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NEW QUESTION # 23
What is the primary purpose of an Al impact assessment?
- A. To define and evaluate the legal risks associated with developing an Al system.
- B. Anticipate and manage the potential risks and harms of an Al system.
- C. To identify and measure the benefits of an Al system.
- D. To define and document the roles and responsibilities of Al stakeholders.
Answer: B
Explanation:
The primary purpose of an AI impact assessment is to anticipate and manage the potential risks and harms of an AI system. This includes identifying the possible negative outcomes and implementing measures to mitigate these risks. This process helps ensure that AI systems are developed and deployed in a manner that is ethically and socially responsible, addressing concerns such as bias, fairness, transparency, and accountability.
The assessment often involves a thorough evaluation of the AI system's design, data inputs, outputs, and the potential impact on various stakeholders. This approach is crucial for maintaining public trust and adherence to regulatory requirements.
NEW QUESTION # 24
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. Purpose Limitation and Data Minimization.
- B. Transparency and Accuracy.
- C. Integrity and Confidentiality.
- D. Accountability and Lawfulness.
Answer: A
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 # 25
The planning phase of the Al life cycle articulates all of the following EXCEPT the?
- A. Choice of the architecture.
- B. Approach to governance.
- C. Context in which the model will operate.
- D. Objective of the model.
Answer: B
Explanation:
The planning phase of the AI life cycle typically includes defining the objective of the model, choosing the appropriate architecture, and understanding the context in which the model will operate. However, the approach to governance is usually established as part of the overall AI governance framework, not specifically within the planning phase. Governance encompasses broader organizational policies and procedures that ensure AI development and deployment align with legal, ethical, and operational standards. Reference: AIGP Body of Knowledge, AI lifecycle planning phase section.
NEW QUESTION # 26
Which type of existing assessment could best be leveraged to create an Al impact assessment?
- A. A privacy impact assessment.
- B. An environmental impact assessment.
- C. A safety impact assessment.
- D. A security impact assessment.
Answer: A
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.
NEW QUESTION # 27
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
Which of the following risks should be of the highest concern to individual teachers using generative Al to ensure students learn the course material?
- A. Model accuracy.
- B. Technical complexity.
- C. Copyright infringement.
- D. Financial cost.
Answer: A
Explanation:
The highest concern for individual teachers using generative AI to ensure students learn the course material is model accuracy. Ensuring that the AI-generated content is accurate and relevant to the curriculum is crucial for effective learning. If the AI model produces inaccurate or irrelevant content, it can mislead students and hinder their understanding of the subject matter.
Reference: According to the AIGP Body of Knowledge, one of the core risks posed by AI systems is the accuracy of the data and models used. Ensuring the accuracy of AI-generated content is essential for maintaining the integrity of the educational material and achieving the desired learning outcomes.
NEW QUESTION # 28
The OECD's Ethical Al Governance Framework is a self-regulation model that proposes to prevent societal harms by?
- A. Focusing on Al technical design and post-deployment monitoring.
- B. Defining requirements specific to each industry sector and high-risk Al domain.
- C. Establishing explain ability criteria to responsibly source and use data to train Al systems.
- D. Balancing Al innovation with ethical considerations.
Answer: D
Explanation:
The OECD's Ethical AI Governance Framework aims to ensure that AI development and deployment are carried out ethically while fostering innovation. The framework includes principles like transparency, accountability, and human rights protections to prevent societal harm. It does not focus solely on technical design or post-deployment monitoring (C), nor does it establish industry-specific requirements (B). While explainability is important, the primary goal is to balance innovation with ethical considerations (D).
NEW QUESTION # 29
To maintain fairness in a deployed system, it is most important to?
- A. Detect anomalies outside established metrics that require new training data.
- B. Protect against loss of personal data in the model.
- C. Monitor for data drift that may affect performance and accuracy.
- D. Optimize computational resources and data to ensure efficiency and scalability.
Answer: C
NEW QUESTION # 30
All of the following are common optimization techniques in deep learning to determine weights that represent the strength of the connection between artificial neurons EXCEPT?
- A. Momentum, which improves the convergence speed and stability of neural network training.
- B. Backpropagation, which starts from the last layer working backwards.
- C. Autoregression, which analyzes and makes predictions about time-series data.
- D. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
Answer: C
Explanation:
Autoregression is not a common optimization technique in deep learning to determine weights for artificial neurons. Common techniques include gradient descent, momentum, and backpropagation. Autoregression is more commonly associated with time-series analysis and forecasting rather than neural network optimization.
Reference: AIGP BODY OF KNOWLEDGE, which discusses common optimization techniques used in deep learning.
NEW QUESTION # 31
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?
- A. Reinforced.
- B. Resilient.
- C. Robust.
- D. Reliable.
