Question # 1
| What is the key feature of Graphical Processing Units (GPUs) that makes them well-suited to running Al applications? | A. GPUs run many tasks concurrently, resulting in faster processing.
| B. GPUs can access memory quickly, resulting in lower latency than CPUs.
| C. GPUs can run every task on a computer, making them more robust than CPUs.
| D. The number of transistors on GPUs doubles every two years, making thechips smaller and lighter.
|
A. GPUs run many tasks concurrently, resulting in faster processing.
Explanation:
GPUs (Graphical Processing Units) are well-suited to running AI applications due to their ability to run many tasks concurrently, which significantly enhances processing speed. This parallel processing capability makes GPUs ideal for handling the large-scale computations required in AI and deep learning tasks.
Reference:
AIGP BODY OF KNOWLEDGE, which explains the importance of compute infrastructure in AI applications.
Question # 2
A company initially intended to use a large data set containing personal information to train an Al model. After consideration, the company determined that it can derive enough value from the data set without any personal information and permanently obfuscated all personal data elements before training the model.
This is an example of applying which privacy-enhancing technique (PET)?
| A. Anonymization.
| B. Pseudonymization.
| C. Differential privacy.
| D. Federated learning.
|
A. Anonymization.
Explanation:
Anonymization is a privacy-enhancing technique that involves removing or permanently altering personal data elements to prevent the identification of individuals. In this case, the company obfuscated all personal data elements before training the model, which aligns with the definition of anonymization. This ensures that the data cannot be traced back to individuals, thereby protecting their privacy while still allowing the company to derive value from the dataset.
Reference:
AIGP Body of Knowledge, privacy-enhancing techniques section.
Question # 3
What is the best reason for a company adopt a policy that prohibits the use of generative Al?
| | A. Avoid using technology that cannot be monetized. | B. Avoid needing to identify and hire qualified resources.
| C. Avoid the time necessary to train employees on acceptable use.
| D. Avoid accidental disclosure to its confidential and proprietary information.
|
D. Avoid accidental disclosure to its confidential and proprietary information.
Explanation:
The primary concern for a company adopting a policy prohibiting the use of generative AI is the risk of accidental disclosure of confidential and proprietary information. Generative AI tools can inadvertently leak sensitive data during the creation process or through data sharing. This risk outweighs the other reasons listed, as protecting sensitive information is critical to maintaining the company’s competitive edge and legal compliance. This rationale is discussed in the sections on risk management and data privacy in the IAPP AIGP Body of Knowledge.
Question # 4
A company is working to develop a self-driving car that can independently decide the appropriate route to take the driver after the driver provides an address.
If they want to make this self-driving car “strong” Al, as opposed to "weak,” the engineers would also need to ensure? | | A. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
| | B. That they have obtained appropriate intellectual property (IP) licenses to use data for training the Al.
| | C. That the Al has strong cybersecurity to prevent malicious actors from taking control of the car.
| | D. That the Al can differentiate among ethnic backgrounds of pedestrians.
|
A. Thatthe Al has full human cognitive abilities that can independently decide where to take the driver.
Question # 5
| Training data is best defined as a subset of data that is used to? | A. Enable a model to detect and learn patterns.
| B. Fine-tune a model to improve accuracy and prevent overfitting.
| C. Detect the initial sources of biases to mitigate prior to deployment.
| D. Resemble the structure and statistical properties of production data.
|
A. Enable a model to detect and learn patterns.
Explanation:
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
To maintain fairness in a deployed system, it is most important to?
| A. Protect against loss of personal data in the model.
| B. Monitor for data drift that may affect performance and accuracy.
| C. Detect anomalies outside established metrics that require new training data.
| D. Optimize computational resources and data to ensure efficiency and scalability.
|
B. Monitor for data drift that may affect performance and accuracy.
Explanation:
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 # 7
Which of the following would be the least likely step for an organization to take when designing an integrated compliance strategy for responsible Al?
| A. Conducting an assessment of existing compliance programs to determine overlaps and integration points.
| B. Employing a new software platform to modernize existing compliance processes across the organization.
| C. Consulting experts to consider the ethical principles underpinning the use of Al within the organization.
| D. Launching a survey to understand the concerns and interests of potentially impacted stakeholders.
|
B. Employing a new software platform to modernize existing compliance processes across the organization.
Explanation:
When designing an integrated compliance strategy for responsible AI, the least likely step would be employing a new software platform to modernize existing compliance processes. While modernizing compliance processes is beneficial, it is not as directly related to the strategic integration of ethical principles and stakeholder concerns. More critical steps include conducting assessments of existing compliance programs to identify overlaps and integration points, consulting experts on ethical principles, and launching surveys to understand stakeholder concerns. These steps ensure that the compliance strategy is comprehensive and aligned with responsible AI principles.
Reference:
AIGP Body of Knowledge on AI Governance and Compliance Integration.
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