Pass the Oracle Oracle Cloud Infrastructure 1z0-1084-25 Questions and answers with CertsForce

Explanation

The correct answer is: "Robust Deployment." When using test cases that simultaneously validate a deployment and perform a selected set of functional tasks, the testing measure that should be considered is "Robust Deployment." Robust Deployment refers to the ability of an application or system to be deployed reliably and consistently, without errors or failures. It involves ensuring that the deployment process is well-defined, automated, and able to handle different scenarios and configurations. When conducting testing that combines the validation of deployment and functional tasks, it is crucial to ensure that the deployment itself is robust. This means verifying that the application or system can be successfully deployed and configured without encountering deployment-related issues such as incorrect configurations, missing dependencies, or compatibility problems. By considering "Robust Deployment" as a testing measure, you can evaluate the reliability and effectiveness of the deployment process, ensuring that the application or system is deployed correctly and ready to perform the selected set of functional tasks.

Explanation

The correct answer is: "Resiliency is about recovering from failures without downtime or data loss." Service resiliency, in the context of cloud-native applications, is the ability of a service or system to recover from failures and continue functioning without downtime or data loss. It involves designing and implementing mechanisms to handle failures, such as network outages, hardware failures, or software errors, in a way that minimizes the impact on the overall system. The goal of resiliency is to ensure that the application or service can continue to operate and provide a certain level of functionality, even in the face of failures. This typically involves techniques such as redundancy, fault tolerance, and graceful degradation. By implementing resiliency measures, a cloud-native application can recover and adapt to failures, maintain availability, and preserve data integrity. The other statements are not accurate regarding service resiliency: Resiliency is not about not bringing a service to a functioning state after a failure. Instead, it is about recovering from failures and ensuring continued functionality. Resiliency is not about avoiding failures entirely. While it is desirable to prevent failures, resiliency focuses on the ability to handle and recover from failures when they do occur. Resiliency testing is not limited to a test environment. It is important to test and validate the resiliency measures in both test environments and production environments to ensure the application can effectively handle failures in real-world scenarios.

Explanation

The correct answer is "Service Mesh." A service mesh is a configurable, low-latency infrastructure layer that controls the interaction between a network of microservices. It provides functionalities such as service discovery, load balancing, traffic management, security, and observability for microservices-based applications. It is designed to improve communication and manage the complex interactions between services within a distributed system. Service mesh frameworks like Istio and Linkerd are commonly used to implement service mesh architecture.

Explanation

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.

OCI Queue is a fully managed serverless service that helps decouple systems and enable asynchronous operations2. Queue handles high-volume transactional data that requires independently processed messages without loss or duplication2. A valid use case for OCI Queue is building decoupled and scalable systems, such as event-driven architectures or microservices-based applications2. For example, you can use Queue to decouple your application and build an event-driven architecture. Decoupling ensures that individual application components can scale independently and that you can future-proof your design so that as new application components are built, they can publish or subscribe to the queue2.

Fn Project is the open source engine that is used by OCI to power Oracle Functions1. Fn Project is an open source, container native, serverless platform that can be run anywhere - any cloud or on-premises1. Fn Project is easy to use, extensible, and performant. You can download and install the open source distribution of Fn Project, develop and test a function locally, and then use the same tooling to deploy that function to Oracle Functions1. Verified References: Overview of Functions

Explanation

The three technologies best suited for implementing a microservices-based application are: Service Mesh: A service mesh is a dedicated infrastructure layer that provides features like service discovery, load balancing, encryption, authentication, and observability for microservices. It helps in managing the communication and interactions between microservices in a scalable and secure manner. Kubernetes: Kubernetes is an open-source container orchestration platform that enables the deployment, scaling, and management of containerized applications. It provides features like automated scaling, service discovery, load balancing, and self-healing capabilities, which are essential for managing microservices in a distributed environment. Docker: Docker is a popular containerization platform that allows packaging applications and their dependencies into lightweight containers. It provides a consistent and portable environment for running microservices, enabling easy deployment and scalability. Docker also facilitates isolation and resource efficiency, making it an ideal choice for deploying microservices. While Big Data, Anomaly Detection, and Terraform are valuable technologies, they are not specifically focused on enabling the implementation of microservices-based applications.

The logconfig.json file is a configuration file that specifies how the Unified Monitoring Agent collects and uploads custom logs to the OCI Logging service2. The logconfig.json file contains an array of objects, each representing a custom log configuration2. Each custom log configuration object has the following fields2:

logGroupObjectId: The OCID of the log group where the custom log is stored.

logObjectId: The OCID of the custom log.

paths: An array of paths to files or directories containing the custom logs.

src: A regular expression that matches the files containing the custom logs.

parser: A parser definition that specifies how to parse the custom logs. If the logconfig.json file has incorrect or missing OCID for the custom log in the logobjectId field, or incorrect or missing OCID for the custom log group in the logGroupObjectId field, then the Unified Monitoring Agent will not be able to upload the custom logs to the OCI Logging service2. Therefore, these are potential reasons for logs from the Meshifyd application not showing up in the OCI Logging service. Verified References: Unified Monitoring Agent Configuration File

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.

To push a new Docker container image to a repository in OCI Registry, you need to use an Auth Token to complete the authentication via the Docker CLI1. An Auth Token is a secure, auto-generated password that you can use to authenticate with OCI services such as OCI Registry1. You can generate an Auth Token in the Console by following these steps1:

In the top-right corner of the Console, open the Profile menu and then click User settings to view the details.

On the Auth Tokens page, click Generate Token.

Enter a friendly description for the auth token. Avoid entering confidential information.

Click Generate Token. The new auth token is displayed.

Copy the auth token immediately to a secure location from where you can retrieve it later, because you won’t see the auth token again in the Console.

Close the Generate Token dialog. After generating an Auth Token, you need to log in to OCI Registry by entering docker login .ocir.io in a terminal window on the client machine running Docker, where corresponds to the key for the OCI Registry region you’re using1. When prompted for a username, enter your username in the format /, where is the auto-generated Object Storage namespace string of your tenancy1. When prompted for a password, enter the Auth Token you copied earlier1.