Free Practice Questions for Cloud Security Alliance CCSK Exam

SLAs play a key role in cloud incident management as they define response expectations and support arrangements between CSPs and CSCs. Reference: [CCSK Study Guide, Domain 11 - Incident Response]

Classification and cataloging help assign security controls andmanage data based on its sensitivity and criticality. Reference: [CCSK v5 Curriculum, Domain 9 - Data Security]

Enforcing the principle of least privilege is the practice of granting users and systems the minimum level of access necessary to perform their tasks. By limiting root or core access and restricting the creation of deployments to onlythose who absolutely need it, the risk of unauthorized access, misuse, or damage is minimized. This helps ensure that critical systems and sensitive data are protected by reducing the number of people or services with high-level access.

Trust and verify on demand is not a standard security practice and could create security gaps. Disabling multi-factor authentication is a poor security practice, as multi-factor authentication (MFA) enhances security by adding an additional layer of verification. Deploying applications with full access) contradicts the principle of least privilege and could expose the system to unnecessary risks.

Among AI workloads,Trainingrequires themost computational power and data resources.

Why AI Training is Computationally Intensive?

Large datasets:

AI models (e.g., deep learning, neural networks)require millions or billions of labeled data points.

Training involvesprocessing massive amounts of structured/unstructured data.

High computational power:

Training deep learning modelsinvolves runningmultiple passes (epochs) over data, adjusting weights, and optimizing parameters.

Requiresspecialized hardwarelikeGPUs (Graphics Processing Units),TPUs (Tensor Processing Units), andHPC (High-Performance Computing).

Long training times:

AI model training can takedays, weeks, or even monthsdepending on complexity.

Cloud platforms offerdistributed computing (multi-GPU training, parallel processing, auto-scaling).

Cloud AI Training Benefits:

Cloud providers (AWS, Azure, GCP) offer ML training serviceswithon-demand scalable compute instances.

Supportsframeworks like TensorFlow, PyTorch, and Scikit-learn.

This aligns with:

CCSK v5 - Security Guidance v4.0, Domain 14 (Related Technologies - AI and ML Security)

Cloud AI Security Risks and AI Data Governance (CCM - AI Security Controls)​

AI can enhance anomaly detection in cybersecurity by analyzing large volumes of data and identifying patterns that deviate from normal behavior. By using machine learning algorithms, AI can improve the accuracy of anomaly detection, reducing false positive alerts. This helps security teams focus on genuine threats while minimizing distractions from irrelevant alerts.

Assisting analysts is a valid benefit of AI, but reducing false positives directly improves anomaly detection capabilities. Threat intelligence refers to gathering and analyzing information about potential threats but isn't directly focused on reducing false positives in the same way as anomaly detection. Automated responses can be part of AI's role in cybersecurity, but reducing false positives is more directly related to improving anomaly detection.

Misconfiguration is one of the most prevalent risks in serverless and container-based environments. Given the complex nature of container orchestration (e.g., Kubernetes), CI/CD pipelines, and ephemeral infrastructure, simple missteps—such as overly permissive roles or exposed ports—can lead to significant vulnerabilities.

These workloads require strict configuration management, automated scanning, and secure defaults to prevent breaches. Unlike traditional servers, containers and functions spin up and down rapidly, making traditional visibility tools insufficient.

This is discussed thoroughly inDomain 8: Virtualization and Containers, where the CCSK guidance identifies misconfiguration as a leading cause of cloud-native exploitation.