To troubleshoot issues in OCI, the option that is not valid is: Trace performance issues in the Application Performance Monitoring service by enabling Function traces. While the Application Performance Monitoring service in OCI allows you to monitor and trace the performance of your applications, it is specifically designed for monitoring OCI Functions (serverless functions) and does not directly apply to all types of applications. The other options mentioned, such as configuring logs in the OCI Logging service, leveraging OCI Cloud Guard for debug logs, viewing service metrics in the OCI Monitoring service, and using OCI Service Connector Hub for log forwarding, are valid options for troubleshooting and monitoring applications in OCI.

The option that is NOT available when viewing the Application metrics in the Oracle Cloud Infrastructure (OCI) Console is: 'The number of retries made by the function before failing due to an error.' When viewing the Application metrics in the OCI Console for Oracle Functions, you can typically see metrics related to the performance and usage of your functions. These metrics provide insights into how your functions are performing and being utilized. The following metrics are usually available: The number of requests to invoke a function that failed due to throttling: This metric indicates the number of requests that were not processed by the function due to reaching the configured concurrency limit or throttling settings. The length of time a function runs for: This metric represents the duration of each function invocation, measuring the time it takes for the function to complete its execution. The number of requests to invoke a function that failed with an error response: This metric counts the number of requests that encountered an error during the function invocation, resulting in a failed response. However, the number of retries made by the function before failing due to an error is not typically available as part of the Application metrics in the OCI Console. The retries made by the function are usually handled at the invoker level, and the specific details of retries may not be captured as part of the application-level metrics. It's important to note that the availability of metrics and their specific details may vary depending on the version and configuration of Oracle Functions and the monitoring setup. It is recommended to refer to the Oracle Functions documentation and consult the official documentation for accurate and up-to-date information on available metrics.

To mandate image verification when deploying container images to Oracle Cloud Infrastructure (OCI) Container Engine for Kubernetes (OKE) clusters, you should enable image verification policies separately for each OKE cluster. This is enforced at the cluster level. Enabling image verification policies at the cluster level ensures that all container images deployed to the OKE cluster are automatically verified against the specified master encryption key (MEK). This helps maintain the security and integrity of the deployed microservices by ensuring that only signed and trusted container images are used. Enabling image verification policies at the cluster level allows for consistent and centralized enforcement of the verification process across all nodes and node pools within the cluster. It provides a standardized approach to image verification for the entire cluster, simplifying management and ensuring compliance with the organization's mandate. Enabling image verification policies separately for each node pool or at the pod level would introduce complexity and potential inconsistencies in the verification process. Therefore, enforcing image verification at the cluster level is the recommended approach.

The correct command to rebuild the API docker image and tag it as OCIdevops/api:latest is: docker build -t OCIdevops/api:latest The docker build command is used to build a Docker image from a Dockerfile. The -t flag is used to specify the name and optionally a tag for the image. In this case, the name of the image is OCIdevops/api and the tag is latest. By running this command, the Docker image will be recreated based on the instructions in the Dockerfile and tagged with the specified name and tag.

The two main reasons to choose a serverless architecture are: Automatic horizontal scaling: Serverless architectures allow for automatic scaling of resources based on demand. The infrastructure automatically provisions and scales resources as needed, ensuring that applications can handle varying workloads efficiently. This eliminates the need for manual scaling and optimizes resource utilization. Reduced operational cost: Serverless architectures follow a pay-per-use model, where you are billed only for the actual execution time and resources consumed by your functions. This leads to cost savings as you don't have to pay for idle resources. Additionally, serverless architectures remove the need for managing and maintaining servers, reducing operational overhead and associated costs. Explanation:: No need for integration testing: Integration testing is still necessary in serverless architectures to ensure that functions integrate correctly with other components and services. Serverless functions can interact with various event sources, databases, and APIs, and testing is required to verify the integration points. Improved in-function state management: Serverless architectures typically encourage stateless functions that operate on short-lived requests or events. While there are mechanisms to manage state within a function, serverless architectures are designed to be stateless by default, promoting scalability and fault tolerance. Easier to run long-running operations: Serverless functions are generally designed for short-lived operations rather than long-running tasks. If you have a requirement for long-running operations, a serverless architecture may not be the ideal choice, as it has execution time limits and may not provide the necessary resources for extended execution.