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PASS RATE Artificial Intelligence Governance AIGP Certified Exam DUMP
IAPP AIGP Exam Syllabus Topics:
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NEW QUESTION # 46
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
What is the best reason the police department should continue to perform investigations even if the Al system scores an individual's likelihood of criminal activity at or above 90%?
- A. Because investigations may uncover information relevant to sentencing.
- B. Because Al systems that affect fundamental civil rights should not be fully automated.
- C. Because the department did not perform an impact assessment for this intended use.
- D. Because investigations may identify additional individuals involved in the crime.
Answer: B
Explanation:
The best reason for the police department to continue performing investigations even if the AI system scores an individual's likelihood of criminal activity at or above 90% is that AI systems affecting fundamental civil rights should not be fully automated. Human oversight is essential to ensure that decisions impacting civil liberties are made with due consideration of context and mitigating factors that an AI might not fully appreciate. This approach ensures fairness, accountability, and adherence to legal standards. Reference: AIGP Body of Knowledge on AI Ethics and Human Oversight.
NEW QUESTION # 47
An artist has been using an Al tool to create digital art and would like to ensure that it has copyright protection in the United States.
Which of the following is most likely to enable the artist to receive copyright protection?
- A. Ensure the tool was trained using publicly available content.
- B. Update the images in a creative way to demonstrate that it is the artist's.
- C. Provide a log of the prompts the artist used to generate the images.
- D. Obtain a representation from the Al provider on how the tool works.
Answer: B
Explanation:
For the artist to receive copyright protection, the most effective approach is to demonstrate that the final artwork includes sufficient creative input by the artist. By updating or altering the images in a way that reflects the artist's personal creativity, the artist can claim originality, which is a core requirement for copyright protection under U.S. law. The other options do not directly address the originality and creative input required for copyright. This is highlighted in the sections on copyright protection in the IAPP AIGP Body of Knowledge.
NEW QUESTION # 48
When monitoring the functional performance of a model that has been deployed into production, all of the following are concerns EXCEPT?
- A. Model drift.
- B. System cost.
- C. Data loss.
- D. Feature drift.
Answer: B
Explanation:
When monitoring the functional performance of a model deployed into production, concerns typically include feature drift, model drift, and data loss. Feature drift refers to changes in the input features that can affect the model's predictions. Model drift is when the model's performance degrades over time due to changes in the data or environment. Data loss can impact the accuracy and reliability of the model. However, system cost, while important for budgeting and financial planning, is not a direct concern when monitoring the functional performance of a deployed model. Reference: AIGP Body of Knowledge on Model Monitoring and Maintenance.
NEW QUESTION # 49
All of the following are included within the scope of post-deployment Al maintenance EXCEPT?
- A. Defining thresholds to conduct new impact assessments.
- B. Dedicating experts to continually monitor the model output.
- C. Evaluating the need for an audit under certain standards.
- D. Ensuring that all model components are subject a control framework.
Answer: A
Explanation:
Post-deployment AI maintenance typically includes ensuring that all model components are subject to a control framework, dedicating experts to continually monitor the model output, and evaluating the need for audits under certain standards. However, defining thresholds to conduct new impact assessments is usually part of the initial deployment and ongoing governance processes rather than a maintenance activity.
Maintenance focuses more on the operational aspects of the AI system rather than setting new thresholds for impact assessments.
Reference: AIGP BODY OF KNOWLEDGE, sections discussing AI lifecycle management and post-deployment activities.
NEW QUESTION # 50
According to the Singapore Model Al Governance Framework, all of the following are recommended measures to promote the responsible use of Al EXCEPT?
- A. Establishing communications and collaboration among stakeholders.
- B. Determining the level of human involvement in algorithmic decision-making.
- C. Employing human-over-the-loop protocols for high-risk systems.
- D. Adapting the existing governance structure algorithmic decision-making.