Answer: B
Explanation:
An AI system that maintains its level of performance within defined acceptable limits despite real-world or adversarial conditions is described as resilient. Resilience in AI refers to the system's ability to withstand and recover from unexpected challenges, such as cyber-attacks, hardware failures, or unusual input data. This characteristic ensures that the AI system can continue to function effectively and reliably in various conditions, maintaining performance and integrity. Robustness, on the other hand, focuses on the system's strength against errors, while reliability ensures consistent performance over time. Resilience combines these aspects with the capacity to adapt and recover.
NEW QUESTION # 32
CASE STUDY
Please use the following answer the next question:
Good Values Corporation (GVC) is a U.S. educational services provider that employs teachers to create and deliver enrichment courses for high school students. GVC has learned that many of its teacher employees are using generative Al to create the enrichment courses, and that many of the students are using generative Al to complete their assignments.
In particular, GVC has learned that the teachers they employ used open source large language models ("LLM") to develop an online tool that customizes study questions for individual students. GVC has also discovered that an art teacher has expressly incorporated the use of generative Al into the curriculum to enable students to use prompts to create digital art.
GVC has started to investigate these practices and develop a process to monitor any use of generative Al, including by teachers and students, going forward.
What is the best reason for GVC to offer students the choice to utilize generative Al in limited, defined circumstances?
- A. Toenable students to learn how to use Al as a supportive educational tool.
- B. Toenable students to learn how to manage their time.
- C. Toenable students to learn about practical applications of Al.
- D. Toenable students to learn about performing research.
Answer: A
Explanation:
The best reason for GVC to offer students the choice to utilize generative AI in limited, defined circumstances is to enable students to learn how to use AI as a supportive educational tool. By integrating AI in a controlled manner, students can learn the practical applications of AI and develop skills to use AI responsibly and effectively in their educational pursuits.
Reference: The AIGP Body of Knowledge highlights the importance of teaching students about AI's practical applications and the responsible use of AI technologies. This aligns with the goal of fostering a better understanding of AI's role and its potential benefits in various contexts, including education.
NEW QUESTION # 33
According to the EU Al Act, providers of what kind of machine learning systems will be required to register with an EU oversight agency before placing their systems in the EU market?
- A. Al systems that are "strong" general intelligence.
- B. Al systems that are harmful based on a legal risk-utility calculation.
- C. Al systems trained on sensitive personal data.
- D. Al systems that are high-risk.
Answer: D
Explanation:
According to the EU AI Act, providers of high-risk AI systems are required to register with an EU oversight agency before these systems can be placed on the market. This requirement is part of the Act's framework to ensure that high-risk AI systems comply with stringent safety, transparency, and accountability standards.
High-risk systems are those that pose significant risks to health, safety, or fundamental rights. Registration with oversight agencies helps facilitate ongoing monitoring and enforcement of compliance with the Act's provisions. Systems categorized under other criteria, such as those trained on sensitive personal data or exhibiting "strong" general intelligence, also fall under scrutiny but are primarily covered under different regulatory requirements or classifications.
NEW QUESTION # 34
A company plans on procuring a tool from an Al provider for its employees to use for certain business purposes.
Which contractual provision would best protect the company's intellectual property in the tool, including training and testing data?
- A. The provider willwarrant that the tool will work as intended.
- B. The provider willgive privacy notice to individuals before using their personal data to train or test the tool.
- C. The provider willdefend and indemnify the company against infringement claims.
- D. The provider willobtain and maintain insurance to cover potential claims.
Answer: C
Explanation:
To protect the company's intellectual property, the most pertinent contractual provision is ensuring that the AI provider will defend and indemnify the company against infringement claims. This clause means the provider will take responsibility for any intellectual property disputes that arise, thereby safeguarding the company from potential legal and financial repercussions related to the use of the tool. Other options, while beneficial, do not directly address the protection of intellectual property. This concept is detailed in the contractual best practices section of the IAPP AIGP Body of Knowledge.
NEW QUESTION # 35
The most important factor in ensuring fairness when training an Al system is?
- A. The data labeling and classification.
- B. The architecture and model selection.
- C. The model accuracy and scale.
- D. The data attributes and variability.
Answer: D
Explanation:
Ensuring fairness when training an AI system largely depends on the data attributes and variability. This involves having a diverse and representative dataset that accurately reflects the population the AI system will serve. Fairness can be compromised if the data is biased or lacks variability, as the model may learn and perpetuate these biases. Diverse data attributes ensure that the model learns from a wide range of examples, reducing the risk of biased predictions. Reference: AIGP Body of Knowledge on Ethical AI Principles and Data Management.
NEW QUESTION # 36
All of the following may be permissible uses of an Al system under the EU Al Act EXCEPT?
- A. To implement social scoring.
- B. To promote equitable distribution of welfare benefits.
- C. To manage border control.
- D. To detect an individual's intent for law enforcement purposes.
