NCA-AIIO Practice Questions: Deployment and Operations Domain

Published: June 12, 2025 | 5 min read

Test your NCA-AIIO knowledge with 5 practice questions from the Security and Compliance domain. Includes detailed explanations and answers.

NCA-AIIO Practice Questions

Master Deployment and Operations

Operations Foundation: Deployment builds upon infrastructure and hardware knowledge. Complete our AI Infrastructure Fundamentals and Hardware and System Architecture practice questions first, then review our Complete NCA-AIIO Study Guide.

Master Deployment and Operations with practice questions covering orchestration, scaling, monitoring, and operational best practices for AI infrastructure in production environments.

Performance Integration

Operational efficiency directly impacts system performance. After mastering deployment concepts, advance to our Performance Optimization and Monitoring practice questions to understand operational monitoring and optimization strategies.

Question 1: Container Orchestration

When deploying AI workloads using Kubernetes, which scheduling strategy ensures optimal GPU resource utilization across heterogeneous GPU clusters?

A) Random pod scheduling

B) Node affinity with GPU type labels

C) First-available node scheduling

D) CPU-only scheduling

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Correct Answer: B

Explanation: Node affinity with GPU type labels allows workloads to be scheduled on appropriate GPU hardware, ensuring optimal resource matching and utilization. This scheduling strategy connects to the hardware concepts covered in our Hardware and System Architecture practice questions.

Question 2: Auto-scaling Strategies

In a production AI inference service, which auto-scaling metric provides the most reliable indicator for scaling decisions?

A) CPU utilization only

B) Request queue length and GPU utilization

C) Memory usage only

D) Network traffic volume

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Correct Answer: B

Explanation: Combining request queue length with GPU utilization provides comprehensive scaling signals - queue length indicates demand, while GPU utilization shows resource saturation. This monitoring approach is detailed in our Performance Optimization and Monitoring practice questions.

Question 3: Blue-Green Deployment

When implementing blue-green deployment for AI model updates, what is the primary advantage over rolling updates in a production environment?

A) Uses less computational resources

B) Enables instant rollback and eliminates version mixing

C) Requires no additional infrastructure

D) Automatically tests model accuracy

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Correct Answer: B

Explanation: Blue-green deployment allows instant traffic switching between environments, enabling immediate rollback if issues are detected, and prevents the complexity of having multiple model versions running simultaneously. This deployment safety connects to the troubleshooting scenarios covered in our Troubleshooting and Maintenance practice questions.

Question 4: Service Mesh Integration

In a microservices architecture for AI applications, which service mesh feature is most critical for managing inter-service communication reliability?

A) Traffic encryption only

B) Circuit breakers and retry policies

C) Load balancing only

D) Service discovery only

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Correct Answer: B

Explanation: Circuit breakers prevent cascade failures by stopping requests to failing services, while retry policies handle transient failures, both essential for maintaining system reliability in distributed AI applications. This reliability pattern connects to the infrastructure fundamentals covered in our AI Infrastructure Fundamentals practice questions.

Question 5: Resource Quotas and Limits

When configuring resource quotas for AI workloads in a multi-tenant environment, which approach provides the best balance of resource utilization and isolation?

A) No resource limits

B) Namespace-level quotas with pod-level requests and limits

C) Only pod-level limits

D) Equal resource division for all tenants

Show Answer & Explanation

Correct Answer: B

Explanation: Namespace-level quotas provide tenant isolation while pod-level requests ensure resource availability and limits prevent resource hogging, enabling efficient multi-tenant resource sharing. This security approach is detailed in our Security and Compliance practice questions.

Operations Mastery Path

Continue building operational expertise with these interconnected domains:

Master AI Infrastructure Operations

Access comprehensive practice questions covering deployment, scaling, and operational best practices.