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| Section | Weight | Objectives |
|---|---|---|
| Cloud Native Observability | 8% | - Monitoring and Metrics
|
| Cloud Native Architecture | 16% | - Architecture Concepts
|
| Cloud Native Application Delivery | 8% | - GitOps
|
| Container Orchestration | 22% | - Storage
|
| Kubernetes Fundamentals | 46% | - Kubernetes API
|
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NEW QUESTION # 55
A Deployment named web-app is running in the production namespace with 2 replicas. The replica count must be increased to 5. Which kubectl command correctly scales the Deployment?
Answer: D
Explanation:
The kubectl scale command changes the desired replica count of an existing Deployment.
Specifying --replicas=5 updates the Deployment to maintain five Pods in the production namespace.
NEW QUESTION # 56
What factors influence the Kubernetes scheduler when it places Pods on nodes?
Answer: A
Explanation:
The Kubernetes scheduler chooses a node for a Pod by evaluating scheduling constraints and cluster state. Key inputs include resource requests (CPU/memory), taints/tolerations, and affinity/anti-affinity rules. Option A directly names three real, high-impact scheduling factors-Pod memory requests, node taints, and Pod affinity-so A is correct.
Resource requests are fundamental: the scheduler must ensure the target node has enough allocatable CPU/memory to satisfy the Pod's requests. Requests (not limits) drive placement decisions. Taints on nodes repel Pods unless the Pod has a matching toleration, which is commonly used to reserve nodes for special workloads (GPU nodes, system nodes, restricted nodes) or to protect nodes under certain conditions. Affinity and anti-affinity allow expressing "place me near" or "place me away" rules-e.g., keep replicas spread across failure domains or co-locate components for latency.
Option B includes labels, which do matter, but "request labels" is not a standard scheduler concept; labels influence scheduling mainly through selectors and affinity, not as a direct category called "request labels." Option C mixes a real concept (taints, priority) with "node level," which isn't a standard scheduling factor term. Option D includes "container command," which does not influence scheduling; the scheduler does not care what command the container runs, only placement constraints and resources.
Under the hood, kube-scheduler uses a two-phase process (filtering then scoring) to select a node, but the inputs it filters/scores include exactly the kinds of constraints in A. Therefore, the verified best answer is A.
NEW QUESTION # 57
Which of these commands is used to retrieve the documentation and field definitions for a Kubernetes resource?
Answer: C
Explanation:
kubectl explain is the command that shows documentation and field definitions for Kubernetes resource schemas, so A is correct. Kubernetes resources have a structured schema: top-level fields like apiVersion, kind, and metadata, and resource-specific structures like spec and status. kubectl explain lets you explore these structures directly from your cluster's API discovery information, including field types, descriptions, and nested fields.
For example, kubectl explain deployment describes the Deployment resource, and kubectl explain deployment.
spec dives into the spec structure. You can continue deeper, such as kubectl explain deployment.spec.template.
spec.containers to discover container fields. This is especially useful when writing or troubleshooting manifests, because it reduces guesswork and prevents invalid YAML fields that would be rejected by the API server. It also helps when APIs evolve: you can confirm which fields exist in your cluster's current version and what they mean.
The other commands do different things. kubectl api-resources lists resource types and their shortnames, whether they are namespaced, and supported verbs-useful discovery, but not detailed field definitions.
kubectl get --help shows CLI usage help for kubectl get, not the Kubernetes object schema. kubectl show is not a standard kubectl subcommand.
From a Kubernetes "declarative configuration" perspective, correct manifests are critical: controllers reconcile desired state from spec, and subtle field mistakes can change runtime behavior. kubectl explain is a built-in way to learn the schema and write manifests that align with the Kubernetes API's expectations. That's why it' s commonly recommended in Kubernetes documentation and troubleshooting workflows.
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NEW QUESTION # 58
A DevOps engineer must ensure Pods in the testing namespace cannot run with elevated privileges. Which action correctly enforces this restriction?
Answer: D
Explanation:
Pod Security Admission enforces Pod Security Standards at the namespace level. Applying the restricted enforcement profile to the testing namespace prevents privileged containers and other unsafe privilege-escalation settings.
NEW QUESTION # 59
Consider the following Prometheus query: kube_pod_container_resource_requests_cpu_cores{pod="my-app-pod", container="my-app- container"} > 0.5 What does this query identify? Select all that apply.
Answer: A
Explanation:
The correct answer is A . The Prometheus query 'kube_pod_container_resource_requests_cpu_cores{pod="my-app-pod", container="my-app-container"} > 0.5' specifically targets the 'kube_pod_container_resource_requests_cpu_cores• metric, which represents the amount of CPU resources requested by a container in a pod. The query filters for pods with the name "my-app-pod" and containers with the name "my-app-container." The 0.5 condition indicates that the query only returns results where the requested CPU cores are greater than 0.5. The other options are incorrect: B : The query does not monitor the actual CPU usage. It focuses on the requested CPU resources, not the currently consumed CPIJ. C : This query does not relate to CPU limits. It focuses on the requested CPU resources, not the defined limits. D : While a pod exceeding its requested resources might indicate resource constraints, this query doesn't provide information about the actual CPU usage, only the requested resources. E : The query does not contain information about the node's CPU capacity. It focuses solely on the resources requested by a specific container within a pod.
NEW QUESTION # 60
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