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| Section | Weight | Objectives |
|---|---|---|
| Kubernetes Fundamentals | 46% | - Containerization Basics
|
| Cloud Native Observability | 8% | - Monitoring & Metrics
|
| Container Orchestration | 22% | - Security & Troubleshooting
|
| Cloud Native Architecture | 16% | - Ecosystem & Landscape
|
| Cloud Native Application Delivery | 8% | - Delivery Models
|
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NEW QUESTION # 300
Which of the following is a good habit for cloud native cost efficiency?
Answer: B
Explanation:
The correct answer is A. In cloud-native environments, costs are highly dynamic: autoscaling changes compute footprint, ephemeral environments come and go, and usage-based billing applies to storage, network egress, load balancers, and observability tooling. Because of this variability, automation is the most sustainable way to achieve cost efficiency. Automated visibility (dashboards, chargeback/showback), anomaly detection, and forecasting help teams understand where spend is coming from and how it changes over time. Automated optimization actions can include right-sizing requests/limits, enforcing TTLs on preview environments, scaling down idle clusters, and cleaning unused resources.
Manual processes (B) don't scale as complexity grows. By the time someone reviews a spreadsheet or dashboard weekly, cost spikes may have already occurred. Automation enables fast feedback loops and guardrails, which is essential for preventing runaway spend caused by misconfiguration (e.g., excessive log ingestion, unbounded autoscaling, oversized node pools).
Option C is not a cost-efficiency "habit." Single-provider strategies may simplify some billing views, but they can also reduce leverage and may not be feasible for resilience/compliance; it's a business choice, not a best practice for cloud-native cost management. Option D is counterproductive: keeping legacy workloads unchanged often wastes money because cloud efficiency typically requires adapting workloads-right-sizing, adopting autoscaling, and using managed services appropriately.
In Kubernetes specifically, cost efficiency is tightly linked to resource management: accurate CPU/memory requests, limits where appropriate, cluster autoscaler tuning, and avoiding overprovisioning. Observability also matters because you can't optimize what you can't measure. Therefore, the best habit is an automated cost optimization approach with strong visibility and forecasting-A.
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NEW QUESTION # 301
Your Kubernetes cluster is experiencing performance issues due to excessive CPU usage by a specific pod. You need to quickly diagnose and resolve the problem. Which tool would be most useful for monitoring and troubleshooting the pod's resource consumption?
Answer: C
Explanation:
The *kubectl top' command provides real-time resource usage statistics for pods, including CPU and memory consumption. It allows you to quickly identify pods that are using excessive resources and pinpoint the cause of the performance bottleneck. This makes it an effective tool for troubleshooting performance problems.
NEW QUESTION # 302
What can be used to create a job that will run at specified times/dates or on a repeating schedule?
Answer: C
Explanation:
The correct answer is D: CronJob. A Kubernetes CronJob is specifically designed for creating Jobs on a schedule-either at specified times/dates (expressed via cron syntax) or on a repeating interval (hourly, daily, weekly). When the schedule triggers, the CronJob controller creates a Job, and the Job controller creates the Pods that execute the workload to completion.
Option A (Job) is not inherently scheduled. A Job runs when you create it, and it continues until it completes successfully or fails according to its retry/backoff behavior. If you want it to run periodically, you need something else to create the Job each time. CronJob is the built-in mechanism for that scheduling.
Options B and C are not standard Kubernetes workload objects. Kubernetes does not include "CalendarJob" or "BatchJob" as official API kinds. The scheduling primitive is CronJob.
CronJobs also include important operational controls: concurrency policies prevent overlapping runs, deadlines control missed schedules, and history limits manage old Job retention. This makes CronJobs more robust than ad-hoc scheduling approaches and keeps the workload lifecycle visible in the Kubernetes API (status/events/logs). It also means you can apply standard Kubernetes patterns: use a service account with least privilege, mount Secrets/ConfigMaps, run in specific namespaces, and manage resource requests/limits so that scheduled workloads don't destabilize the cluster.
So the correct Kubernetes resource for scheduled and repeating job execution is CronJob (D).
NEW QUESTION # 303
What does "continuous" mean in the context of CI/CD?
Answer: C
Explanation:
The correct answer is C: in CI/CD, "continuous" implies frequent releases, automation, repeatability, and fast feedback/processing. The intent is to reduce batch size and latency between code change and validation/deployment. Instead of integrating or releasing in large, risky chunks, teams integrate changes continually and rely on automation to validate and deliver them safely.
"Continuous" does not mean "periodic" (which eliminates B and D). It also does not mean "manual processes" (which eliminates A and B). Automation is core: build, test, security checks, and deployment steps are consistently executed by pipeline systems, producing reliable outcomes and auditability.
In practice, CI means every merge triggers automated builds and tests so the main branch stays in a healthy state. CD means those validated artifacts are promoted through environments with minimal manual steps, often including progressive delivery controls (canary, blue/green), automated rollbacks on health signal failures, and policy checks. Kubernetes works well with CI/CD because it is declarative and supports rollout primitives: Deployments, readiness probes, and rollback revision history enable safer continuous delivery when paired with pipeline automation.
Repeatability is a major part of "continuous." The same pipeline should run the same way every time, producing consistent artifacts and deployments. This reduces "works on my machine" issues and shortens incident resolution because changes are traceable and reproducible. Fast processing and frequent releases also mean smaller diffs, easier debugging, and quicker customer value delivery.
So, the combination that accurately reflects "continuous" in CI/CD is frequent + automated + repeatable + fast, which is option C.
NEW QUESTION # 304
What is the goal of load balancing?
Answer: A
NEW QUESTION # 305
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