform, and operations perspectives before committing to a broad AI investment roadmap. Which AWS resource is MOST directly designed to support this kind of structured organizational assessment?
A. AWS Cost Explorer B. The AWS Cloud Adoption Framework (AWS CAF) C. Amazon Bedrock D. AWS Marketplace
Answer: B
EXPLANATION
The AWS Cloud Adoption Framework provides structured guidance to help organizations assess their readiness for cloud and AI adoption across multiple organizational perspectives, including business, people, governance, platform, and operations. It is designed to help leadership identify gaps and build a structured roadmap for transformation, which fits the enterprise's stated need for a comprehensive readiness assessment before committing to a broad, multi-year AI investment.
WHY THE OTHER OPTIONS ARE INCORRECT
A. AWS Cost Explorer is a cost visibility and analysis tool, not an organizational readiness framework.
B. Correct option.
C. Amazon Bedrock is a generative AI service for building applications, not an organizational assessment framework.
D. AWS Marketplace is a procurement catalog for software and services, not a readiness assessment framework.
Question # 7
A company's compliance function periodically re-evaluates whether a given AI use case still falls into the same risk tier it was originally assigned, particularly after new regulations are introduced in one of its markets. Why is this ongoing re-evaluation important?
A. Because AI risk tiers never need to be reviewed once they are initially assigned B. Because ongoing re-evaluation eliminates the need for the original risk assessment C. Because compliance requirements are identical across all countries and markets D. Because AI risk classification and compliance requirements can change over time as regulations, use case scope, or data sensitivity evolve, and a one-time assessment can become outdated
Answer: D
EXPLANATION
AI risk classification is not a static, one-time exercise. As regulations evolve, as a use case's scope expands, or as the sensitivity of the data involved changes, a use case's risk profile and applicable compliance requirements can shift, meaning an assessment conducted at launch may no longer accurately reflect current risk. Periodically re-evaluating risk tier assignments, especially after regulatory changes in relevant markets, helps ensure governance controls remain appropriately
matched to current risk.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Static, one-time risk tiering fails to account for evolving regulations and use case changes.
B. Ongoing re-evaluation builds on, rather than eliminates the value of, the original assessment.
C. Compliance requirements vary significantly across countries and markets, which is part of why ongoing re-evaluation matters.
D. Correct option.
Question # 8
A company's IT security team wants employees to have a single, clearly published list of generative AI tools that have been reviewed and approved for business use, so employees do not need to seek out unapproved alternatives. What is the PRIMARY business benefit of maintaining this approved AI tool list?
A. It guarantees the company will never have any AI-related risk B. It eliminates the need for any further AI governance activity C. It ensures employees never make mistakes when using AI tools D. It reduces the likelihood of shadow AI by giving employees a clear, sanctioned path to productive AI tools that meet the company's security and governance standards
Answer: D
EXPLANATION
Maintaining a clearly published, approved AI tool list gives employees a sanctioned, convenient path to productive AI tools that have already been vetted for security and governance requirements. This directly addresses one of the root causes of shadow AI, employees turning to unapproved tools out of convenience or lack of awareness, by making compliant options easy to find and use, reducing the incentive to seek out ungoverned alternatives.
WHY THE OTHER OPTIONS ARE INCORRECT
A. No governance measure can guarantee zero AI-related risk; it reduces, but does not eliminate, risk.
B. An approved tool list is one component of governance, not a replacement for ongoing governance activities such as monitoring and review.
C. An approved list reduces certain risks but does not guarantee error-free use of AI tools by employees.
D. Correct option.
Question # 9
A startup wants to quickly experiment with several publicly available foundation models to determine which best fits a new generative AI feature, without managing model-hosting infrastructure, while a separate internal team plans to later build and train a fully custom fraud-detection model using its own labeled data. Which pairing of AWS services BEST fits these two distinct needs?
A. Amazon SageMaker AI for foundation model experimentation, and Amazon Bedrock for building the custom fraud-detection model B. Amazon Bedrock for foundation model experimentation, and Amazon SageMaker AI for building the custom fraud-detection model C. AWS Cost Explorer for both needs D. AWS Marketplace for both needs, with no other services required
Answer: B
EXPLANATION
Amazon Bedrock is well suited to quickly experimenting with a choice of foundation models through a managed API without hosting infrastructure, making it a strong fit for the startup's generative AI feature exploration. Amazon SageMaker AI, by contrast, provides an end-to-end platform for building, training, and deploying fully custom machine learning models using an organization's own labeled data, which fits the internal team's fraud-detection model-building need. Selecting the right AWS service for each distinct need reflects sound AI solution selection.
WHY THE OTHER OPTIONS ARE INCORRECT
A. This reverses the appropriate service pairing; SageMaker AI is not primarily designed for quick, infrastructure-free foundation model experimentation, and Bedrock is not designed for training fully custom models from labeled data.
B. Correct option.
C. AWS Cost Explorer is a cost visibility tool, not a model development or hosting service.
D. AWS Marketplace is a procurement catalog, not a substitute for either model-experimentation or model-building capabilities.
Question # 10
A bank has successfully piloted an AI-powered document review tool and is preparing for full production rollout. The operations team wants to ensure there is a clear support process, including who to contact and how issues will be resolved if the tool malfunctions during business hours. What is the operations team MOST directly addressing?
A. Model fine-tuning frequency B. Seat-based pricing negotiations C. Operational readiness, including support processes needed to run the AI system reliably in production D. Token limit configuration
Answer: C
EXPLANATION
Operational readiness involves ensuring that an organization has the processes, support structures, and accountability in place to run a system reliably once it moves into full production, including clear escalation paths and support contacts if something malfunctions. This is a distinct and necessary consideration separate from the technical capability of the AI system itself, and it becomes increasingly important as an initiative moves from a limited pilot to full-scale, business-critical operation.
WHY THE OTHER OPTIONS ARE INCORRECT
A. Fine-tuning frequency is a technical model-adaptation topic, not a support/operations process.
B. Seat-based pricing negotiations concern licensing cost structure, not operational support processes.
C. Correct option.
D. Token limits are a technical text-processing constraint, unrelated to support and escalation processes