Answer: C
Explanation:
The Singapore Model AI Governance Framework recommends several measures to promote the responsible use of AI, such as determining the level of human involvement in decision-making, adapting governance structures, and establishing communications and collaboration among stakeholders. However, employing human-over-the-loop protocols is not specifically mentioned in this framework. The focus is more on integrating human oversight appropriately within the decision-making process rather than exclusively employing such protocols. Reference: AIGP Body of Knowledge, section on AI governance frameworks.
NEW QUESTION # 51
Under the Canadian Artificial Intelligence and Data Act, when must the Minister of Innovation, Science and Industry be notified about a high-impact Al system?
- A. When the algorithmic impact assessment has been completed.
- B. Upon release of a new version of the system.
- C. When use of the system causes or is likely to cause material harm.
- D. Upon initial deployment of the system.
Answer: D
Explanation:
According to the Canadian Artificial Intelligence and Data Act, high-impact AI systems must notify the Minister of Innovation, Science and Industry upon initial deployment. This requirement ensures that the authorities are aware of the deployment of significant AI systems and can monitor their impacts and compliance with regulatory standards from the outset. This initial notification is crucial for maintaining oversight and ensuring the responsible use of AI technologies. Reference: AIGP Body of Knowledge, domain on AI laws and standards.
NEW QUESTION # 52
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
In the design phase, which of the following steps is most important in gathering the data from the clinical research partner?
- A. Review the terms of use.
- B. Perform a privacy impact assessment.
- C. Combine only anonymized data.
- D. Segregate the data sets.
Answer: A
Explanation:
Reviewing the terms of use is essential when gathering data from a clinical research partner. This step ensures that the healthcare network complies with all legal and contractual obligations related to data usage. It addresses data ownership, usage limitations, consent requirements, and privacy obligations, which are critical to maintaining ethical standards and avoiding legal repercussions. This review helps ensure that the data is used in a manner consistent with the agreements made and the regulatory environment, which is fundamental for lawful and ethical AI development. Reference: AIGP Body of Knowledge on Legal and Regulatory Considerations.
NEW QUESTION # 53
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. Conduct champion/challenger testing.
- B. Replace the model with a previous version.
- C. Perform an audit of the model.
- D. Run red-teaming exercises.
Answer: A
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 # 54
You are the chief privacy officer of a medical research company that would like to collect and use sensitive data about cancer patients, such as their names, addresses, race and ethnic origin, medical histories, insurance claims, pharmaceutical prescriptions, eating and drinking habits and physical activity.
The company will use this sensitive data to build an Al algorithm that will spot common attributes that will help predict if seemingly healthy people are more likely to get cancer. However, the company is unable to obtain consent from enough patients to sufficiently collect the minimum data to train its model.
Which of the following solutions would most efficiently balance privacy concerns with the lack of available data during the testing phase?
- A. Utilize synthetic data to offset the lack of patient data.
- B. Extend the model to multi-modal ingestion with text and images.
- C. Deploy the current model and recalibrate it over time with more data.
- D. Refocus the algorithm to patients without cancer.
Answer: A
Explanation:
Utilizing synthetic data to offset the lack of patient data is an efficient solution that balances privacy concerns with the need for sufficient data to train the model. Synthetic data can be generated to simulate real patient data while avoiding the privacy issues associated with using actual patient data. This approach allows for the development and testing of the AI algorithm without compromising patient privacy, and it can be refined with real data as it becomes available. Reference: AIGP Body of Knowledge on Data Privacy and AI Model Training.
NEW QUESTION # 55
What is the 1956 Dartmouth summer research project on Al best known as?
- A. A meeting focused on the impacts of the launch of the first mass-produced computer.
- B. A research project on the impacts of technology on society.
- C. A research project to create a test for machine intelligence.
- D. A meeting focused on the founding of the Al field.
Answer: D
Explanation:
The 1956 Dartmouth summer research project on AI is best known as a meeting focused on the founding of the AI field. This conference is historically significant because it marked the formal beginning of artificial intelligence as an academic discipline. The term "artificial intelligence" was coined during this event, and it laid the foundation for future research and development in AI.
