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Exam CSPAI Review | Dump CSPAI FileIn the Desktop CSPAI practice exam software version of SISA CSPAI practice test is updated and real. The software is useable on Windows-based computers and laptops. There is a demo of the CSPAI practice exam which is totally free. CSPAI practice test is very customizable and you can adjust its time and number of questions. Desktop CSPAI Practice Exam software also keeps track of the earlier attempted CSPAI practice test so you can know mistakes and overcome them at each and every step. SISA Certified Security Professional in Artificial Intelligence Sample Questions (Q20-Q25):NEW QUESTION # 20
In the context of a supply chain attack involving machine learning, which of the following is a critical component that attackers may target?
A. The underlying ML model and its training data.
B. The physical hardware running the AI system
C. The user interface of the AI application
D. The marketing materials associated with the AI product
Answer: A
Explanation:
Supply chain attacks in ML exploit vulnerabilities in the ecosystem, with the core ML model and training data being prime targets due to their foundational role in system behavior. Attackers might inject backdoors into pretrained models via compromised libraries (e.g., PyTorch or TensorFlow packages) or poison datasets during sourcing, leading to manipulated outputs or data exfiltration. This is more critical than targeting UI or hardware, as model/data compromises persist across deployments, enabling stealthy, long-term exploits like trojan attacks. Mitigation includes verifying model provenance, using secure repositories, and conducting integrity checks with hashing or digital signatures. In SISA guidelines, emphasis is on end-to-end supply chain auditing to prevent such intrusions, which could result in biased decisions or security breaches in applications like recommendation systems. Protecting these components ensures model reliability and data confidentiality, integral to AI security posture. Exact extract: "In supply chain attacks on machine learning, attackers critically target the underlying ML model and its training data to introduce persistent vulnerabilities." (Reference: Cyber Security for AI by SISA Study Guide, Section on Supply Chain Risks in AI, Page 145-148).
NEW QUESTION # 21
How does machine learning improve the accuracy of predictive models in finance?
A. By using historical data patterns to make predictions without updates
B. By continuously learning from new data patterns to refine predictions
C. By avoiding any use of past data and focusing solely on current trends
D. By relying exclusively on manual adjustments and human input for predictions.
Answer: B
Explanation:
Machine learning enhances financial predictive models by continuously learning from new data, refining predictions for tasks like fraud detection or market forecasting. This adaptability leverages evolving patterns, unlike static historical or manual methods, and improves security posture through real-time anomaly detection. Exact extract: "ML improves financial predictive accuracy by continuously learning from new data patterns to refine predictions." (Reference: Cyber Security for AI by SISA Study Guide, Section on ML in Financial Security, Page 85-88).
NEW QUESTION # 22
What does the OCTAVE model emphasize in GenAI risk assessment?
A. Short-term tactical responses over strategic planning.
B. Operational Critical Threat, Asset, and Vulnerability Evaluation focused on organizational risks.
C. Exclusion of stakeholder input in assessments.
D. Solely technical vulnerabilities in AI models.
Answer: B
Explanation:
OCTAVE adapts to GenAI by emphasizing organizational risk perspectives, identifying critical assets like models and data, evaluating threats, and prioritizing mitigations through stakeholder collaboration. It fosters a strategic, enterprise-wide approach to AI risks, integrating business impacts. Exact extract: "OCTAVE emphasizes operational critical threat, asset, and vulnerability evaluation in GenAI risk assessment." (Reference: Cyber Security for AI by SISA Study Guide, Section on OCTAVE for AI, Page 255-258).
NEW QUESTION # 23
In what way can GenAI assist in phishing detection and prevention?
A. By relying solely on signature-based detection methods.
B. By sending automated phishing emails to test employee awareness.
C. By blocking all incoming emails to prevent any potential threats.
D. By generating realistic phishing simulations and analyzing user responses.
Answer: D
Explanation:
GenAI bolsters phishing defenses by creating sophisticated simulation campaigns that mimic real attacks, training employees and refining detection algorithms based on interaction data. It analyzes email content, URLs, and attachments semantically to identify subtle manipulations, going beyond traditional filters. This dynamic method adapts to evolving tactics like AI-generated deepfakes in emails, improving prevention through predictive modeling. Organizations benefit from reduced successful breach rates and enhanced user education. Integration with email gateways provides real-time alerts, strengthening overall security. Exact extract: "GenAI assists in phishing detection by generating simulations and analyzing responses, thereby preventing attacks and improving security posture." (Reference: Cyber Security for AI by SISA Study Guide, Section on GenAI in Phishing Mitigation, Page 210-213).
NEW QUESTION # 24
During the development of AI technologies, how did the shift from rule-based systems to machine learning models impact the efficiency of automated tasks?
A. Improved scalability and performance in handling diverse and evolving data.
B. Increased system complexity and the requirement for specialized knowledge,
C. Enhanced the precision and relevance of automated outputs with reduced manual tuning.
D. Enabled more dynamic decision-making and adaptability with minimal manual intervention
Answer: D
Explanation:
The transition from rigid rule-based systems, which rely on predefined logic and struggle with variability, to machine learning models introduced data-driven learning, allowing systems to adapt dynamically to new patterns with less human oversight. This shift boosted efficiency in automated tasks by enabling real-time adjustments, such as in spam detection where ML models evolve with threats, unlike static rules. It minimized manual rule updates, fostering scalability and handling complex, unstructured data effectively. However, it introduced challenges like interpretability needs. In GenAI evolution, this paved the way for advanced models like Transformers, impacting sectors by automating nuanced decisions. Exact extract: "The shift enabled more dynamic decision-making and adaptability with minimal manual intervention, significantly improving the efficiency of automated tasks." (Reference: Cyber Security for AI by SISA Study Guide, Section on AI Evolution and Impacts, Page 20-23).
NEW QUESTION # 25
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