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AIGP Test Collection | Exam AIGP Bible

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IAPP Certified Artificial Intelligence Governance Professional Sample Questions (Q21-Q26):

NEW QUESTION # 21
Random forest algorithms are in what type of machine learning model?

A. Natural language processing.
B. Generative.
C. Symbolic.
D. Discriminative.

Answer: D

Explanation:
Random forest algorithms are classified as discriminative models. Discriminative models are used to classify data by learning the boundaries between classes, which is the core functionality of random forest algorithms.
They are used for classification and regression tasks by aggregating the results of multiple decision trees to make accurate predictions.
Reference: The AIGP Body of Knowledge explains that discriminative models, including random forest algorithms, are designed to distinguish between different classes in the data, making them effective for various predictive modeling tasks.

NEW QUESTION # 22
If it is possible to provide a rationale for a specific output of an Al system, that system can best be described as?

A. Transparent.
B. Reliable.
C. Explainable.
D. Accountable.

Answer: C

Explanation:
If it is possible to provide a rationale for a specific output of an AI system, that system can best be described as explainable. Explainability in AI refers to the ability to interpret and understand the decision-making process of the AI system. This involves being able to articulate the factors and logic that led to a particular output or decision. Explainability is critical for building trust, enabling users to understand and validate the AI system's actions, and ensuring compliance with ethical and regulatory standards. It also facilitates debugging and improving the system by providing insights into its behavior.

NEW QUESTION # 23
All of the following are penalties and enforcements outlined in the EU Al Act EXCEPT?

A. Fines for violations of banned Al applications will be €35 million or 7% global annual turnover (whichever is higher).
B. The Al Pact will act as a transitional bridge until the Regulations are fully enacted.
C. Fines for SMEs and startups will be proportionally capped.
D. Rules on General Purpose Al will apply after 6 months as a specific provision.

Answer: B

Explanation:
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.

NEW QUESTION # 24
What is the best method to proactively train an LLM so that there is mathematical proof that no specific piece of training data has more than a negligible effect on the model or its output?

A. Clustering.
B. Data compartmentalization.
C. Transfer learning.
D. Differential privacy.

Answer: D

Explanation:
Differential privacy is a technique used to ensure that the inclusion or exclusion of a single data point does not significantly affect the outcome of any analysis, providing a way to mathematically prove that no specific piece of training data has more than a negligible effect on the model or its output. This is achieved by introducing randomness into the data or the algorithms processing the data. In the context of training large language models (LLMs), differential privacy helps in protecting individual data points while still enabling the model to learn effectively. By adding noise to the training process, differential privacy provides strong guarantees about the privacy of the training data.
Reference: AIGP BODY OF KNOWLEDGE, pages related to data privacy and security in model training.

NEW QUESTION # 25
In the machine learning context, feature engineering is the process of?

A. Creating learning schema for a model apply.
B. Extracting attributes and variables from raw data.
C. Developing guidelines to train and test a model.
D. Converting raw data into clean data.

Answer: B

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.

NEW QUESTION # 26
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