Reference: The AIGP Body of Knowledge highlights the importance of the Dartmouth Conference as a pivotal moment in the history of AI, which established AI as a distinct field of study and research.
NEW QUESTION # 56
Which of the following Al uses is best described as human-centric?
- A. Virtual assistants are used adapt educational content and teaching methods to individuals, offering personalized recommendations based on ability and needs.
- B. Pattern recognition algorithms are used to improve the accuracy of weather predictions, which benefits many industries and everyday life.
- C. Machine learning is used for demand forecasting and inventory management, ensuring that consumers can find products they want when they want them.
- D. Autonomous robots are used to move products within a warehouse, allowing human workers to reduce physical strain and alleviate monotony.
Answer: A
Explanation:
Human-centric AI focuses on improving the human experience by addressing individual needs and enhancing human capabilities. Option D exemplifies this by using virtual assistants to tailor educational content to each student's unique abilities and needs, thereby supporting personalized learning and improving educational outcomes. This use case directly benefits individuals by providing customized assistance and adapting to their learning pace and style, aligning with the principles of human-centric AI.
Reference: AIGP BODY OF KNOWLEDGE, sections on trustworthy AI and human-centric AI principles.
NEW QUESTION # 57
An Al system that maintains its level of performance within defined acceptable limits despite real world or adversarial conditions would be described as?
- A. Reliable.
- B. Robust.
- C. Reinforced.
- D. Resilient.
Answer: D
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 # 58
The White House Executive Order from November 2023 requires companies that develop dual-use foundation models to provide reports to the federal government about all of the following EXCEPT?
- A. Any environmental impact study for each dual-use foundation model.
- B. The physical and cybersecurity protection measures of their dual-use foundation models.
- C. Any current training or development of dual-use foundation models.
- D. The results of red-team testing of each dual-use foundation model.
Answer: A
Explanation:
The White House Executive Order from November 2023 requires companies developing dual-use foundation models to report on their current training or development activities, the results of red-team testing, and the physical and cybersecurity protection measures. However, it does not mandate reports on environmental impact studies for each dual-use foundation model. While environmental considerations are important, they are not specified in this context as a reporting requirement under this Executive Order.
Reference: AIGP BODY OF KNOWLEDGE, sections on compliance and reporting requirements, and the White House Executive Order of November 2023.
NEW QUESTION # 59
According to the GDPR's transparency principle, when an Al system processes personal data in automated decision-making, controllers are required to provide data subjects specific information on?
- A. The contact details of the data protection officer and the data protection national authority.
- B. The existence of automated decision-making and meaningful information on its logic and consequences.
- C. The personal data used during processing, including inferences drawn by the Al system about the data.
- D. The data protection impact assessments carried out on the Al system and legal bases for processing.
Answer: B
Explanation:
The GDPR's transparency principle requires that when personal data is processed for automated decision-making, including profiling, data subjects must be informed about the existence of such automated decision-making. Additionally, they must be provided with meaningful information about the logic involved, as well as the significance and the envisaged consequences of such processing for them. This requirement ensures that data subjects are fully aware of how their personal data is being used and the potential impacts, thereby promoting transparency and trust in the processing activities.
NEW QUESTION # 60
CASE STUDY
Please use the following answer the next question:
A local police department in the United States procured an Al system to monitor and analyze social media feeds, online marketplaces and other sources of public information to detect evidence of illegal activities (e.g., sale of drugs or stolen goods). The Al system works by surveilling the public sites in order to identify individuals that are likely to have committed a crime. It cross-references the individuals against data maintained by law enforcement and then assigns a percentage score of the likelihood of criminal activity based on certain factors like previous criminal history, location, time, race and gender.
The police department retained a third-party consultant assist in the procurement process, specifically to evaluate two finalists. Each of the vendors provided information about their system's accuracy rates, the diversity of their training data and how their system works. The consultant determined that the first vendor's system has a higher accuracy rate and based on this information, recommended this vendor to the police department.
The police department chose the first vendor and implemented its Al system. As part of the implementation, the department and consultant created a usage policy for the system, which includes training police officers on how the system works and how to incorporate it into their investigation process.
