Which provisioning strategy ensures efficient resource scaling for an application on Kubernetes?
Correct Answer: B
The most efficient and scalable strategy is to use a declarative approach with Infrastructure as Code (IaC) . Option B is correct because declarative definitions specify the desired state (e.g., resource requests, limits, autoscaling policies) in code, allowing Kubernetes controllers and autoscalers to reconcile and enforce them dynamically. This ensures that applications can scale efficiently based on actual demand. Option A (fixed allocation) is inefficient, leading to wasted resources during low usage or insufficient capacity during high demand. Option C (manual provisioning) introduces delays, risk of error, and operational overhead. Option D (imperative scripting) is not sustainable for large-scale or dynamic workloads, as it requires constant manual intervention. Declarative IaC aligns with GitOps workflows, enabling automated, version-controlled scaling decisions. Combined with Kubernetes' Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, this approach allows platforms to balance cost efficiency with application reliability. References:- CNCF GitOps Principles- Kubernetes Autoscaling Documentation- Cloud Native Platform Engineering Study Guide
Question 27
A platform team is deciding whether to invest engineering time into automating cluster autoscaling. Which of the following best justifies making this automation a priority?
Correct Answer: A
Automation in platform engineering is primarily about reducing repetitive manual work, or toil, which consumes engineering capacity and increases the risk of human error. Option A is correct because cluster autoscaling-adjusting resources to meet workload demand-is a repetitive, ongoing task that is better handled through automation. Automating this process ensures scalability, efficiency, and reliability while freeing platform teams to focus on higher-value work. Option B may provide learning opportunities but is not a sustainable justification. Option C is subjective and inefficient, while Option D is overly broad-automation should be applied thoughtfully to tasks that bring measurable benefits. Automating autoscaling aligns with cloud native best practices, ensuring workloads can respond elastically to demand changes while maintaining cost efficiency. This reduces manual overhead, improves resiliency, and supports the developer experience by ensuring resource availability. References:- CNCF Platforms Whitepaper- SRE Principles on Eliminating Toil- Cloud Native Platform Engineering Study Guide
Question 28
A developer is struggling to access the necessary services on a cloud native platform due to complex Kubernetes configurations. What approach can best simplify their access to platform capabilities?
Correct Answer: B
One of the primary objectives of internal developer platforms (IDPs) is to improve developer experience by reducing cognitive load. Complex Kubernetes configurations often overwhelm developers who simply want to consume services and deploy code without worrying about infrastructure intricacies. Option B is correct because implementing a self-service web portal (or developer portal) abstracts away Kubernetes complexities, providing developers with easy access to platform services through standardized workflows, templates, and golden paths. This aligns with platform engineering principles: empowering developers with self-service capabilities while maintaining governance, security, and compliance. Option A increases burden unnecessarily and negatively impacts productivity. Option C limits access to services, reducing flexibility and developer autonomy, which goes against the core goal of IDPs. Option D, while helpful for education, does not remove complexity-it only shifts the responsibility back to the developer. By leveraging portals, APIs, and automation, platform teams allow developers to focus on building business value instead of managing infrastructure details. References:- CNCF Platforms Whitepaper- Team Topologies and Platform Engineering Practices- Cloud Native Platform Engineering Study Guide
