FREE PDF QUIZ NEWEST IAPP - AIGP TEST REGISTRATION

Free PDF Quiz Newest IAPP - AIGP Test Registration

Free PDF Quiz Newest IAPP - AIGP Test Registration

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q65-Q70):

NEW QUESTION # 65
A company developed Al technology that can analyze text, video, images and sound to tag content, including the names of animals, humans and objects.
What type of Al is this technology classified as?

  • A. Transformative Al.
  • B. Expert system.
  • C. Deductive inference.
  • D. Multi-modal model.

Answer: D

Explanation:
A multi-modal model is an AI system that can process and analyze multiple types of data, such as text, video, images, and sound. This type of AI integrates different data sources to enhance its understanding and decision-making capabilities. In the given scenario, the AI technology that tags content including names of animals, humans, and objects falls under this category. Reference: AIGP BODY OF KNOWLEDGE, which outlines the capabilities and use cases of multi-modal models.


NEW QUESTION # 66
A company is creating a mobile app to enable individuals to upload images and videos, and analyze this data using ML to provide lifestyle improvement recommendations. The signup form has the following data fields:
1.First name
2.Last name
3.Mobile number
4.Email ID
5.New password
6.Date of birth
7.Gender
In addition, the app obtains a device's IP address and location information while in use.
What GDPR privacy principles does this violate?

  • A. Accountability and Lawfulness.
  • B. Transparency and Accuracy.
  • C. Integrity and Confidentiality.
  • D. Purpose Limitation and Data Minimization.

Answer: D

Explanation:
The GDPR privacy principles that this scenario violates are Purpose Limitation and Data Minimization.
Purpose Limitation requires that personal data be collected for specified, explicit, and legitimate purposes and not further processed in a manner that is incompatible with those purposes. Data Minimization mandates that personal data collected should be adequate, relevant, and limited to what is necessary in relation to the purposes for which they are processed. In this case, collecting extensive personal information (e.g., IP address, location, gender) and potentially using it beyond the necessary scope for the app's functionality could violate these principles by collecting more data than needed and possibly using it for purposes not originally intended.


NEW QUESTION # 67
What is the technique to remove the effects of improperly used data from an ML system?

  • A. Model inversion.
  • B. Data cleansing.
  • C. Data de-duplication.
  • D. Model disgorgement.

Answer: D

Explanation:
Model disgorgement is the technique used to remove the effects of improperly used data from an ML system.
This process involves retraining or adjusting the model to eliminate any biases or inaccuracies introduced by the inappropriate data. It ensures that the model's outputs are not influenced by data that was not meant to be used or was used incorrectly. Reference: AIGP Body of Knowledge on Data Management and Model Integrity.


NEW QUESTION # 68
You are part of your organization's ML engineering team and notice that the accuracy of a model that was recently deployed into production is deteriorating.
What is the best first step address this?

  • A. Run red-teaming exercises.
  • B. Conduct champion/challenger testing.
  • C. Replace the model with a previous version.
  • D. Perform an audit of the model.

Answer: B

Explanation:
When the accuracy of a model deteriorates, the best first step is to conduct champion/challenger testing. This involves deploying a new model (challenger) alongside the current model (champion) to compare their performance. This method helps identify if the new model can perform better under current conditions without immediately discarding the existing model. It provides a controlled environment to test improvements and understand the reasons behind the deterioration. This approach is preferable to directly replacing the model, performing audits, or running red-teaming exercises, which may be subsequent steps based on the findings from the champion/challenger testing.
Reference: AIGP BODY OF KNOWLEDGE, sections on model performance management and testing strategies.


NEW QUESTION # 69
CASE STUDY
Please use the following answer the next question:
ABC Corp, is a leading insurance provider offering a range of coverage options to individuals. ABC has decided to utilize artificial intelligence to streamline and improve its customer acquisition and underwriting process, including the accuracy and efficiency of pricing policies.
ABC has engaged a cloud provider to utilize and fine-tune its pre-trained, general purpose large language model ("LLM"). In particular, ABC intends to use its historical customer data-including applications, policies, and claims-and proprietary pricing and risk strategies to provide an initial qualification assessment of potential customers, which would then be routed a human underwriter for final review.
ABC and the cloud provider have completed training and testing the LLM, performed a readiness assessment, and made the decision to deploy the LLM into production. ABC has designated an internal compliance team to monitor the model during the first month, specifically to evaluate the accuracy, fairness, and reliability of its output. After the first month in production, ABC realizes that the LLM declines a higher percentage of women's loan applications due primarily to women historically receiving lower salaries than men.
Which of the following is the most important reason to train the underwriters on the model prior to deployment?

  • A. Tosolicit on-going feedback on model performance.
  • B. Toapply their own judgment to the initial assessment.
  • C. Toensure they provide transparency applicants on the model.
  • D. Toprovide a reminder of a right appeal.

Answer: B

Explanation:
Training underwriters on the model prior to deployment is crucial so they can apply their own judgment to the initial assessment. While AI models can streamline the process, human judgment is still essential to catch nuances that the model might miss or to account for any biases or errors in the model's decision-making process.
Reference: The AIGP Body of Knowledge emphasizes the importance of human oversight in AI systems, particularly in high-stakes areas such as underwriting and loan approvals. Human underwriters can provide a critical review and ensure that the model's assessments are accurate and fair, integrating their expertise and understanding of complex cases.


NEW QUESTION # 70
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