Pearson CCSK New Attempt

Infrastructure as a Service (IaaS) primarily provides users with storage, computing resources, and networking capabilities. In the IaaS model, cloud providers offer virtualized computing resources over the internet. Users can rent servers, storage, and networking equipment without needing to manage the physical hardware themselves. This allows for flexible scaling and resource management according to the users' needs.

FaaS focuses on serverless computing where users run code in response to events. PaaS provides a platform that allows users to develop, run, and manage applications without worrying about the underlying infrastructure. SaaS delivers fully managed applications over the internet, where users access software without managing the infrastructure.

In multi-tenant technologies, the fundamental security requirement is segmented and segregated customer environments. Multi-tenancy means that multiple customers (tenants) share the same physical or virtual infrastructure while maintaining logical separation to prevent data leakage and unauthorized access between tenants.

To ensure security and compliance in multi-tenant environments, providers implement:

Network segmentation (VLANs, Virtual Private Clouds)

Isolation mechanisms (such as virtual firewalls and access control lists)

Data isolation through encryption and access controls

Hypervisor-based isolation in virtualized environments

The goal is to create strong logical isolation between tenants to mitigate risks like data leakage, guest-hopping attacks, and unauthorized access.

Why Other Options Are Incorrect:

B. Limited resource allocation: While resource limits may help performance management, they do not inherently ensure security in multi-tenant settings.

C. Resource pooling: Though fundamental to cloud computing, it does not address the isolation needed for secure multi-tenancy.

D. Abstraction and automation: These are key elements in cloud computing but do not directly address multi-tenant security.

Differential privacy is a strategy designed to protect data confidentiality by ensuring that the output of a machine learning model does not expose sensitive information about individual data points. In the context of model inversion attacks, where attackers try to infer confidential data from the model, differential privacy introduces noise into the model’s output in a way that prevents attackers from accurately reconstructing the input data. This helps safeguard against attacks that threaten the privacy of the data used to train the model.

Secure multi-party computation is useful for enabling collaborative computation on encrypted data but does not specifically address model inversion attacks. Encryption is important for securing data at rest or in transit but does not directly protect against model inversion attacks. Model hardening refers to general measures to make models more robust to adversarial attacks, but it does not directly mitigate the specific risk of model inversion attacks related to data confidentiality.

Multi-region resiliency in cloud environments is primarily used to improve fault tolerance by deploying applications and services across multiple geographical regions. This strategy ensures that if one region experiences an outage or failure, the application or service can failover to another region, maintaining availability and minimizing downtime. Multi-region deployments help organizations ensure business continuity, disaster recovery, and high availability.

Increasing the number of users in each region is not the main purpose of multi-region resiliency. While multi-region deployment can help with compliance, the primary goal is fault tolerance and availability, not compliance with data laws. While multi-region deployment may offer some efficiency benefits, the main purpose is not cost reduction; it's about ensuring reliability and availability.