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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Testing and Securing Cloud Native Applications | 13% | - Application security best practices - Testing strategies and secure deployment configurations - Secrets management with OCI Vault |
| Topic 2: Cloud Native Applications and Containerization | 33% | - Building containerized applications with Docker - Deployment and management with Oracle Kubernetes Engine (OKE) - Oracle Container Registry (OCIR) usage |
| Topic 3: Monitoring & Troubleshooting Cloud Native Applications | 10% | - Application performance monitoring and tracing - Troubleshooting containerized and serverless workloads - Centralized logging and metrics collection |
| Topic 4: Leveraging Serverless Technologies for Cloud Native Development | 33% | - Oracle Functions development and management - Event-driven architecture with OCI Events, Streaming, and Queue - API-driven architectures using API Gateway |
| Topic 5: Cloud Native Fundamentals | 11% | - Cloud-native principles and microservices architecture - OCI developer services and tools overview - DevOps concepts and CI/CD workflows |
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NEW QUESTION # 79
Your organization is deploying a high-performance distributed deep learning training workload that requires GPU-enabled bare metal Compute instances connected through an ultra-low-latency Oracle Cloud Infrastructure (OCI) RDMA cluster network. To maximize processing efficiency and comply with strict corporate OS requirements, your team must use a custom, pre-hardened Oracle Linux operating system image that is not supported by default node pools. The containerized training jobs must be orchestrated and scheduled using your existing Container Engine for Kubernetes (OKE) control plane.
Which worker node deployment model must you implement?
Answer: A
Explanation:
OKE Self-Managed Nodes, also called Bring Your Own Nodes, provide the necessary infrastructure flexibility for specialized Compute workloads. Oracle states that self-managed nodes are Compute instances that customers create themselves and then attach to an enhanced OKE cluster. Because the instances are provisioned through OCI Compute rather than a managed OKE node pool, organizations can select specialized shape and image combinations unavailable to managed or virtual nodes. Oracle specifically cites GPU shapes, HPC shapes, and high-bandwidth, ultra-low-latency RDMA cluster networks as examples suited to self-managed nodes. This model also enables custom operating-system configuration. Virtual Nodes abstract the infrastructure entirely, while Managed Node Pools constrain supported infrastructure combinations. Therefore, Self-Managed Nodes satisfy all of the stated GPU, RDMA, and custom-image requirements.
NEW QUESTION # 80
Your organization is developing serverless applications with Oracle Functions. Many functions will need to store state data in a database, which will require using appropriate credentials. However, your corporate security standards mandate encryption of secret information, such as database passwords. How would you address this security requirement?
Answer: B
Explanation:
The best way to store and use secret information, such as database passwords, in Oracle Functions is to use the OCI Vault service. The OCI Vault service provides encryption and decryption capabilities for sensitive data. You can use the OCI Vault service to encrypt the password and store it as an application-level configuration variable. Then, you can use the generated key to decrypt the password in your function code when you need to access the database. Verified References: Oracle Functions: Using Key Management To Encrypt And Decrypt Configuration Variables
NEW QUESTION # 81
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 # 82
In the DevOps lifecycle, what is the difference between continuous delivery and continuous deployment?
(Choose two.)
Answer: B,D
Explanation:
The two correct differences between continuous delivery and continuous deployment in the DevOps lifecycle are: Continuous delivery is a process that initiates deployment manually, while continuous deployment is based on automating the deployment process. In continuous delivery, the software is ready for deployment, but the decision to deploy is made manually by a human. On the other hand, continuous deployment automates the deployment process, and once the software passes all the necessary tests and quality checks, it is automatically deployed without human intervention. Continuous delivery involves automatic deployment to a development environment, while continuous deployment involves automatic deployment to a production environment. In continuous delivery, the software is automatically deployed to a development or staging environment for further testing and validation. However, the actual deployment to the production environment is performed manually. In continuous deployment, the software is automatically deployed to the production environment, eliminating the need for manual intervention in the deployment process. These differences highlight the level of automation and human involvement in the deployment process between continuous delivery and continuous deployment approaches in the DevOps lifecycle.
NEW QUESTION # 83
Which term describes an individual set of aggregated data for a metric with zero or more dimension values in the Oracle Cloud Infrastructure (OCI) Monitoring service?
Answer: D
Explanation:
Oracle Monitoring formally defines ametric streamas an individual set of aggregated data for a metric with zero or more dimension values. Different dimension combinations create distinct streams. For example, CPU utilization emitted for two separate Compute instance OCIDs can constitute two separate metric streams because the resourceId dimension differs. A metric namespace identifies the service or application emitting the metrics, such as oci_computeagent. A datapoint is an individual timestamped value within a metric stream. Oracle also uses the term metric group for limit calculations, defining it as the combination of a metric, namespace, and tenancy; that is different from the dimension-specific time-series data described in the question. Therefore, option A exactly matches Oracle ' s documented Monitoring terminology.
NEW QUESTION # 84
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