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Linux Foundation Kubernetes and Cloud Native Associate Sample Questions (Q72-Q77):
NEW QUESTION # 72
You are building a serverless application on Google Cloud Functions that needs to interact with a Google Cloud SQL database. How can you securely connect your Cloud Functions to the Cloud SQL instance?
- A. Configure a Cloud SQL proxy service to forward connections to the Cloud SQL instance.
- B. Use a dedicated virtual machine (VM) to host a database connector that communicates with both Cloud Functions and Cloud SQL.
- C. Utilize Google Cloud IAM to grant Cloud Functions access to the Cloud SQL instance.
- D. Use Google Cloud Functions' built-in database connector to access Cloud SQL.
- E. Use a service account with appropriate permissions to connect to Cloud SQL from Cloud Functions.
Answer: E
Explanation:
The most secure and recommended approach is to use a service account with appropriate permissions to connect to Cloud SQL from Cloud Functions. This method provides granular control over access and ensures that Cloud Functions only have the necessary permissions to interact with the database. Option A is not a standard feature. Option B is an alternative but adds complexity. Option C introduces extra infrastructure and potential security risks. Option D, while important for overall security, doesnt specifically address database connections.
NEW QUESTION # 73
What is the main difference between Argo vs. Flux CD?
- A. Argo is push-based, and Flux is pull-based
- B. No difference; both are pull-based
- C. Argo is pull-based, and Flux is push-based
- D. No difference; both are push-based
Answer: A
Explanation:
AgroCD: https://argo-cd.readthedocs.io/en/stable/developer-guide/ci/#can-i-retrigger-the-checks-without-pushing-a-new-commit
FluxCD: https://fluxcd.io/
NEW QUESTION # 74
To specify a Kubernetes object which language is used?
- A. YAML
- B. Go
- C. Python
- D. Node
- E. JSON
Answer: A
Explanation:
https://kubernetes.io/docs/concepts/overview/working-with-objects/kubernetes-objects/
NEW QUESTION # 75
What is the smallest possible unit in Kubernetes to run a container?
- A. container
- B. service
- C. docker
- D. pod
Answer: D
Explanation:
https://kubernetes.io/docs/concepts/workloads/pods/
NEW QUESTION # 76
You're deploying a microservices application in a Kubernetes cluster. How can you use Jaeger for distributed tracing to troubleshoot a performance issue within the application?
- A. Instrument your application code to emit tracing data.
- B. Create a custom Prometheus exporter that integrates with Jaeger.
- C. Use Jaeger's IJI to visualize the tracing data and analyze request flow across different services.
- D. Configure a dedicated Jaeger deployment and configure your application to send tracing data to it.
- E. Deploy Jaeger as a sidecar container within each pod.
Answer: A,C,D
Explanation:
The correct answers are B, D, and E . To effectively use Jaeger for distributed tracing, you need to perform these steps: B: Instrument your application code to emit tracing data. Jaeger integrates with application code to track requests across services. It's common to use libraries like OpenTelemetry or Jaeger's client libraries to inject tracing data into your application's code. D: Configure a dedicated Jaeger deployment and configure your application to send tracing data to it. Jaeger needs its own deployment to collect and process the tracing data. You need to set up a Jaeger instance within your Kubernetes cluster and configure your application to send its tracing data to this instance. E: Use Jaeger's Ul to visualize the tracing data and analyze request flow across different services. Once the tracing data is collected, Jaeger provides a user interface to visualize the flow of requests across your microservices, identify bottlenecks, and analyze performance issues. The Jaeger UI allows you to drill down into individual traces, view the spans within a trace, and gain insights into latency, errors, and dependencies between services.
NEW QUESTION # 77
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