IAPP AIGP Exam Practice Test Instant Access

Question 1

You are a privacy program manager at a large e-commerce company that uses an Al tool to deliver personalized product recommendations based on visitors' personal information that has been collected from the company website, the chatbot and public data the company has scraped from social media.

A user submits a data access request under an applicable U.S. state privacy law, specifically seeking a copy of their personal data, including information used to create their profile for product recommendations.

What is the most challenging aspect of managing this request?

ASome of the visitor's data is synthetic data that the company does not have to provide to the data subject.

BThe data subject's data is structured data that can be searched, compiled and reviewed only by an automated tool.

CThe data subject is not entitled to receive a copy of their data because some of it was scraped from public sources.

DSome of the data subject's data is unstructured data and you cannot untangle it from the other data, including information about other individuals.

Answer : D

The most challenging aspect of managing a data access request in this scenario is dealing with unstructured data that cannot be easily disentangled from other data, including information about other individuals. Unstructured data, such as free-text inputs or social media posts, often lacks a clear structure and may be intermingled with data from multiple individuals, making it difficult to isolate the specific data related to the requester. This complexity poses significant challenges in complying with data access requests under privacy laws. Reference: AIGP Body of Knowledge on Data Subject Rights and Data Management.

Question 2

After completing model testing and validation, which of the following is the most important step that an organization takes prior to deploying the model into production?

APerform a readiness assessment.

BDefine a model-validation methodology.

CDocument maintenance teams and processes.

DIdentify known edge cases to monitor post-deployment.

Answer : A

After completing model testing and validation, the most important step prior to deploying the model into production is to perform a readiness assessment. This assessment ensures that the model is fully prepared for deployment, addressing any potential issues related to infrastructure, performance, security, and compliance. It verifies that the model meets all necessary criteria for a successful launch. Other steps, such as defining a model-validation methodology, documenting maintenance teams and processes, and identifying known edge cases, are also important but come secondary to confirming overall readiness. Reference: AIGP Body of Knowledge on Deployment Readiness.

Question 3

When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?

AFeature drift.

BSystem cost.

CModel drift.

DData loss.

Answer : B

When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.

Question 4

To maintain fairness in a deployed system, it is most important to?

AProtect against loss of personal data in the model.

BMonitor for data drift that may affect performance and accuracy.

CDetect anomalies outside established metrics that require new training data.

DOptimize computational resources and data to ensure efficiency and scalability.

Answer : B

To maintain fairness in a deployed system, it is crucial to monitor for data drift that may affect performance and accuracy. Data drift occurs when the statistical properties of the input data change over time, which can lead to a decline in model performance. Continuous monitoring and updating of the model with new data ensure that it remains fair and accurate, adapting to any changes in the data distribution. Reference: AIGP Body of Knowledge on Post-Deployment Monitoring and Model Maintenance.

Question 5

Training data is best defined as a subset of data that is used to?

AEnable a model to detect and learn patterns.

BFine-tune a model to improve accuracy and prevent overfitting.

CDetect the initial sources of biases to mitigate prior to deployment.

DResemble the structure and statistical properties of production data.

Answer : A

Training data is used to enable a model to detect and learn patterns. During the training phase, the model learns from the labeled data, identifying patterns and relationships that it will later use to make predictions on new, unseen data. This process is fundamental in building an AI model's capability to perform tasks accurately. Reference: AIGP Body of Knowledge on Model Training and Pattern Recognition.

Question 6

Which of the following is an example of a high-risk application under the EU Al Act?

AA resume scanning tool that ranks applicants.

BAn Al-enabled inventory management tool.

CA government-run social scoring tool.

DA customer service chatbot tool.

Answer : C

The EU AI Act categorizes certain applications of AI as high-risk due to their potential impact on fundamental rights and safety. High-risk applications include those used in critical areas such as employment, education, and essential public services. A government-run social scoring tool, which assesses individuals based on their social behavior or perceived trustworthiness, falls under this category because of its profound implications for privacy, fairness, and individual rights. This contrasts with other AI applications like resume scanning tools or customer service chatbots, which are generally not classified as high-risk under the EU AI Act.

Question 7

All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

AFines for SMEs and startups will be proportionally capped.

BRules on General Purpose Al will apply after 6 months as a specific provision.

CThe Al Pact will act as a transitional bridge until the Regulations are fully enacted.

DFines for violations of banned Al applications will be 35 million or 7% global annual turnover (whichever is higher).

Answer : C

The EU AI Act outlines specific penalties and enforcement mechanisms to ensure compliance with its regulations. Among these, fines for violations of banned AI applications can be as high as 35 million or 7% of the global annual turnover of the offending organization, whichever is higher. Proportional caps on fines are applied to SMEs and startups to ensure fairness. General Purpose AI rules are to apply after a 6-month period as a specific provision to ensure that stakeholders have adequate time to comply. However, there is no provision for an 'AI Pact' acting as a transitional bridge until the regulations are fully enacted, making option C the correct answer.

IAPP AIGP Artificial Intelligence Governance Professional Exam Practice Test