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
Which type of existing assessment could best be leveraged to create an Al impact assessment?
|
A. A safety impact assessment.
| B. A privacy impact assessment.
| C. A security impact assessment.
| D. An environmental impact assessment.
|
B. A privacy impact assessment.
Explanation:
A privacy impact assessment (PIA) can be effectively leveraged to create an AI impact assessment. A PIA evaluates the potential privacy risks associated with the use of personal data and helps in implementing measures to mitigate those risks. Since AI systems often involve processing large amounts of personal data, the principles and methodologies of a PIA are highly applicable and can be extended to assess broader impacts, including ethical, social, and legal implications of AI.
Reference:
AIGP Body of Knowledge on Impact Assessments.
Question # 3
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.
Question # 4
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. Replace the model with a previous version.
| B. Conduct champion/challenger testing.
| C. Perform an audit of the model.
| D. Run red-teaming exercises.
|
B. Conduct champion/challenger testing.
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., , ]
Question # 5
In the machine learning context, feature engineering is the process of?
|
A. Converting raw data into clean data.
| B. Creating learning schema for a model apply.
| C. Developing guidelines to train and test a model.
| D. Extracting attributes and variables from raw data.
|
D. Extracting attributes and variables from raw data.
Explanation:
In the machine learning context, feature engineering is the process of extracting attributes and variables from raw data to make it suitable for training an AI model. This step is crucial as it transforms raw data into meaningful features that can improve the model's accuracy and performance. Feature engineering involves selecting, modifying, and creating new features that help the model learn more effectively.
Reference:
AIGP Body of Knowledge on AI Model Development and Feature Engineering.
Question # 6
Pursuant to the White House Executive Order of November 2023, who is responsible for creating guidelines to conduct red-teaming tests of Al systems?
|
A. National Institute of Standards and Technology (NIST).
| B. National Science and Technology Council (NSTC).
| C. Office of Science and Technology Policy (OSTP).
| D. Department of Homeland Security (DHS).
|
A. National Institute of Standards and Technology (NIST).
Explanation:
The White House Executive Order of November 2023 designates the National Institute of Standards and Technology (NIST) as the responsible body for creating guidelines to conduct red-teaming tests of AI systems. NIST is tasked with developing and providing standards and frameworks to ensure the security, reliability, and ethical deployment of AI systems, including conducting rigorous red-teaming exercises to identify vulnerabilities and assess risks in AI systems.
[Reference: AIGP BODY OF KNOWLEDGE, sections on AI governance and regulatory frameworks, and the White House Executive Order of November 2023., , ]
Question # 7
What is the term for an algorithm that focuses on making the best choice achieve an immediate objective at a particular step or decision point, based on the available information and without regard for the longer-term best solutions?
|
A. Single-lane.
| B. Optimized.
| C. Efficient.
| D. Greedy.
|
D. Greedy.
Explanation:
A greedy algorithm is one that makes the best choice at each step to achieve an immediate objective, without considering the longer-term consequences. It focuses on local optimization at each decision point with the hope that these local solutions will lead to an optimal global solution. However, greedy algorithms do not always produce the best overall solution for certain problems, but they are useful when an immediate, locally optimal solution is desired. Reference: AIGP Body of Knowledge, algorithm types section.
Question # 8
What is the primary purpose of conducting ethical red-teaming on an Al system?
|
A. To improve the model's accuracy.
| B. To simulate model risk scenarios.
| C. To identify security vulnerabilities.
| D. To ensure compliance with applicable law.
|
B. To simulate model risk scenarios.
Explanation:
The primary purpose of conducting ethical red-teaming on an AI system is to simulate model risk scenarios. Ethical red-teaming involves rigorously testing the AI system to identify potential weaknesses, biases, and vulnerabilities by simulating real-world attack or failure scenarios. This helps in proactively addressing issues that could compromise the system's reliability, fairness, and security.
Reference:
AIGP Body of Knowledge on AI Risk Management and Ethical AI Practices.
Question # 9
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 # 10
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?
|
A. Perform a readiness assessment.
| B. Define a model-validation methodology.
| C. Document maintenance teams and processes.
| D. Identify known edge cases to monitor post-deployment.
|
A. Perform a readiness assessment.
Explanation:
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.
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