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| Section | Weight | Objectives |
|---|---|---|
| Container Orchestration | 22% | - Service Mesh
|
| Kubernetes Fundamentals | 46% | - Kubernetes Architecture
|
| Cloud Native Observability | 8% | - Tracing
|
| Cloud Native Application Delivery | 8% | - Deployment Strategies
|
| Cloud Native Architecture | 16% | - Infrastructure and Practices
|
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NEW QUESTION # 62
Your application relies on a backend database service. Using Istio, you want to configure a circuit breaker pattern to prevent cascading failures if the database becomes unresponsive. How would you implement this?
Answer: E
Explanation:
Istio's VirtualService and DestinationRule features allow you to configure fallback services. In the case of the database being unavailable, you can define a fallback service or mechanism to handle the request. Option B helps test circuit breaker behavior but doesn't implement it. Option C could be used for retries, but doesn't address the circuit breaker pattern. Option D is related to request timeouts, not circuit breaking. Option E is a manual approach, while Istio provides a more integrated solution.
NEW QUESTION # 63
Which GitOps engine can be used to orchestrate parallel jobs on Kubernetes?
Answer: B
Explanation:
Argo Workflows is a Kubernetes-native workflow engine that supports orchestration of parallel jobs and complex workflows using custom resources.
NEW QUESTION # 64
Which of the following cloud native proxies is used for ingress/egress in a service mesh and can also serve as an application gateway?
Answer: D
Explanation:
Envoy Proxy is a high-performance, cloud-native proxy widely used for ingress and egress traffic management in service mesh architectures, and it can also function as an application gateway. It is the foundational data-plane component for popular service meshes such as Istio, Consul, and AWS App Mesh, making option C the correct answer.
In a service mesh, Envoy is typically deployed as a sidecar proxy alongside each application Pod. This allows Envoy to transparently intercept and manage all inbound and outbound traffic for the service. Through this model, Envoy enables advanced traffic management features such as load balancing, retries, timeouts, circuit breaking, mutual TLS, and fine-grained observability without requiring application code changes.
Envoy is also commonly used at the mesh boundary to handle ingress and egress traffic. When deployed as an ingress gateway, Envoy acts as the entry point for external traffic into the mesh, performing TLS termination, routing, authentication, and policy enforcement. As an egress gateway, it controls outbound traffic from the mesh to external services, enabling security controls and traffic visibility. These capabilities allow Envoy to serve effectively as an application gateway, not just an internal proxy.
Option A, "Frontend proxy," is a generic term and not a specific cloud-native component. Option B, kube- proxy, is responsible for implementing Kubernetes Service networking rules at the node level and does not provide service mesh features or gateway functionality. Option D, "Reverse proxy," is a general architectural pattern rather than a specific cloud-native proxy implementation.
Envoy's extensibility, performance, and deep integration with Kubernetes and service mesh control planes make it the industry-standard proxy for modern cloud-native networking. Its ability to function both as a sidecar proxy and as a centralized ingress or egress gateway clearly establishes Envoy proxy as the correct and verified answer.
NEW QUESTION # 65
Which key-value store is used to persist Kubernetes cluster data?
Answer: A
Explanation:
Kubernetes stores its cluster state (API objects) in etcd, making A correct. etcd is a distributed, strongly consistent key-value store that serves as the source of truth for the Kubernetes control plane. When you create or update objects such as Pods, Deployments, ConfigMaps, Secrets, or Nodes, the kube-apiserver validates the request and then persists the desired state into etcd. Controllers and the scheduler watch the API for changes (which ultimately reflect etcd state) and reconcile the cluster to match that desired state.
etcd's consistency guarantees are crucial. Kubernetes relies on accurate, up-to-date state to make scheduling decisions, enforce RBAC/admission policies, coordinate leader elections, and ensure controllers behave correctly. etcd uses the Raft consensus algorithm to replicate data among members and requires quorum for writes, enabling fault tolerance when deployed in HA configurations (commonly three or five members).
The other options are incorrect in Kubernetes' standard architecture. ZooKeeper is a distributed coordination system used by some other platforms, but Kubernetes does not use it as its primary datastore. Redis is an in-memory data store used for caching or messaging, not as Kubernetes' authoritative state store. "ControlPlaneStore" is not a standard Kubernetes component.
Operationally, etcd health is one of the most important determinants of cluster reliability. Slow disk I/O or unstable networking can degrade etcd performance and cause API latency spikes. Backup and restore procedures for etcd are critical disaster-recovery practices, and securing etcd (TLS, access restrictions) is essential because it may contain sensitive data (e.g., Secrets-often base64-encoded, and optionally encrypted at rest depending on configuration).
Therefore, the verified Kubernetes datastore is etcd, option A.
NEW QUESTION # 66
Which of the following options is true about considerations for large Kubernetes clusters?
Answer: D
Explanation:
The correct answer is C: Kubernetes scalability guidance commonly cites support up to 5000 nodes and recommends no more than 110 Pods per node. The "110 Pods per node" recommendation is a practical limit based on kubelet, networking, and IP addressing constraints, as well as performance characteristics for scheduling, service routing, and node-level resource management. It is also historically aligned with common CNI/IPAM defaults where node Pod CIDRs are sized for ~110 usable Pod IPs.
Why the other options are incorrect: A and D reference "containers per node," which is not the standard sizing guidance (Kubernetes typically discusses Pods per node). B's "500 Pods per node" is far above typical recommended limits for many environments and would stress IPAM, kubelet, and node resources significantly.
In large clusters, several considerations matter beyond the headline limits: API server and etcd performance, watch/list traffic, controller reconciliation load, CoreDNS scaling, and metrics/observability overhead. You must also plan for IP addressing (cluster CIDR sizing), node sizes (CPU/memory), and autoscaling behavior. On each node, kubelet and the container runtime must handle churn (starts/stops), logging, and volume operations. Networking implementations (kube-proxy, eBPF dataplanes) also have scaling characteristics.
Kubernetes provides patterns to keep systems stable at scale: request/limit discipline, Pod disruption budgets, topology spread constraints, namespaces and quotas, and careful observability sampling. But the exam-style fact this question targets is the published scalability figure and per-node Pod recommendation.
Therefore, the verified true statement among the options is C.
NEW QUESTION # 67
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