In the DevOps lifecycle, what is the difference between continuous delivery and continuous deployment? (Choose two.)
The two correct differences between continuous delivery and continuous deployment in the DevOps lifecycle are: Continuous delivery is a process that initiates deployment manually, while continuous deployment is based on automating the deployment process. In continuous delivery, the software is ready for deployment, but the decision to deploy is made manually by a human. On the other hand, continuous deployment automates the deployment process, and once the software passes all the necessary tests and quality checks, it is automatically deployed without human intervention. Continuous delivery involves automatic deployment to a development environment, while continuous deployment involves automatic deployment to a production environment. In continuous delivery, the software is automatically deployed to a development or staging environment for further testing and validation. However, the actual deployment to the production environment is performed manually. In continuous deployment, the software is automatically deployed to the production environment, eliminating the need for manual intervention in the deployment process. These differences highlight the level of automation and human involvement in the deployment process between continuous delivery and continuous deployment approaches in the DevOps lifecycle.
Kubernetes includes various elements such as compute, network, and storage. Compute is essentially CPU (units) and memory (bytes). Within an OKE cluster, what is considered to be the smallest unit of deployment with respect to compute?
(CHK_4>3) Your development team decides to create and deploy some business logic to serverless Oracle Functions. You are asked to help facilitate the monitoring, logging, and tracing of these services. Which is NOT valid about troubleshooting Oracle Functions?
The option that is NOT valid about troubleshooting Oracle Functions is: 'Oracle Functions tracing is enabled at the function level.' In Oracle Functions, tracing is not enabled at the function level. Instead, tracing is enabled at the application level. When you enable tracing for an application, it applies to all the functions within that application. Tracing allows you to capture detailed information about the execution flow and performance of the functions, helping you analyze and debug issues. The other options mentioned are valid: Oracle Functions invocation logs are enabled at the application level. Invocation logs provide visibility into the details of function invocations, including input, output, duration, and any error messages. These logs are generated and stored by Oracle Functions, and you can access them for troubleshooting and monitoring purposes. Oracle Functions invocation is enabled by default. Once you deploy a function, it becomes invocable by default. You can configure different triggers to invoke the function, such as HTTP requests, scheduled events, or events from other Oracle Cloud Infrastructure services. Oracle Functions metrics are available at both the function and application level. Metrics provide insights into the usage, performance, and behavior of functions. They can include metrics such as invocations per minute, average duration, and error counts. These metrics can be viewed in the Oracle Cloud Infrastructure Console or accessed programmatically through APIs. It's important to note that the specific configuration and behavior of monitoring, logging, and tracing in Oracle Functions may depend on the version, configuration, and options you have chosen. It is recommended to refer to the Oracle Functions documentation and consult the official documentation for accurate and up-to-date information on troubleshooting and monitoring Oracle Functions.
As a Cloud Native developer, you develop two services in Node.js and deploy them to two different Container Engine for Kubernetes (OKE) clusters that use the same Virtual Cloud Network (VCN). Your security team wants to analyze the network communication between them. How can this requirement be met in the most cost-effective way?
The best answer is: 'Use the OCI Logging service and enable VCN flow logs.' To meet the requirement of analyzing network communication between two services deployed in different Container Engine for Kubernetes (OKE) clusters within the same Virtual Cloud Network (VCN) in a cost-effective way, you can use the OCI Logging service and enable VCN flow logs. The VCN flow logs feature in OCI allows you to capture and log network traffic information for your VCN resources. By enabling VCN flow logs, you can monitor and analyze the network communication between your services without the need for additional third-party logging services or tools. Enabling VCN flow logs provides visibility into the network traffic, including source and destination IP addresses, ports, protocols, and other relevant details. This information can be collected and stored in the OCI Logging service, where you can analyze and gain insights into the network communication patterns between your services. By leveraging the built-in capabilities of the OCI Logging service and enabling VCN flow logs, you can fulfill the security team's requirement for network communication analysis in a cost-effective manner. This eliminates the need for deploying additional third-party logging services or tools, reducing complexity and potential costs associated with their setup and maintenance. The other options mentioned are not the most cost-effective or suitable solutions for analyzing network communication in this scenario: Deploying a third-party logging service and aggregating the network flow logs would introduce additional costs and complexity, which may not be necessary considering the built-in capabilities provided by OCI. Rewriting the application to send logs to an outside log aggregator would not directly address the requirement of analyzing network communication between the services. It would focus more on application-level logs rather than network-level analysis. Deploying Wireshark and intercepting packets would require additional infrastructure setup and maintenance, which may not be the most cost-effective approach for network analysis in this scenario.
What can you use to dynamically make Kubernetes resources discoverable to public DNS servers? (Choose the best answer.)
To dynamically make Kubernetes resources discoverable to public DNS servers, you can use ExternalDNS. ExternalDNS is a Kubernetes add-on that automates the management of DNS records for your Kubernetes services and ingresses. It can be configured to monitor the changes in your Kubernetes resources and automatically update DNS records in a supported DNS provider. By integrating ExternalDNS with your Kubernetes cluster, you can ensure that the DNS records for your services and ingresses are automatically created, updated, or deleted based on changes in your Kubernetes resources. This allows your Kubernetes resources to be discoverable by external systems through public DNS servers.