End-to-end testing is a technique that involves checking the entire user flow as well as the moving parts of a cloud native app, ensuring that there are no high-level discrepancies3. End-to-end testing simulates real user scenarios and validates the functionality, performance, reliability, and security of the app from start to finish3. End-to-end testing has several benefits, such as3:

Comprehensive testing: You can test your app as a whole and verify that all the components work together as expected.

User-centric testing: You can test your app from the user’s perspective and ensure that it meets the user’s needs and expectations.

Quality assurance: You can test your app in a realistic environment and identify any issues or defects before releasing it to the users.

Explanation

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.

A pod is the smallest and simplest unit in the Kubernetes object model that you create or deploy2. A pod represents a single instance of a running process in your cluster. Pods contain one or more containers, such as Docker containers. When you create a pod, you define how much CPU and memory (RAM) each container needs. A pod can also include storage volumes, IP addresses, options that govern how the container(s) should run, and more2. Pods are the basic building blocks of larger Kubernetes constructs such as deployments, replica sets, and services2.

Visibility timeout is the concept in OCI Queue that is responsible for hiding a message from other consumers for a predefined amount of time after it has been delivered to a consumer1. The visibility timeout can be set at the queue level when creating a queue, or it can be specified when consuming or updating messages1. If a consumer is having difficulty successfully processing a message, it can update the message to extend its invisibility1. If a message’s visibility timeout is not extended, and the consumer does not delete the message, it returns to the queue1. Verified References: Overview of Queue

Explanation

The feature that is typically NOT associated with Cloud Native is "Application Servers." Cloud Native architecture emphasizes lightweight, scalable, and containerized deployments, which often replace traditional monolithic application servers. Instead of relying on application servers, Cloud Native applications are typically deployed as containerized microservices that can be orchestrated and managed using container orchestration platforms like Kubernetes. This approach enables greater flexibility, scalability, and agility in deploying and managing applications. While application servers have been widely used in traditional application architectures, they are not a characteristic feature of Cloud Native architectures. Cloud Native architectures focus on containerization, declarative APIs, immutable infrastructure, and service meshes to enable efficient and scalable deployment and management of applications.

Explanation

The correct answer is: Microservices are used for long running operations while serverless functions are used for short running operations. One of the key differences between microservices and serverless functions is the duration of their execution. Microservices are typically designed to handle long-running operations and may continuously run and process requests as part of a larger system. They are often deployed and managed as long-lived services. On the other hand, serverless functions are designed to handle short-lived operations or tasks that execute in response to specific events or triggers. They are event-driven and execute only when invoked, providing a lightweight and ephemeral computing model. Serverless functions are often used for executing small, isolated pieces of code without the need for managing infrastructure or scaling concerns. While both microservices and serverless functions can be stateless or stateful depending on the specific implementation, the key distinction lies in the typical duration and execution pattern of these components within an application architecture.

Explanation

The correct answer is: "Tightly coupled." Tightly coupling is not a criterion that is usually met by a microservice. In fact, microservices are designed to be loosely coupled. Loosely coupling refers to reducing dependencies and minimizing the direct interactions between different components or services. Microservices promote independence and autonomy, allowing each service to operate independently without being tightly bound to other services. The other options listed are criteria that are typically met by microservices: Organized around business capabilities: Microservices architecture suggests designing services around specific business capabilities or functionalities. This allows for focused and specialized services that align with the organization's business needs. Independently deployable: Microservices are designed to be independently deployable units. Each microservice can be developed, tested, and deployed separately, without impacting other services. This enables agility and scalability in the deployment process. Highly maintainable: Microservices are often designed to be highly maintainable. They are smaller in scope and focused on specific tasks, making it easier to manage and maintain individual services. Additionally, microservices can be updated, patched, or replaced without affecting the entire system, facilitating easier maintenance and evolution of the application. Therefore, the criterion that is NOT typically met by a microservice is being tightly coupled.

Explanation

The correct value for the "type" field in the API deployment specification is "STOCK_RESPONSE_BACKEND". By setting the "type" to "STOCK_RESPONSE_BACKEND", you are indicating that the backend for the specified route should return a pre-defined response. This type of backend is commonly used when you want a specific response to be returned consistently, regardless of the actual backend service implementation. In this case, the API deployment specification is configured to have a single route with the path "/hello" and the method "GET". The backend section specifies the type as "STOCK_RESPONSE_BACKEND". Additionally, it defines the response status code as 200, sets the "Content-Type" header to "application/json", and provides the JSON content in the "body" field. Using this configuration, any request to the "/hello" path with the "GET" method will always receive a consistent JSON response with the content "{"myjson": "consistent response"}".

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 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.

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.

Explanation

The statement that accurately describes the Oracle Cloud Infrastructure (OCI) Load Balancer integration with OCI Container Engine for Kubernetes (OKE) is: "OKE service provisions an OCI Load Balancer instance for each Kubernetes service with LoadBalancer type in the YAML configuration." When you define a Kubernetes service in your YAML configuration with the LoadBalancer type, the OKE service automatically provisions an OCI Load Balancer instance specifically for that service. This Load Balancer instance is dedicated to the Kubernetes service and provides traffic balancing functionality. Each Kubernetes service that requires load balancing will have its own OCI Load Balancer instance provisioned by OKE.

