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| Section | Weight | Objectives |
|---|---|---|
| AI Governance, Risk and Compliance | 19% | - Risk management for AI
|
| Securing AI Systems | 40% | - Defending against AI-specific attacks
|
| Basic AI Concepts Related to Cybersecurity | 17% | - AI applications in security
|
| AI-assisted Security | 24% | - AI for threat detection and response
|
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NEW QUESTION # 122
A security administrator must provide access controls for AI systems to list tables. Which of the following should the administrator implement?
Answer: C
Explanation:
Since the requirement is to control which users or systems can list tables, the proper control lies at the data access level. Implementing data access controls ensures only authorized entities can view or query the underlying tables used by the AI system.
NEW QUESTION # 123
During the selection of a machine learning (ML)-based threat classification model, a cybersecurity administrator verifies that label distribution is highly unbalanced.
Which of the following processing techniques should the engineer use to balance the model?
Answer: A
Explanation:
Basic Concept: Class imbalance in training data - where some categories have significantly more examples than others - causes ML models to be biased toward the majority class, producing poor detection of minority class threats. Addressing this imbalance before training is critical for threat classification accuracy. CompTIA SecAI+ covers data preparation techniques under basic AI concepts.
Why B is Correct: Data augmentation addresses class imbalance by artificially increasing the number of training samples in under-represented classes. Techniques include oversampling minority classes by creating synthetic examples using methods like SMOTE (Synthetic Minority Over-sampling Technique), or undersampling majority classes. This balances label distribution and enables the model to learn decision boundaries that accurately classify all threat categories, not just the dominant ones.
Why A is Wrong: Data lineage documents the origin, movement, and transformation of data throughout its lifecycle. It provides traceability and auditability but does not address class imbalance in training data distribution.
Why C is Wrong: Data provenance records the history and context of data origins. Like lineage, it is a governance and tracking concept that does not alter data distribution for model training balance.
Why D is Wrong: Data verification confirms that data is correct and consistent with expected formats and values. It checks data quality and integrity but does not address the statistical distribution imbalance between threat classes in training datasets.
NEW QUESTION # 124
Which of the following should an auditor reference when reviewing a company's human resources AI systems for legal non-compliance?
Answer: A
Explanation:
The EU AI Act is legally binding legislation that specifically governs the use of AI systems, including those used in human resources for hiring, promotion, and evaluation. An auditor reviewing AI systems for legal non-compliance must reference this act because it establishes enforceable requirements related to transparency, bias, risk classification, and prohibited practices.
NEW QUESTION # 125
Which of the following technologies is used in deepfake?
Answer: A
Explanation:
Basic Concept: Deepfakes are AI-generated synthetic media that convincingly replace or manipulate a person
' s likeness, voice, or actions in images and videos. Creating realistic deepfakes requires generative AI techniques capable of learning and reproducing complex data distributions. CompTIA SecAI+ Exam Objectives cover deepfake technology under basic AI concepts.
Why A is Correct: Generative Adversarial Networks (GANs) are the primary technology behind deepfakes. A GAN consists of two competing neural networks: a generator that creates synthetic content and a discriminator that evaluates whether content is real or fake. Through adversarial training, the generator continuously improves at creating convincing synthetic media such as realistic human faces, voice clones, and video manipulations indistinguishable from authentic recordings.
Why B is Wrong: Multi-shot prompting is a prompting technique where multiple examples are provided to an LLM to guide its responses. It is an inference technique for language models and has no role in generating synthetic video or image deepfake content.
Why C is Wrong: Prompt engineering is the practice of crafting effective prompts to guide LLM outputs. It is a communication strategy for working with text-based AI systems, not a technology for generating synthetic media.
Why D is Wrong: Transfer learning is a training technique that repurposes knowledge from one domain to another, improving model performance with limited data. While it can be used in model training pipelines, it is not the core technology that enables deepfake generation.
NEW QUESTION # 126
Which of the following is used to train an AI model with unstructured data?
Answer: B
Explanation:
Fine-tuning allows an AI model to adapt to unstructured data (such as text, audio, or images) by retraining it on domain-specific datasets. This process improves the model's ability to handle and generate outputs aligned with the unstructured data context.
NEW QUESTION # 127
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