Deploying and Integrating Generative AI in Test Organizations practice questions

Shadow AI risks: information security and data privacy weaknesses, compliance and regulatory issues, and vague intellectual property.

The syllabus lists three risk areas for shadow AI: security and data privacy (personal tools may lack robust security), compliance and regulatory issues (unapproved tools can break standards and regulations), and vague intellectual property (unclear licensing can lead to IP disputes). Energy, drift and coverage are GenAI topics elsewhere, not shadow AI risks.

1. Recall the three shadow AI risk areas: security/privacy, compliance, intellectual property.
2. Match each option to one of them.
3. Reject risks that belong to other GenAI topics.

Exam tip: Shadow AI risks: security and privacy, compliance, intellectual property.

Personal AI tools used without approval may lack robust security, so sensitive data put into them can be breached.

The syllabus's first shadow AI risk is information security and data privacy: personal AI tools may lack robust security, leading to potential data breaches. Customer email addresses are personal data, so pasting them into personal accounts most directly creates that risk. IP disputes, hallucinations and cost are real concerns but are not what this scenario most directly exposes.

1. Identify what is put into the tool: personal data.
2. Identify the tool: a personal, unapproved account.
3. Map personal data in an unvetted tool to the security and privacy risk.

Exam tip: Sensitive data + personal AI tool = breach risk.

Unapproved AI tools can lead to non-compliance with industry standards and regulations, potentially with legal consequences.

The syllabus's compliance risk is that unapproved AI tools can lead to non-compliance with industry standards and regulations and so to legal consequences. The stem makes the regulation explicit and rules out personal data (the data is synthetic), so a data breach is not the evident risk; quality and ownership are secondary.

1. Note the stated rule: tools must be assessed and registered.
2. Note that the data is synthetic, so no personal data is exposed.
3. The unregistered tool breaks the rule: a compliance risk.

Exam tip: Unapproved tool in a regulated setting = compliance and legal risk.

AI tools with unclear licensing can expose users to intellectual property disputes, especially when copyrighted data is processed without proper authorization.

The third shadow AI risk in the syllabus is vague intellectual property. Two details point to it: the specifications are licensed to the organization by a third party, and the tool's terms do not say how uploaded content is used or who owns the output. No personal data is involved, so privacy regulation is not the main concern here.

1. Note the content: licensed, copyrighted third-party material.
2. Note the tool: unclear terms of use and ownership.
3. Unclear licensing + copyrighted input = intellectual property risk.

Exam tip: Unclear licensing terms + copyrighted input = IP dispute risk.

A strategy and steps for integrating and deploying GenAI help test organizations avoid the risk of shadow AI.

Every option is a real control, but the stem names the cause: approved tools do not cover the testers' tasks. Blocking, policies and audits restrict or detect unapproved use without meeting the need, so use tends to move elsewhere. The syllabus answer is a GenAI strategy with integration and deployment steps that put approved tools where the work is, which is the long-term fix.

1. Find the cause in the stem: an unmet need for GenAI support.
2. Ask which option removes that cause rather than restricting or detecting use.
3. A strategy and deployment steps provide sanctioned tools.

Exam tip: Avoid shadow AI by giving people an approved path: strategy plus deployment steps.

A GenAI strategy for testing begins with measurable test objectives, such as increasing productivity, shortening test cycles and improving quality.

The syllabus starts the strategy with measurable test objectives, giving examples such as increasing productivity, shortening test cycles and improving quality. All four options are measurable and dated, so measurability alone does not decide: only the shorter regression cycle is a test outcome. Usage hours and the share of generated test cases are adoption or output targets that can be met while testing gets no better, and moving to a model is a selection decision that should follow from the objectives.

1. Notice that every option has a number and a deadline.
2. Ask which one describes an improvement in testing itself: productivity, cycle time or quality.
3. Reject usage, output-volume and tool-choice targets presented as objectives.

Exam tip: A strategy objective measures a test outcome, not how much GenAI is used.