Practice Free CCSK Exam Online Questions
Question #91
What is the primary function of a Load Balancer Service in a Software Defined Network (SDN) environment?
- A . To create isolated virtual networks
- B . To monitor network performance and activity
- C . To distribute incoming network traffic across multiple destinations
- D . To encrypt data for secure transmission
Correct Answer: C
C
Explanation:
The correct answer is C. To distribute incoming network traffic across multiple destinations.
A Load Balancer Service in an SDN environment is responsible for efficiently distributing network traffic across multiple servers or instances. This ensures high availability, reliability, and optimized resource usage.
Key Functions:
Traffic Distribution: Balances incoming requests to various servers based on predefined algorithms (round-robin, least connections, etc.).
High Availability: Prevents server overload and reduces downtime by distributing workload.
Scalability: Automatically adjusts as the number of requests or available resources changes.
Health Monitoring: Continually checks server availability and responsiveness to avoid directing traffic to non-responsive instances.
Why Other Options Are Incorrect:
C
Explanation:
The correct answer is C. To distribute incoming network traffic across multiple destinations.
A Load Balancer Service in an SDN environment is responsible for efficiently distributing network traffic across multiple servers or instances. This ensures high availability, reliability, and optimized resource usage.
Key Functions:
Traffic Distribution: Balances incoming requests to various servers based on predefined algorithms (round-robin, least connections, etc.).
High Availability: Prevents server overload and reduces downtime by distributing workload.
Scalability: Automatically adjusts as the number of requests or available resources changes.
Health Monitoring: Continually checks server availability and responsiveness to avoid directing traffic to non-responsive instances.
Why Other Options Are Incorrect:
Question #92
What is critical for securing serverless computing models in the cloud?
- A . Disabling console access completely or using privileged access management
- B . Validating the underlying container security
- C . Managing secrets and configuration with the least privilege
- D . Placing serverless components behind application load balancers
Correct Answer: C
C
Explanation:
In serverless computing models, the primary security concern is ensuring that secrets (such as API keys, database credentials, etc.) and configuration settings are handled securely. The principle of least privilege means that these secrets and configurations should only be accessible by the minimum set of functions or services that truly need them, reducing the attack surface. Proper management of secrets and configurations ensures that unauthorized access or misuse is prevented.
Disabling console access completely or using privileged access management is important for securing any environment, but it is not specifically tied to serverless models. Validating the underlying container security is more relevant to containerized environments rather than serverless computing, which abstracts away infrastructure management. Placing serverless components behind application load balancers is useful for routing traffic but is not specifically critical for securing the serverless model itself. Managing secrets and access controls is a more direct concern for securing serverless environments.
C
Explanation:
In serverless computing models, the primary security concern is ensuring that secrets (such as API keys, database credentials, etc.) and configuration settings are handled securely. The principle of least privilege means that these secrets and configurations should only be accessible by the minimum set of functions or services that truly need them, reducing the attack surface. Proper management of secrets and configurations ensures that unauthorized access or misuse is prevented.
Disabling console access completely or using privileged access management is important for securing any environment, but it is not specifically tied to serverless models. Validating the underlying container security is more relevant to containerized environments rather than serverless computing, which abstracts away infrastructure management. Placing serverless components behind application load balancers is useful for routing traffic but is not specifically critical for securing the serverless model itself. Managing secrets and access controls is a more direct concern for securing serverless environments.
Question #93
What is a core tenant of risk management?
- A . The provider is accountable for all risk management.
- B . You can manage, transfer, accept, or avoid risks.
- C . The consumers are completely responsible for all risk.
- D . If there is still residual risk after assessments and controls are inplace, you must accept the risk.
- E . Risk insurance covers all financial losses, including loss ofcustomers.
Correct Answer: B
Question #94
Which of the following is one of the five essential characteristics of cloud computing as defined by NIST?
- A . Multi-tenancy
- B . Nation-state boundaries
- C . Measured service
- D . Unlimited bandwidth
- E . Hybrid clouds
Correct Answer: C
Question #95
Which governance domain deals with evaluating how cloud computing affects compliance with internal security policies and various legal requirements, such as regulatory and legislative?
- A . Legal Issues: Contracts and Electronic Discovery
- B . Infrastructure Security
- C . Compliance and Audit Management
- D . Information Governance
- E . Governance and Enterprise Risk Management
Correct Answer: C
Question #96
What is a key advantage of using Policy-Based Access Control (PBAC) for cloud-based access
management?
- A . PBAC eliminates the need for defining and managing user roles and permissions.
- B . PBAC is easier to implement and manage compared to Role-Based Access Control (RBAC).
- C . PBAC allows enforcement of granular, context-aware security policies using multiple attributes.
- D . PBAC ensures that access policies are consistent across all cloud providers and platforms.
Correct Answer: C
C
Explanation:
PBAC enables highly specific access control based on multiple attributes, enhancing flexibility and security in cloud environments.
Reference: [CCSK v5 Curriculum, Domain 5 – IAM][16†source].
C
Explanation:
PBAC enables highly specific access control based on multiple attributes, enhancing flexibility and security in cloud environments.
Reference: [CCSK v5 Curriculum, Domain 5 – IAM][16†source].
Question #97
Which type of AI workload typically requires large data sets and substantial computing resources?
- A . Evaluation
- B . Data Preparation
- C . Training
- D . Inference
Correct Answer: C
C
Explanation:
Among AI workloads, Training requires the most 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 involves processing massive amounts of structured/unstructured data.
High computational power:
Training deep learning models involves running multiple passes (epochs) over data, adjusting weights, and optimizing parameters.
Requires specialized hardware like GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and HPC (High-Performance Computing).
Long training times:
AI model training can take days, weeks, or even months depending on complexity.
Cloud platforms offer distributed computing (multi-GPU training, parallel processing, auto-scaling).
Cloud AI Training Benefits:
Cloud providers (AWS, Azure, GCP) offer ML training services with on-demand scalable compute instances.
Supports frameworks 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)
C
Explanation:
Among AI workloads, Training requires the most 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 involves processing massive amounts of structured/unstructured data.
High computational power:
Training deep learning models involves running multiple passes (epochs) over data, adjusting weights, and optimizing parameters.
Requires specialized hardware like GPUs (Graphics Processing Units), TPUs (Tensor Processing Units), and HPC (High-Performance Computing).
Long training times:
AI model training can take days, weeks, or even months depending on complexity.
Cloud platforms offer distributed computing (multi-GPU training, parallel processing, auto-scaling).
Cloud AI Training Benefits:
Cloud providers (AWS, Azure, GCP) offer ML training services with on-demand scalable compute instances.
Supports frameworks 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)