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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Cloud Native Architecture | 16% | - Architecture Concepts
|
| Topic 2: Kubernetes Fundamentals | 46% | - Containers
|
| Topic 3: Cloud Native Observability | 8% | - Logging
|
| Topic 4: Cloud Native Application Delivery | 8% | - CI/CD
|
| Topic 5: Container Orchestration | 22% | - Orchestration Fundamentals
|
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NEW QUESTION # 327
When modifying an existing Helm release to apply new configuration values, which approach is the best practice?
Answer: B
Explanation:
Helm is a package manager for Kubernetes that provides a declarative and versioned approach to application deployment and lifecycle management. When updating configuration values for an existing Helm release, the recommended and best-practice approach is to use helm upgrade, optionally with the --set flag or a values file, to apply the new configuration while preserving the release's history.
Option A is correct because helm upgrade updates an existing release in a controlled and auditable manner.
Helm stores each revision of a release, allowing teams to inspect past configurations and roll back to a previous known-good state if needed. Using --set enables quick overrides of individual values, while using -f values.yaml supports more complex or repeatable configurations. This approach aligns with GitOps and infrastructure-as-code principles, ensuring consistency and traceability.
Option B is incorrect because modifying Helm-managed resources directly with kubectl edit breaks Helm's state tracking. Helm maintains a record of the desired state for each release, and manual edits can cause configuration drift, making future upgrades unpredictable or unsafe. Kubernetes documentation and Helm guidance strongly discourage modifying Helm-managed resources outside of Helm itself.
Option C is incorrect because deleting and reinstalling a release discards the release history and may cause unnecessary downtime or data loss, especially for stateful applications. Helm's upgrade mechanism is specifically designed to avoid this disruption while still applying configuration changes safely.
Option D is also incorrect because editing chart source files directly and reapplying them bypasses Helm's release management model. While chart changes are appropriate during development, applying them directly to a running release without helm upgrade undermines versioning, rollback, and repeatability.
According to Helm documentation, helm upgrade is the standard and supported method for modifying deployed applications. It ensures controlled updates, preserves operational history, and enables safe rollbacks, making option A the correct and fully verified best practice.
NEW QUESTION # 328
In a cloud native environment, who is usually responsible for maintaining the workloads running across the different platforms?
Answer: D
Explanation:
B (the Site Reliability Engineering team) is correct. In cloud-native organizations, SREs are commonly responsible for the reliability, availability, and operational health of workloads across platforms (multiple clusters, regions, clouds, and supporting services). While responsibilities vary by company, the classic SRE charter is to apply software engineering to operations: build automation, standardize runbooks, manage incident response, define SLOs/SLIs, and continuously improve system reliability.
Maintaining workloads "across different platforms" implies cross-cutting operational ownership: deployments need to behave consistently, rollouts must be safe, monitoring and alerting must be uniform, and incident practices must work across environments. SRE teams typically own or heavily influence the observability stack (metrics/logs/traces), operational readiness, capacity planning, and reliability guardrails (error budgets, progressive delivery, automated rollback triggers). They also collaborate closely with platform engineering and application teams, but SRE is often the group that ensures production workloads meet reliability targets.
Why other options are less correct:
The cloud provider (A) maintains the underlying cloud services, but not your application workloads' correctness, SLOs, or operational processes.
Developers (C) do maintain application code and may own on-call in some models, but the question asks "usually" in cloud-native environments; SRE is the widely recognized function for workload reliability across platforms.
Support Engineering (D) typically focuses on customer support and troubleshooting from a user perspective, not maintaining platform workload reliability at scale.
So, the best and verified answer is B: SRE teams commonly maintain and ensure reliability of workloads across cloud-native platforms.
NEW QUESTION # 329
If a Pod was waiting for container images to download on the scheduled node, what state would it be in?
Answer: C
Explanation:
If a Pod is waiting for its container images to be pulled to the node, it remains in the Pending phase, so D is correct. Kubernetes Pod "phase" is a high-level summary of where the Pod is in its lifecycle. Pending means the Pod has been accepted by the cluster but one or more of its containers has not started yet. That can occur because the Pod is waiting to be scheduled, waiting on volume attachment/mount, or-very commonly- waiting for the container runtime to pull the image.
When image pulling is the blocker, kubectl describe pod <name> usually shows events like "Pulling image
..." and "Successfully pulled image ..." or failures like ImagePullBackOff/ErrImagePull. Even if the node has been assigned (scheduler has set spec.nodeName), the Pod can still be Pending while kubelet and the runtime perform preparation steps.
Why the other phases don't apply:
* Succeeded is for run-to-completion Pods that have finished successfully (typical for Jobs).
* Failed means the Pod terminated and at least one container terminated in failure (and won't be restarted, depending on restartPolicy).
* Unknown is used when the node can't be contacted and the Pod's state can't be reliably determined (rare in healthy clusters).
A subtle but important Kubernetes detail: status "Waiting" reasons like ImagePullBackOff are container states inside .status.containerStatuses, while the Pod phase can still be Pending. So, "waiting for images to download" maps to Pod Pending, with container waiting reasons providing the deeper diagnosis.
Therefore, the verified correct answer is D: Pending.
=========
NEW QUESTION # 330
Which organizational persona creates Service Level Agreements 'SLA', Service Level Objectives
'SLO', and Service Level Indicator 'SLI'?
Answer: A
Explanation:
SREs create SLAs, SLOs, and SLIs to define and implement standards for application and infra- structure reliability.
NEW QUESTION # 331
You're developing a serverless application using AWS Lambda that requires access to environment variables for configuration purposes. How would you securely manage and access these environment variables within your Lambda functions?
Answer: A,E
Explanation:
The most secure and recommended approaches for managing environment variables in AWS Lambda are to use AWS Secrets Manager (B) and AWS Parameter Store (D). Secrets Manager is specifically designed for storing and retrieving sensitive data like API keys, passwords, and other confidential information. Parameter Store allows you to manage configuration parameters, including environment variables, in a centralized and hierarchical manner. Storing environment variables directly in the code (A) is insecure. Configuring them in the Lambda console (C) is not suitable for managing sensitive data. Storing them in a separate file (E) is less secure and less manageable.
NEW QUESTION # 332
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