Oracle Updated 1z0-1084-26 Exam Questions and Answers by adeline

Explanation

The valid troubleshooting strategy in this scenario is to enable function logging in the OCI console, add some print statements in your function code, and then view the logs to troubleshoot. Enabling function logging allows you to capture and store logs generated by your function during its execution. By adding print statements or log statements in your function code, you can output relevant information and debug messages to the logs. This helps you understand the execution flow, identify any errors or issues, and gather more information about the function ' s behavior. To troubleshoot the issue of the Python function not stopping the non-compliant OCI compute instances, you can follow these steps: Enable function logging in the OCI console: Enable logging for your function to ensure that logs are captured during its execution. Modify your function code: Add relevant print statements or log statements at key points in your code to output debug information or verify the execution flow. For example, you can print the instance details that are being evaluated for compliance. Invoke the function: Trigger the function execution either through an event or manually. View the logs: Access the function logs in the OCI console or retrieve them programmatically. Look for the expected print statements or log entries that indicate the status of each instance and the decisions made by the function. By reviewing the logs, you can analyze the output and identify any issues or discrepancies. It can help you determine if the function is correctly evaluating the compliance criteria, retrieving the instance details, or making the necessary API calls to stop the instances. You may need to adjust your code logic or investigate further based on the information provided in the logs. Enabling function remote debugging is not a suitable strategy in this case because it is primarily used for inspecting and debugging the function code during development, rather than troubleshooting issues in a deployed function. Enabling function tracing can provide insights into the execution flow and performance of the function but may not directly address the issue of the instances not being stopped. Ensuring that the application is deployed within the same OCI compartment as the instance is not directly related to troubleshooting the issue with the non-compliant instances. It is a consideration for access and permissions but does not provide specific insights into the problem at hand. Remember to refer to the Oracle Functions documentation and consult the official resources for detailed instructions and best practices on troubleshooting and monitoring Oracle Functions.

Explanation

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.

In the shared responsibility model, Oracle is responsible for securing the underlying cloudinfrastructure and platform services, while customers are responsible for securing theirdata and applications within the cloud4. For provisioned OKE clusters, Oracle manages the control plane (master nodes) of the Kubernetes cluster, while customers manage the data plane (worker nodes) of the cluster5. Therefore, it is the responsibility of the customer to perform patching, upgrading, and maintaining of the worker nodes in provisioned OKE clusters5. Customers can use tools such as Terraform or kubectl to automate these tasks5.

Explanation

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.