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| Section | Weight | Objectives |
|---|---|---|
| Container Orchestration | 22% | - Orchestration Principles
|
| Cloud Native Application Delivery | 8% | - Delivery Models
|
| Kubernetes Fundamentals | 46% | - Scheduling and Administration
|
| Cloud Native Architecture | 16% | - Cloud Native Principles
|
| Cloud Native Observability | 8% | - Monitoring & Metrics
|
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NEW QUESTION # 71
Which of the following best describes horizontally scaling an application deployment?
Answer: B
Explanation:
Horizontal scaling means changing how many instances of an application are running, not changing how big each instance is. Therefore, the best description is C: adding/removing application instances of the same application to meet demand. In Kubernetes, "instances" typically correspond to Pod replicas managed by a controller like a Deployment. When you scale horizontally, you increase or decrease the replica count, which increases or decreases total throughput and resilience by distributing load across more Pods.
Option A is about cluster/node scaling (adding or removing nodes), which is infrastructure scaling typically handled by a cluster autoscaler in cloud environments. Node scaling can enable more Pods to be scheduled, but it's not the definition of horizontal application scaling itself. Option D describes vertical scaling-adding
/removing CPU or memory resources to a given instance (Pod/container) by changing requests/limits or using VPA. Option B is vague and not the standard definition.
Horizontal scaling is a core cloud-native pattern because it improves availability and elasticity. If one Pod fails, other replicas continue serving traffic. In Kubernetes, scaling can be manual (kubectl scale deployment
... --replicas=N) or automatic using the Horizontal Pod Autoscaler (HPA). HPA adjusts replicas based on observed metrics like CPU utilization, memory, or custom/external metrics (for example, request rate or queue length). This creates responsive systems that can handle variable traffic.
From an architecture perspective, designing for horizontal scaling often means ensuring your application is stateless (or manages state externally), uses idempotent request handling, and supports multiple concurrent instances. Stateful workloads can also scale horizontally, but usually with additional constraints (StatefulSets, sharding, quorum membership, stable identity).
So the verified definition and correct choice is C.
=========
NEW QUESTION # 72
What is the primary purpose of a Horizontal Pod Autoscaler (HPA) in Kubernetes?
Answer: B
NEW QUESTION # 73
Which of the following is a good habit for cloud native cost efficiency?
Answer: A
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.
=========
NEW QUESTION # 74
You are implementing a new security policy for your Kubernetes cluster. The policy requires that all pods running in the cluster must authenticate with a specific identity provider before they are allowed to access any resources. Which Kubernetes component is responsible for enforcing this authentication policy?
Answer: C
Explanation:
The kube-apiserver component acts as the central control plane for Kubernetes. It handles all communication and requests from other components, including authentication and authorization. It enforces security policies by verifying credentials and granting access based on configured rules.
NEW QUESTION # 75
A Pod was created that shows the ErrImagePull status. What is most the cause of the error?
Answer: C
Explanation:
ErrImagePull indicates that the kubelet attempted to pull the container image but failed, typically due to an incorrect image name, missing repository access, or authentication issues preventing the image from being downloaded.
NEW QUESTION # 76
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