Answer: A
Explanation:
The EU AI Act explicitly prohibits the use of AI systems for social scoring by public authorities, as it can lead to discrimination and unfair treatment of individuals based on their social behavior or perceived trustworthiness. While AI can be used to promote equitable distribution of welfare benefits, manage border control, and even detect an individual's intent for law enforcement purposes (within strict regulatory and ethical boundaries), implementing social scoring systems is not permissible under the Act due to the significant risks to fundamental rights and freedoms.
NEW QUESTION # 37
Training data is best defined as a subset of data that is used to?
- A. Resemble the structure and statistical properties of production data.
- B. Detect the initial sources of biases to mitigate prior to deployment.
- C. Fine-tune a model to improve accuracy and prevent overfitting.
- D. Enable a model to detect and learn patterns.
Answer: D
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.
NEW QUESTION # 38
You are an engineer that developed an Al-based ad recommendation tool.
Which of the following should be monitored to evaluate the tool's effectiveness?
- A. Output data, assess the delta between the prediction and actual ad clicks.
- B. Input data, to ensure the ads are reaching the target audience.
- C. GPU performance, to evaluate the tool's robustness.
- D. Algorithmic patterns, to show the model has a high degree of accuracy.
Answer: A
Explanation:
To evaluate the effectiveness of an AI-based ad recommendation tool, the most relevant metric is the output data, specifically assessing the delta between the prediction and actual ad clicks. This metric directly measures the tool's accuracy and effectiveness in making accurate recommendations that lead to user engagement. While monitoring algorithmic patterns and input data can provide insights into the model's behavior and targeting accuracy, and GPU performance can indicate the robustness and efficiency of the tool, the primary indicator of effectiveness for an ad recommendation tool is how well it predicts actual ad clicks.
Reference: AIGP BODY OF KNOWLEDGE, sections on AI performance metrics and evaluation methods.
NEW QUESTION # 39
A company has trained an ML model primarily using synthetic data, and now intends to use live personal data to test the model.
Which of the following is NOT a best practice apply during the testing?
- A. The test data should be anonymized to the extent practicable.
- B. Testing should be performed specific to the intended uses.
- C. Testing should minimize human involvement to the extent practicable.
- D. The test data should be representative of the expected operationaldata.
Answer: C
Explanation:
Minimizing human involvement to the extent practicable is not a best practice during the testing of an ML model. Human oversight is crucial during testing to ensure that the model performs correctly and ethically, and to interpret any anomalies or issues that arise. Best practices include using representative test data, anonymizing data to the extent practicable, and performing testing specific to the intended uses of the model.
Reference: AIGP Body of Knowledge on AI Model Testing and Human Oversight.
NEW QUESTION # 40
All of the following are reasons to deploy a challenger Al model in addition a champion Al model EXCEPT to?
- A. Automate real-time monitoring of the champion model.
- B. Provide a framework to consider alternatives to the champion model.
- C. Retrain the champion model.
- D. Perform testing on the champion model.
Answer: C
Explanation:
Deploying a challenger AI model alongside a champion model is a strategy used to compare the performance of different models in a real-world environment. This approach helps in providing a framework to consider alternatives to the champion model, automating real-time monitoring of the champion model, and performing testing on the champion model. However, retraining the champion model is not a reason to deploy a challenger model. Retraining is a separate process that involves updating the champion model with new data or techniques, which is not related to the use of a challenger model.
Reference: AIGP BODY OF KNOWLEDGE, sections on model evaluation and management.
NEW QUESTION # 41
All of the following types of testing can help evaluate the performance of a responsible Al system EXCEPT?
- A. Decision analysis.
- B. Risk probability/severity.
- C. Statistical sampling.
- D. Adversarial robustness.
Answer: B
Explanation:
Risk probability/severity testing is not typically used to evaluate the performance of an AI system. While important for risk management, it does not directly assess an AI system's operational performance. Adversarial robustness, statistical sampling, and decision analysis are all methods that can help evaluate the performance of a responsible AI system by testing its resilience, accuracy, and decision-making processes under various conditions. Reference: AIGP Body of Knowledge on AI Performance Evaluation and Testing.
NEW QUESTION # 42
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. Run red-teaming exercises.
- C. Perform an audit of the model.
- D. Conduct champion/challenger testing.
Answer: D
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 # 43
Machine learning is best described as a type of algorithm by which?
- A. Systems can mimic human intelligence with the goal of replacing humans.
- B. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
- C. Systems can automatically improve from experience through predictive patterns.
- D. Statistical inferences are drawn from a sample with the goal of predicting human intelligence.
Answer: C
Explanation:
Machine learning (ML) is a subset of artificial intelligence (AI) where systems use data to learn and improve over time without being explicitly programmed. Option B accurately describes machine learning by stating that systems can automatically improve from experience through predictive patterns. This aligns with the fundamental concept of ML where algorithms analyze data, recognize patterns, and make decisions with minimal human intervention. Reference: AIGP BODY OF KNOWLEDGE, which covers the basics of AI and machine learning concepts.
NEW QUESTION # 44
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