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| Section | Weight | Objectives |
|---|---|---|
| Leveraging Serverless Technologies for Cloud Native Development | 33% | - Event-driven architecture with OCI Events, Streaming, and Queue - Oracle Functions development and management - API-driven architectures using API Gateway |
| Cloud Native Fundamentals | 11% | - Cloud-native principles and microservices architecture - OCI developer services and tools overview - DevOps concepts and CI/CD workflows |
| Monitoring & Troubleshooting Cloud Native Applications | 10% | - Troubleshooting containerized and serverless workloads - Application performance monitoring and tracing - Centralized logging and metrics collection |
| 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% | - Application security best practices - Secrets management with OCI Vault - Testing strategies and secure deployment configurations |
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NEW QUESTION # 177
What is the difference between continuous delivery and continuous deployment in the DevOps methodology?
(Choose the best answer.)
Answer: 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 utilizes 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 # 178
Your DevOps team wants to configure a critical alert in OCI Monitoring for a payment gateway microservice application. The security architecture mandate requires that an alert must only fire if the average transaction latency exceeds 500 milliseconds over a sliding 15-minute interval. To avoid individual instance alerts, the query must aggregate all resource metrics within the target compartment together into a single unified stream before checking the threshold.
Which Monitoring Query Language (MQL) expression correctly satisfies all these requirements?
Answer: D
Explanation:
In OCI Monitoring Query Language, grouping() aggregates multiple matching metric streams into a common result rather than retaining separate streams for individual resources. Oracle ' s documentation demonstrates this syntax with expressions such as IopsRead[1m]{...}.grouping().max(). Here, [15m] establishes the metric aggregation interval, .mean() calculates the average transaction latency, and > 500 supplies the alarm threshold. groupBy(resourceId) would preserve separate result streams by resource ID, which directly contradicts the requirement to evaluate all resource metrics as one unified stream. filter(resourceId) is not valid grouping syntax, and absent() is designed to detect missing metric data rather than evaluate latency values. Therefore, TransactionLatency[15m].grouping().mean() > 500 correctly implements the stated aggregation and threshold logic.
NEW QUESTION # 179
How are cloud native application versions deployed to an OKE cluster when using a blue/green deployment strategy?
Answer: A
Explanation:
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
NEW QUESTION # 180
Which pattern is used to decompose a monolithic application by gradually replacing specific parts of its functionality with microservices?
Answer: C
Explanation:
The Strangler Pattern is specifically designed for incremental modernization of a monolithic application.
Oracle ' s cloud-native guidance describes it as gradually replacing portions of a monolith with microservices over time, minimizing disruption and avoiding the operational risk of a complete " big bang " rewrite. New functionality or migrated capabilities are implemented as independent services while remaining functionality continues to operate in the existing monolith. Eventually, the legacy application can be retired after its responsibilities have been progressively replaced. The Saga Pattern instead manages distributed transactions, the Sidecar Pattern places supporting functionality beside an application workload, and the Transactional Outbox Pattern provides reliable coordination between database changes and event publishing. Therefore, the pattern matching gradual monolith decomposition is the Strangler Pattern.
NEW QUESTION # 181
Which node configuration in Oracle Cloud Infrastructure Container Engine for Kubernetes provides a serverless experience with granular pod-level elasticity and billing?
Answer: D
Explanation:
OKE Virtual Nodes provide Oracle ' s serverless Kubernetes execution model. Oracle manages the underlying node infrastructure, eliminating the need for customers to provision, patch, upgrade, scale, or troubleshoot conventional worker-node infrastructure. Resource allocation and elasticity occur at the pod level, allowing workloads to scale without planning the capacity of a conventional node pool. Oracle also describes Virtual Nodes as providing pay-per-use pricing based on the compute resources consumed by each Kubernetes pod rather than charging for entire worker servers that might contain unused capacity. Managed, bare metal, and self-managed nodes remain infrastructure-oriented worker-node models and therefore do not provide the same serverless pod-level operating model. Consequently, Virtual Nodes are the configuration designed for granular elasticity and pod-oriented consumption.
NEW QUESTION # 182
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