Question 91 · 282 in bank
What factors influence the Kubernetes scheduler when it places Pods on nodes?
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Answer: A
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What factors influence the Kubernetes scheduler when it places Pods on nodes?
Answer: A
Which Prometheus metric represents a single value that can go up and down?
Answer: B
What is the core metric type in Prometheus used to represent a single numerical value that can go up and down?
Answer: D
What is the primary mechanism to identify grouped objects in a Kubernetes cluster?
Answer: B
Labels are key-value pairs that you attach to Kubernetes objects. They define attributes and allow you to "tag" objects to group or identify them. Label Selectors are used to query or filter objects that have certain labels. While selectors allow you to find or match objects based on labels, they don't define the labels themselves. Labels are the mechanism used to mark or identify objects. Without labels, there would be nothing for Label Selectors to query. Label Selectors are only tools to query/filter based on existing labels.
Source: Adapted from community discussion — abitwrong
What is the name of the Kubernetes resource used to expose an application?
Answer: B
What is a DaemonSet?
Answer: A
What is the telemetry component that represents a series of related distributed events that encode the end-to-end request flow through a distributed system?
Answer: D
In the Kubernetes platform, which component is responsible for running containers?
Answer: B
Services and Pods in Kubernetes are ______ objects.
Answer: D
Services and Pods in Kubernetes are: D. REST objects In Kubernetes, resources such as Services and Pods are represented as RESTful objects. This means that they can be accessed and manipulated using HTTP methods such as GET, POST, PUT, and DELETE via the Kubernetes API server. While configurations for Kubernetes resources are often written in YAML or JSON format, these formats are used to define the desired state of the objects rather than representing the nature of the objects themselves
Source: Adapted from community discussion — 377ecc2
Let's assume that an organization needs to process large amounts of data in bursts, on a cloud-based Kubernetes cluster. For instance: each Monday morning, they need to run a batch of 1000 compute jobs of 1 hour each, and these jobs must be completed by Monday night. What's going to be the most cost-effective method?
Answer: B
The most cost-effective method would likely be option B, leveraging the Kubernetes Cluster Autoscaler to automatically start and stop nodes as they're needed. Here's why: Burst processing workloads, like the one described, benefit from the elasticity provided by cloud-based Kubernetes clusters. With Kubernetes Cluster Autoscaler, you can scale your cluster up when there's a demand for more resources (e.g., Monday mornings when the batch jobs need to run) and scale it down during periods of low demand (e.g., after the batch jobs are completed). This ensures that you're only paying for the resources you actually need, avoiding over-provisioning and reducing costs.
Source: Adapted from community discussion — pablokoba
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