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| Section | Objectives |
|---|---|
| Topic 1: Cloud Native Observability | - Monitoring and logging
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| Topic 2: Cloud Security and Governance | - Security fundamentals in cloud native systems
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| Topic 3: Cloud Native Application Delivery | - CI/CD and deployment strategies
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| Topic 4: Cloud Native Architecture | - Microservices and distributed systems
|
| Topic 5: Container Orchestration | - Container lifecycle and scheduling
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| Topic 6: Kubernetes Fundamentals | - Core Kubernetes concepts
|
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NEW QUESTION # 66
Which command lists the running containers in the current Kubernetes namespace?
Answer: C
NEW QUESTION # 67
You are troubleshooting a performance issue with an application running in a Kubernetes cluster. You have access to Prometheus and Jaeger, but the Jaeger Ul shows that a specific request took an unusually long time to process. Which metric in Prometheus can help you identify the specific component or service within the application that caused the delay?
Answer: A
Explanation:
The correct answer is E . The metric 'http_request_duration_seconds* is typically used to track the time taken for HTTP requests to be processed. In this case, by looking at the 'http_request_duration_seconds' metric for the specific request identified in Jaeger, you can see the duration of each stage of the request processing. This can pinpoint the specific component or service where the delay occurred. The other options are not relevant to identifying the specific component or service causing the delay: A : Shows CPU resource requests for a pod. B : Shows the pod's phase. C : Shows the number of replicas in a deployment. D : Shows the restart count for a pod's containers. These metrics do not provide information about the processing time of individual requests.
NEW QUESTION # 68
CI/CD stands for:
Answer: C
Explanation:
CI/CD is a foundational practice for delivering software rapidly and reliably, and it maps strongly to cloud native delivery workflows commonly used with Kubernetes. CI stands for Continuous Integration: developers merge code changes frequently into a shared repository, and automated systems build and test those changes to detect issues early. CD is commonly used to mean Continuous Delivery or Continuous Deployment depending on how far automation goes. In many certification contexts and simplified definitions like this question, CD is interpreted as Continuous Deployment, meaning every change that passes the automated pipeline is automatically released to production. That matches option D.
In a Kubernetes context, CI typically produces artifacts such as container images (built from Dockerfiles or similar build definitions), runs unit/integration tests, scans dependencies, and pushes images to a registry. CD then promotes those images into environments by updating Kubernetes manifests (Deployments, Helm charts, Kustomize overlays, etc.). Progressive delivery patterns (rolling updates, canary, blue/green) often use Kubernetes-native controllers and Service routing to reduce risk.
Why the other options are incorrect: "Continuous Development" isn't the standard "D" term; it's ambiguous and not the established acronym expansion. "Cloud Integration/Cloud Development" is unrelated. Continuous Delivery (in the stricter sense) means changes are always in a deployable state and releases may still require a manual approval step, while Continuous Deployment removes that final manual gate. But because the option set explicitly includes "Continuous Deployment," and that is one of the accepted canonical expansions for CD, D is the correct selection here.
Practically, CI/CD complements Kubernetes' declarative model: pipelines update desired state (Git or manifests), and Kubernetes reconciles it. This combination enables frequent releases, repeatability, reduced human error, and faster recovery through automated rollbacks and controlled rollout strategies.
NEW QUESTION # 69
In which framework do the developers no longer have to deal with capacity, deployments, scaling and fault tolerance, and OS?
Answer: B
Explanation:
Serverless is the model where developers most directly avoid managing server capacity, OS operations, and much of the deployment/scaling/fault-tolerance mechanics, which is why D is correct. In serverless computing (commonly Function-as-a-Service, FaaS, and managed serverless container platforms), the provider abstracts away the underlying servers. You typically deploy code (functions) or a container image, define triggers (HTTP events, queues, schedules), and the platform automatically provisions the required compute, scales it based on demand, and handles much of the availability and fault tolerance behind the scenes.
It's important to compare this to Kubernetes: Kubernetes does automate scheduling, self-healing, rolling updates, and scaling, but it still requires you (or your platform team) to design and operate cluster capacity, node pools, upgrades, runtime configuration, networking, and baseline reliability controls. Even in managed Kubernetes services, you still choose node sizes, scale policies, and operational configuration. Kubernetes reduces toil, but it does not eliminate infrastructure concerns in the same way serverless does.
Docker Swarm and Mesos are orchestration platforms that schedule workloads, but they also require managing the underlying capacity and OS-level aspects. They are not "no longer have to deal with capacity and OS" frameworks.
From a cloud native viewpoint, serverless is about consuming compute as an on-demand utility. Kubernetes can be a foundation for a serverless experience (for example, with event-driven autoscaling or serverless frameworks), but the pure framework that removes the most operational burden from developers is serverless.
NEW QUESTION # 70
Which of the following is not the required field to describe Kubernetes objects?
Answer: C
NEW QUESTION # 71
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