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| Section | Weight | Objectives |
|---|---|---|
| Cloud Native Applications and Containerization | 33% | - Oracle Container Registry (OCIR) usage - Building containerized applications with Docker - Deployment and management with Oracle Kubernetes Engine (OKE) |
| Testing and Securing Cloud Native Applications | 13% | - Secrets management with OCI Vault - Application security best practices - Testing strategies and secure deployment configurations |
| Monitoring & Troubleshooting Cloud Native Applications | 10% | - Troubleshooting containerized and serverless workloads - Application performance monitoring and tracing - Centralized logging and metrics collection |
| Leveraging Serverless Technologies for Cloud Native Development | 33% | - API-driven architectures using API Gateway - Event-driven architecture with OCI Events, Streaming, and Queue - Oracle Functions development and management |
| Cloud Native Fundamentals | 11% | - Cloud-native principles and microservices architecture - DevOps concepts and CI/CD workflows - OCI developer services and tools overview |
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NEW QUESTION # 122
Your team has created a serverless application deployed in Oracle Functions. It uses a Python function leveraging the Oracle Cloud Infrastructure (OCI) Python SDK to stop any OCI compute instance that does not comply with your corporate security standards. Although there are three non-compliant OCI compute instances, when you invoke this function, none of the instances were stopped. With respect to this issue, which of the following is a valid troubleshooting strategy?
Answer: A
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.
NEW QUESTION # 123
When utilizing the OCI Monitoring PostMetricData API to publish custom metrics, what is the default batch atomicity behavior applied to input validation for the metric objects?
Answer: A
Explanation:
Oracle explicitly defines the default batchAtomicity setting for PostMetricData asNON_ATOMIC. With NON_ATOMIC validation, at least one metric object in the request must pass validation for valid metric objects to be accepted. Individual objects that fail validation are rejected and identified in the returned summary with their associated errors. By comparison, ATOMIC requires every metric object in the batch to pass validation; if one object fails, the whole request fails validation. PARTIAL_COMMIT and TRANSACTIONAL are not supported values for the batchAtomicity parameter. The supported enumeration consists of ATOMIC and NON_ATOMIC, with NON_ATOMIC being the documented default. Therefore, B accurately reflects OCI Monitoring ' s default custom-metric ingestion behavior.
NEW QUESTION # 124
Which statement is NOT valid regarding the structure and behavior of Docker images and containers?
Answer: C
Explanation:
Docker images and containers have separate lifecycles. Oracle describes a Docker image as a read-only template used to create containers, while image implementations consist of immutable read-only layers. A running container adds its own writable runtime state without changing those underlying image layers.
Removing a container with docker rm removes that container instance; deleting the corresponding image requires a separate image-removal operation such as docker rmi. Oracle ' s Cloud Shell documentation likewise explicitly uses docker rmi when deleting local images. Therefore, deleting a container doesnotautomatically or permanently delete its source image. Statements A, B, and D describe the layered and copy-on-write behavior associated with Docker containers, making C the invalid statement.
NEW QUESTION # 125
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?
Answer: D
Explanation:
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.
NEW QUESTION # 126
In the shared responsibility model, who should perform patching, upgrading, and maintaining of the worker nodes in provisioned Oracle Container Engine for Kubernetes (OKE) clusters?
Answer: C
Explanation:
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.
NEW QUESTION # 127
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