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

Which TWO statements are correct regarding Docker images and containers? (Choose two.) Writing and building a new Dockerfile is the only way you can create new Docker images. A container can exist without an image but an image cannot exist without a container. Only one container can be spawned from a given image at a time. If multiple containers are spawned from the same image, then they all use the same copy of that image in memory. (Correct) An image is a collection of immutable layers whereas a container is a running instance of an image. (Correct) ChatGPT The correct statements regarding Docker images and containers are: If multiple containers are spawned from the same image, then they all use the same copy of that image in memory. This statement is correct. When multiple containers are created from the same Docker image, they share the underlying layers of the image in memory. Each container has its own isolated filesystem and runtime environment, but they all use the same base image, which helps in efficient resource utilization. An image is a collection of immutable layers whereas a container is a running instance of an image. This statement is also correct. Docker images are composed of multiple layers, with each layer representing a specific change or addition to the previous layer. These layers are read-only and can be shared among multiple containers. On the other hand, a container is a lightweight and isolated runtime instance created from a specific image. It is a running process that has its own filesystem, network, and runtime configuration. The statements "Writing and building a new Dockerfile is the only way you can create new Docker images" and "A container can exist without an image but an image cannot exist without a container" are incorrect. Docker images can be created in various ways, including using Dockerfiles, importing from existing images, or pulling from registries. Additionally, a container requires an image to run, so an image must exist before a container can be created from it.

Explanation

The correct answers are: Amount of RAM used by a function: Oracle Functions collects and reports the amount of memory (RAM) used by a function during its execution. This metric helps in monitoring and optimizing the resource consumption of functions. Length of time a function runs: Oracle Functions captures and provides the duration of function executions. This metric allows you to track the performance and responsiveness of your functions and identify any potential bottlenecks or delays. These metrics provide valuable insights into the resource utilization and performance of your functions, enabling you to monitor and optimize their behavior in the Oracle Cloud Infrastructure (OCI) environment.

Explanation

The strategy that involves exposing new functionality or features to only a small set of users is called Canary Deployment. Canary deployment is a technique used in software development and deployment where a new version of an application or feature is released to a small subset of users or a specific group of servers. This allows for testing and gathering feedback on the new functionality in a controlled and limited environment before making it available to a wider audience. In a canary deployment, a small portion of the traffic is routed to the new version while the majority of the traffic still goes to the stable version. This allows for monitoring and evaluation of the new functionality in real-world conditions while minimizing the impact of any potential issues or bugs. If the new version performs well and meets the desired criteria, it can then be gradually rolled out to a larger user base or all servers. By exposing the new functionality or features to a small set of users initially, canary deployment helps in identifying any unforeseen issues, gathering feedback, and ensuring the stability and reliability of the application before a full deployment.

Explanation

The correct answer is: "A finely tuned piece of software that performs a single or small collection of tasks." Microservices are a software architectural approach where a system is decomposed into small, independent services that are responsible for performing a specific set of tasks. Each microservice is designed to be focused, finely tuned, and highly cohesive, handling a single or a small collection of related tasks. This granularity allows for better scalability, maintainability, and flexibility in building complex applications. The other options provided do not accurately define microservices: An open-source system for automating deployment, scaling, and management of containerized applications refers to a container orchestration tool like Kubernetes, which can be used to manage microservices but is not a definition of microservices itself. A statically typed and compiled language describes a type of programming language characteristic and is not specific to the concept of microservices. An organized collection of structured information or data, typically stored electronically in a computer system is a definition of a database or data storage system and is unrelated to microservices.

Explanation

The correct answer is: "read only." The layers of a Docker image are read-only. Once a layer is created, it cannot be modified. Each layer represents a Dockerfile instruction, and it is stacked on top of the previous layer, forming a stack of immutable layers. These layers are designed to be read-only to ensure consistency and integrity of the image. When a Docker image is built, each instruction in the Dockerfile creates a new layer. Each layer represents the changes made by that instruction relative to the previous layer. The layers are stacked on top of each other to form the complete image. This layer-based approach allows for efficient storage and distribution of Docker images. Because the layers are read-only, any changes or modifications to the image result in the creation of new layers rather than modifying the existing ones. This immutability ensures that each layer remains intact and preserves the integrity of the image. It also enables Docker's caching mechanism, where previously built layers can be reused if the corresponding instructions haven't changed, speeding up the image build process. The other options mentioned, such as "write only," "write once," "movable," and "read mostly," do not accurately describe the permission associated with Docker image layers. Docker image layers are specifically designed to be read-only.

