Cloud Security Alliance Updated CCSK Exam Questions and Answers by ivy-rae

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)​

Elasticity of cloud resources is a key feature that directly contributes to enhanced performance and resource utilization while avoiding excess costs. Cloud elasticity allows resources (such as compute power, storage, and network bandwidth) to automatically scale up or down based on demand. This ensures that organizations are only using the resources they need at any given time, optimizing both performance and cost-efficiency.

Fixed resource allocations do not provide the flexibility needed to optimize resource utilization and can lead to either over-provisioning (wasting resources) or under-provisioning (affecting performance). Unlimited data storage capacity is not typical in all cloud environments and does not directly impact resource optimization or performance. Increased on-premise hardware is unrelated to cloud workload security, as it refers to traditional, non-cloud infrastructure.