Pass the Cloud Security Alliance Cloud Security Knowledge CCSK Questions and answers with CertsForce

When comparing different Cloud Service Providers (CSPs), it is important to recognize that while they may have similar organizational structures — such as divisions for security, compliance, and support — they often use varying terminology to describe their services, roles, and responsibilities. Understanding these differences is crucial for cybersecurity professionals to ensure proper alignment of security practices, controls, and policies across different cloud platforms.

CSPs typically have variations in organizational structure and terminology. While the structure can vary, it is not usually "vastly" different in terms of core functions. Differences in terminology can have implications for understanding security roles, policies, and practices, affecting how cybersecurity tasks are performed.

The primary function ofData Encryption Keys (DEK)in cloud security is toencrypt application data. DEKs are used to encrypt and decrypt specific data objects, such as files or database records, ensuring data confidentiality in cloud environments.

From theCCSK v5.0 Study Guide, Domain 10 (Data Security and Encryption), Section 10.3:

“Data Encryption Keys (DEKs) are used to encrypt and decrypt application data in cloud environments. DEKs are typically managed by key management services and applied to specific data objects to ensure confidentiality and protect against unauthorized access.”

Option B (To encrypt application data) is the correct answer.

Option A (Increase speed) is incorrect because encryption does not enhance performance.

Option C (Manage user access control) is incorrect because DEKs are for encryption, not access control.

Option D (Primary key for all resources) is incorrect because DEKs are specific to data encryption, not resource management.

Updating forensics tools for virtual machines (VMs) and containers is critical because cloud environments can differ significantly from traditional on-premises environments. As cloud technologies evolve, it is important to ensure that forensic tools are compatible with the latest cloud infrastructure, such as VMs, containers, and serverless architectures. Thisensures that the tools can effectively collect, analyze, and preserve evidence in the event of a security incident, allowing for accurate and efficient incident analysis.

Complying with cloud service level agreements (SLAs)) is not the primary reason for updating forensics tools, although some SLAs may require certain levels of incident response capabilities. Streamlining communication with cloud service providers and customers) is important, but the primary concern is the ability to analyze incidents, not just communication. Increasing the speed of incident response team deployments) is a consideration, but ensuring the tools are up to date and compatible is the main priority for effective incident analysis.

The primary objective of cloud governance in an organization is to align cloud usage with corporate objectives. Cloud governance ensures that the cloud resources, services, and strategies are used effectively and efficiently, supporting the organization’s overall goals and priorities. It involves establishing policies, compliance measures, and management practices to ensure that cloud adoption and usage are aligned with business needs, security requirements, and regulatory obligations.

Implementing multi-tenancy and resource pooling is important for cloud infrastructure but is more related to the underlying technology rather than governance. Simplifying scalability and automating resource management are benefits of cloud environments, but they are more about cloud architecture and operations than governance. Enhancing user experience and reducing latency are concerns of performance optimization and user interface design, not the primary focus of cloud governance.

Immutable containers are not altered post-deployment, ensuring the integrity of the deployed environment and reducing the risk of unauthorized modifications. Reference: [CCSK v5 Curriculum, Domain 8 - Cloud Workload Security][16†source].

An SDP creates a "dark" network, visible only to authorized users, enhancing security by hiding infrastructure from potential attackers. Reference: [Security Guidance v5, Domain 7 - Infrastructure & Networking]

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)​