OCI Streaming is a fully managed, scalable, and durable messaging solution for ingesting continuous, high-volume streams of data that you can consume and process in real-time1. Streaming is suitable for any use case in which data is produced and processed continually and sequentially in a publish-subscribe messaging model1. Streaming can handle millions of messages per second with low latency2. Therefore, OCI Streaming is the most appropriate choice for handling real-time data feeds from a fleet of vehicles. Verified References: Overview of Streaming, Container Engine for Kubernetes

Explanation

The user is responsible for patching, upgrading, and maintaining the worker nodes in Oracle Cloud Infrastructure (OCI) Container Engine for Kubernetes (OKE). In OKE, the user has control over the worker nodes, which are the compute instances that run the Kubernetes worker components. As the user, you are responsible for managing and maintaining these worker nodes, including tasks such as patching the underlying operating system, upgrading Kubernetes versions, and performing any necessary maintenance activities. While Oracle provides the underlying infrastructure and support services, including managing the control plane and ensuring the availability of the OKE service, the responsibility for managing the worker nodes lies with the user. This allows you to have control and flexibility in managing your Kubernetes environment according to your specific needs and requirements.

Blue/Green deployment strategy allows releasing a new version of an application using two identical environments where one of them is active at a given time. The current version of the application is provisioned on the active environment, whereas the new version gets deployed to the standby environment1. The traffic is shifted from the active to the standby environment by updating the ingress resource2. Therefore, both old and new application versions are deployed to production at the same time, but only one of them receives the traffic. Verified References: Announcing new deployment strategies for OCI DevOps Service, Blue-Green OKE Deployment

Explanation

The correct API method to read and process a stream in the Oracle Cloud Infrastructure (OCI) Streaming service is "GetMessages". When consuming messages from a stream in OCI Streaming, you use the "GetMessages" API method. This method allows you to retrieve a batch of messages from the stream for processing. You can specify parameters such as the number of messages to retrieve, the maximum size of the messages, and the timeout for the request. By using the "GetMessages" API method, you can retrieve messages from the stream and then process them in your microservices application. This allows you to consume and handle the data in real-time as it becomes available in the stream. The "GetMessages" method provides flexibility in how you consume and process the messages, enabling you to implement custom logic and workflows based on your specific application requirements.

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.

Explanation

The correct answer is: "Once rotated, older key versions can be used for encryption until they are deleted." The statement that is NOT valid about rotating keys in the OCI Vault service is: "Once rotated, older key versions can be used for encryption until they are deleted." In the OCI Vault service, when you rotate a master encryption key (MEK), a new key version is automatically generated. However, once a key is rotated and a new version is created, the older key versions are no longer usable for encryption. The purpose of key rotation is to ensure that the encryption keys are regularly updated and that older keys are no longer used to protect sensitive data. This enhances security by minimizing the impact of potential key compromises. The other statements mentioned are valid: Both software and hardware security module (HSM)-protected MEKs can be rotated. This provides flexibility in choosing the type of MEK and ensures that key rotation can be performed regardless of the encryption method used. Each key version is tracked internally with separate unique OCIDs (Oracle Cloud Identifiers). This allows for easy management and tracking of different key versions within the OCI Vault service. In summary, the statement that is NOT valid is the one suggesting that older key versions can still be used for encryption until they are deleted. Key rotation is designed to ensure the use of the latest key version and to retire older key versions to enhance security.

Explanation

The option that allows changes to a Docker image to be stored in a version control system is: docker commit The docker commit command is used to create a new image from a container's changes. It takes a running container as input, captures the changes made to it, and creates a new image with those changes. This new image can then be tagged and pushed to a registry, or saved locally. By using docker commit, you can effectively capture the changes made to a container as a new image and store it in a version control system along with the Dockerfile and other project files. This allows for reproducibility and traceability of changes to the Docker image over time.

Explanation

The option that will get you more information on the error when invoking Oracle Functions from your Cloud Shell session is: "DEBUG=1 fn invoke myapp myfunction". Setting the environment variable DEBUG=1 before invoking the function using the fn command allows you to enable debug mode, which provides more detailed information about the execution of the function. This can be useful for troubleshooting and understanding the root cause of the error. By using the command "DEBUG=1 fn invoke myapp myfunction", the function invocation will be executed with debug mode enabled, and additional debug information will be displayed in the console output. This information can include stack traces, detailed error messages, and other relevant details that can help identify and resolve the issue. Using the verbose option (--verbose) or debug option (--debug) with the fn command may also provide additional information, but the specific behavior may depend on the version and configuration of the fn CLI tool. While contacting Oracle support with the error message is always an option, enabling debug mode using the DEBUG=1 environment variable provides immediate access to more detailed information and can help in diagnosing and resolving the error more efficiently.

Explanation

The maximum execution time of Oracle Functions is 300 seconds, which is equivalent to 5 minutes. This means that a function running within Oracle Functions cannot exceed a runtime of 5 minutes. If a function requires longer execution times, alternative approaches such as invoking external services asynchronously or using long-running processes should be considered. It is important to design functions with this execution time limitation in mind to ensure optimal performance and efficiency within the Oracle Functions platform.