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| Section | Objectives |
|---|---|
| Topic 1: Cloud Native Observability | - Monitoring and logging
|
| Topic 2: Kubernetes Fundamentals | - Core Kubernetes concepts
|
| Topic 3: Container Orchestration | - Container lifecycle and scheduling
|
| Topic 4: Cloud Native Application Delivery | - CI/CD and deployment strategies
|
| Topic 5: Cloud Security and Governance | - Security fundamentals in cloud native systems
|
| Topic 6: Cloud Native Architecture | - Microservices and distributed systems
|
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NEW QUESTION # 35
You are working on a Kubernetes deployment for a microservices-based application. You need to enforce consistent configuration across different environments (development, staging, production). Which of the following approaches is most appropriate?
Answer: E
Explanation:
Kubernetes ConfigMaps provide a native mechanism for storing and managing configuration data in a central location. This allows for consistent configuration across different environments and simplifies the process of updating configurations without modifying the application code.
NEW QUESTION # 36
You have a Kubernetes cluster with multiple nodes, and you want to ensure that traffic is routed to pods based on labels. Which networking feature should you use?
Answer: D
Explanation:
The correct answer is 'Service of type 'ClusterlP' with selectors'. This approach allows you to define a Service with a 'ClusterlP' and specify a selector that matches pods with specific labels. Kubernetes will then route traffic to pods that have the matching labels. 'NetworkPolicy' is used for network access control, not for load balancing based on labels. 'Service of type 'LoadBalancer" is used for exposing services to the external network. 'Ingress resource' manages external traffic to the cluster 'Pod affinity and anti-affinity' are used for scheduling pods on specific nodes based on labels.
NEW QUESTION # 37
Which is an industry-standard container runtime with an "emphasis" on simplicity, robustness, and portability?
Answer: A
Explanation:
containerd is a widely adopted, industry-standard container runtime known for simplicity, robustness, and portability, so C is correct. containerd originated as a core component extracted from Docker and has become a common runtime across Kubernetes distributions and managed services. It implements container lifecycle management (image pull, unpack, container execution, snapshotting) and typically delegates low-level container execution to an OCI runtime like runc.
In Kubernetes, kubelet communicates with container runtimes through CRI. containerd provides a CRI plugin (or can be integrated via CRI implementations) that makes it a first-class choice for Kubernetes nodes. This aligns with the runtime landscape after dockershim removal: Kubernetes users commonly run containerd or CRI-O as the node runtime.
Option A (CRI-O) is also a CRI-focused runtime and is valid in Kubernetes contexts, but the phrasing "industry-standard ... emphasis on simplicity, robustness, and portability" is strongly associated with containerd's positioning and broad cross-platform adoption beyond Kubernetes. Option B (LXD) is a system container manager (often associated with LXC) and not the standard Kubernetes runtime in mainstream CRI discussions. Option D (kata-runtime) is associated with Kata Containers, which focuses on stronger isolation by running containers inside lightweight VMs; that is a security-oriented sandbox approach rather than a simplicity/portability "industry standard" baseline runtime.
From a cloud-native operations point of view, containerd's popularity comes from its stable API, strong ecosystem support, and alignment with OCI standards. It integrates cleanly with image registries, supports modern snapshotters, and is heavily used in production by many Kubernetes providers. Therefore, the best verified answer is C: containerd.
NEW QUESTION # 38
What is a key advantage of using a DaemonSet in Kubernetes for deploying cluster-wide services such as logging or monitoring agents?
Answer: D
Explanation:
A DaemonSet ensures that a copy of the specified Pod runs on every eligible node. When new nodes join the cluster, Kubernetes automatically schedules the Pod on them, making DaemonSets well suited for node-level logging and monitoring agents.
NEW QUESTION # 39
What are the two steps performed by the kube-scheduler to select a node to schedule a pod?
Answer: D
Explanation:
The kube-scheduler selects a node in two main phases: filtering and scoring, so C is correct. First, filtering identifies which nodes are feasible for the Pod by applying hard constraints. These include resource availability (CPU/memory requests), node taints/tolerations, node selectors and required affinities, topology constraints, and other scheduling requirements. Nodes that cannot satisfy the Pod's requirements are removed from consideration.
Second, scoring ranks the remaining feasible nodes using priority functions to choose the "best" placement. Scoring can consider factors like spreading Pods across nodes/zones, packing efficiency, affinity preferences, and other policies configured in the scheduler. The node with the highest score is selected (with tie-breaking), and the scheduler binds the Pod by setting spec.nodeName.
Option B ("filtering and selecting") is close but misses the explicit scoring step that is central to scheduler design. The scheduler does "select" a node, but the canonical two-step wording in Kubernetes scheduling is filtering then scoring. Options A and D are not how scheduler internals are described.
Operationally, understanding filtering vs scoring helps troubleshoot scheduling failures. If a Pod can't be scheduled, it failed in filtering-kubectl describe pod often shows "0/... nodes are available" reasons (insufficient CPU, taints, affinity mismatch). If it schedules but lands in unexpected places, it's often about scoring preferences (affinity weights, topology spread preferences, default scheduler profiles).
So the verified correct answer is C: kube-scheduler uses Filtering and Scoring.
NEW QUESTION # 40
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