An AI security administrator notices that the information referenced by the model is incorrectly formatted and missing values. Which of the following job roles would most likely be responsible for correcting this error?
Answer(s): C
A data engineer is responsible for preparing, cleaning, and formatting data pipelines. When information is incorrectly formatted or missing values, the data engineer ensures data integrity and quality before it is used by AI models.
Which of the following describe the practice of providing examples in a prompt? (Choose two.)
Answer(s): E,F
Providing examples in a prompt is referred to as one-shot prompting when a single example is given and multi-shot prompting when multiple examples are provided.
A user interface engineer adds new graphics to the latest release of an AI-integrated application. During the update, the engineer accidentally causes the model to retain on unverified data. After the update, the model begins to return many errors. Which of the following is the best way to mitigate future errors?
Following a secure and structured model development life cycle (MDLC) ensures controls such as data validation, verification, and testing are in place. This prevents issues like retraining on unverified data and reduces the likelihood of future model errors.
A short AI-generated video shows a celebrity’s likeness talking about a fake public security event. Which of the following was used to create this video?
Answer(s): B
Convolutional neural networks (CNNs) are commonly used in generating deepfake videos, where a person’s likeness is realistically mapped and animated to create fake but convincing audiovisual content.
An AI security team must assess the probability of an attack on its new system and the impact associated with such an attack. Which of the following threat-modeling resources best addresses the threat landscape for machine learning (ML)?
MITRE ATLAS is specifically designed to capture adversarial tactics, techniques, and procedures (TTPs) targeting machine learning systems. It helps organizations assess both the probability and impact of AI/ML-related attacks, making it the most relevant threat-modeling resource.
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