The police department has now been using the Al system for a year. An internal review has found that every time the system scored a likelihood of criminal activity at or above 90%, the police investigation subsequently confirmed that the individual had, in fact, committed a crime. Based on these results, the police department wants to forego investigations for cases where the Al system gives a score of at least 90% and proceed directly with an arrest.
The best human oversight mechanism for the police department to implement is that a police officer should?
- A. Explain to the accused how the Al system works.
- B. Ensure an accused is given notice that the Al system was used.
- C. Consider the Al recommendation as part of the criminal investigation.
- D. Confirm the Al recommendation prior to sentencing.
Answer: C
Explanation:
The best human oversight mechanism for the police department to implement is for a police officer to consider the AI recommendation as part of the criminal investigation. This ensures that the AI system's output is used as a tool to aid human decision-making rather than replace it. The police officer should integrate the AI's insights with other evidence and contextual information to make informed decisions, maintaining a balance between technological aid and human judgment. Reference: AIGP Body of Knowledge on AI Integration and Human Oversight.
NEW QUESTION # 61
Machine learning is best described as a type of algorithm by which?
- A. Previously unknown properties are discovered in data and used to predict and make improvements in the data.
- B. Systems can mimic human intelligence with the goal of replacing humans.
- 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 # 62
What is the primary purpose of an Al impact assessment?
- A. Anticipate and manage the potential risks and harms of an Al system.
- B. To define and evaluate the legal risks associated with developing 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: A
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 # 63
During the development of semi-autonomous vehicles, various failures occurred as a result of the sensors misinterpreting environmental surroundings, such as sunlight.
These failures are an example of?
- A. Forgetting.
- B. Brittleness.
- C. Hallucination.
- D. Uncertainty.
Answer: B
Explanation:
The failures in semi-autonomous vehicles due to sensors misinterpreting environmental surroundings, such as sunlight, are examples of brittleness. Brittleness in AI systems refers to their inability to handle variations in input data or unexpected conditions, leading to failures when the system encounters situations that were not adequately covered during training. These systems perform well under specific conditions but fail when those conditions change. Reference: AIGP Body of Knowledge on AI System Robustness and Failures.
NEW QUESTION # 64
Retraining an LLM can be necessary for all of the following reasons EXCEPT?
- A. To ensure interpretability of the model's predictions.
- B. To minimize degradation in prediction accuracy due tochanges in data.
- C. Adjust the model's hyper parameters specific use case.
- D. Account for new interpretations of the same data.
Answer: A
Explanation:
Retraining an LLM (Large Language Model) is primarily done to improve or maintain its performance as data changes over time, to fine-tune it for specific use cases, and to incorporate new data interpretations to enhance accuracy and relevance. However, ensuring interpretability of the model's predictions is not typically a reason for retraining. Interpretability relates to how easily the outputs of the model can be understood and explained, which is generally addressed through different techniques or methods rather than through the retraining process itself. References to this can be found in the IAPP AIGP Body of Knowledge discussing model retraining and interpretability as separate concepts.
NEW QUESTION # 65
Which of the following is NOT a common type of machine learning?
- A. Cognitive learning.
- B. Deep learning.
- C. Reinforcement learning.
- D. Unsupervised learning.
Answer: A
Explanation:
The common types of machine learning include supervised learning, unsupervised learning, reinforcement learning, and deep learning. Cognitive learning is not a type of machine learning; rather, it is a term often associated with the broader field of cognitive science and psychology. Reference: AIGP BODY OF KNOWLEDGE and standard AI/ML literature.
NEW QUESTION # 66
CASE STUDY
Please use the following answer the next question:
XYZ Corp., a premier payroll services company that employs thousands of people globally, is embarking on a new hiring campaign and wants to implement policies and procedures to identify and retain the best talent. The new talent will help the company's product team expand its payroll offerings to companies in the healthcare and transportation sectors, including in Asia.
It has become time consuming and expensive for HR to review all resumes, and they are concerned that human reviewers might be susceptible to bias.
