ASSOCIATE-CLOUD-ENGINEER Question #294: Real Exam Question with Answer & Explanation

Question

You are running multiple microservices in a Kubernetes Engine cluster. One microservice is rendering images. The microservice responsible for the image rendering requires a large amount of CPU time compared to the memory it requires. The other microservices are workloads that are optimized for n2-standard machine types. You need to optimize your cluster so that all workloads are using resources as efficiently as possible. What should you do?

Options

• AAssign the pods of the image rendering microservice a higher pod priority than the other
• BCreate a node pool with compute-optimized machine type nodes for the image rendering
• CUse the node pool with general-purpose machine type nodes for the image rendering
• DConfigure the required amount of CPU and memory in the resource requests specification of the

Explanation

Option B is correct because the image rendering microservice is CPU-heavy relative to memory, making compute-optimized machine types (like c2 series in GCP) the ideal fit - they provide higher CPU-to-memory ratios and are purpose-built for CPU-intensive workloads, maximizing efficiency without paying for unused memory.

Option A is wrong because pod priority controls scheduling order during resource contention, not resource efficiency - it doesn't change how CPU or memory is allocated to the workload.

Option C is wrong because general-purpose nodes (n2-standard) are already being used by the other microservices and are balanced for CPU/memory - running a CPU-intensive workload on them wastes money by over-provisioning memory you don't need.

Option D is wrong because setting resource requests improves scheduling accuracy but doesn't change the underlying machine type; you're still paying for a general-purpose machine with a suboptimal CPU-to-memory ratio.

Memory tip: Think "match the workload profile to the machine family" - CPU-heavy = compute-optimized, memory-heavy = memory-optimized, balanced = general-purpose. Node pools let you mix machine families in one cluster, so you never have to compromise.

No community discussion yet for this question.