Address these concerns, the company is considering using a third-party Al tool to screen resumes and assist with hiring. They have been talking to several vendors about possibly obtaining a third-party Al-enabled hiring solution, as long as it would achieve its goals and comply with all applicable laws.
The organization has a large procurement team that is responsible for the contracting of technology solutions.
One of the procurement team's goals is to reduce costs, and it often prefers lower-cost solutions. Others within the company are responsible for integrating and deploying technology solutions into the organization's operations in a responsible, cost-effective manner.
The organization is aware of the risks presented by Al hiring tools and wants to mitigate them. It also questions how best to organize and train its existing personnel to use the Al hiring tool responsibly. Their concerns are heightened by the fact that relevant laws vary across jurisdictions and continue to change.
If XYZ does not deploy and use the Al hiring tool responsibly in the United States, its liability would likely increase under all of the following laws EXCEPT?
- A. Product liability laws.
- B. Anti-discriminationlaws.
- C. Privacy laws.
- D. Accessibility laws.
Answer: A
Explanation:
In the United States, the use of AI hiring tools must comply with anti-discrimination laws, accessibility laws, and privacy laws to avoid increasing liability. Anti-discrimination laws (A) ensure that hiring practices do not unlawfully discriminate against protected classes. Accessibility laws (C) require that hiring tools are accessible to all applicants, including those with disabilities. Privacy laws (D) govern the handling of personal data during the hiring process. Product liability laws (B), however, typically apply to the safety and reliability of physical products and would not generally increase liability specifically related to the responsible use of AI hiring tools in the employment context.
NEW QUESTION # 67
If it is possible to provide a rationale for a specific output of an Al system, that system can best be described as?
- A. Reliable.
- B. Accountable.
- C. Explainable.
- D. Transparent.
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 # 68
CASE STUDY
Please use the following answer the next question:
A mid-size US healthcare network has decided to develop an Al solution to detect a type of cancer that is most likely arise in adults. Specifically, the healthcare network intends to create a recognition algorithm that will perform an initial review of all imaging and then route records a radiologist for secondary review pursuant Agreed-upon criteria (e.g., a confidence score below a threshold).
To date, the healthcare network has taken the following steps: defined its Al ethical principles: conducted discovery to identify the intended uses and success criteria for the system: established an Al governance committee; assembled a broad, crossfunctional team with clear roles and responsibilities; and created policies and procedures to document standards, workflows, timelines and risk thresholds during the project.
The healthcare network intends to retain a cloud provider to host the solution and a consulting firm to help develop the algorithm using the healthcare network's existing data and de-identified data that is licensed from a large US clinical research partner.
Which of the following steps can best mitigate the possibility of discrimination prior to training and testing the Al solution?
- A. Perform an impact assessment.
- B. Create a bias bounty program.
- C. Engage a third party to perform an audit.
- D. Procure more data from clinical research partners.
Answer: A
Explanation:
Performing an impact assessment is the best step to mitigate the possibility of discrimination before training and testing the AI solution. An impact assessment, such as a Data Protection Impact Assessment (DPIA) or Algorithmic Impact Assessment (AIA), helps identify potential biases and discriminatory outcomes that could arise from the AI system. This process involves evaluating the data and the algorithm for fairness, accountability, and transparency. It ensures that any biases in the data are detected and addressed, thus preventing discriminatory practices and promoting ethical AI deployment. Reference: AIGP Body of Knowledge on Ethical AI and Impact Assessments.
NEW QUESTION # 69
The most important factor in ensuring fairness when training an Al system is?
- A. The model accuracy and scale.
- B. The architecture and model selection.
- C. The data attributes and variability.
- D. The data labeling and classification.
Answer: C
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 # 70
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. Backpropagation, which starts from the last layer working backwards.
- B. Autoregression, which analyzes and makes predictions about time-series data.
- C. Gradient descent, which initially sets weights arbitrary values, and then at each step changes them.
- D. Momentum, which improves the convergence speed and stability of neural network training.
Answer: B
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 # 71
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