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| Section | Weight | Objectives |
|---|---|---|
| Cloud Native Applications and Containerization | 33% | - OCI Registry
|
| Cloud Native Fundamentals | 11% | - Cloud Native Principles
|
| Testing and Securing Cloud Native Applications | 13% | - Cloud Native Security
|
| Monitoring & Troubleshooting Cloud Native Applications | 10% | - OCI Monitoring
|
| Leveraging Serverless Technologies for Cloud Native Development | 33% | - API Gateway
|
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質問 # 50
Which one of the following is NOT a valid backend-type supported by Oracle Cloud Infrastructure (OCI) API Gateway?
正解：D
解説：
Oracle Cloud Infrastructure (OCI) API Gateway supports various backend-types to handle incoming requests and route them to the appropriate backend service. However, " ORACLE_STREAMS_BACKEND " is not a valid backend-type in OCI API Gateway. " STOCK_RESPONSE_BACKEND " is a valid backend-type that allows you to configure static responses directly in the API Gateway without routing requests to any specific backend service. This can be useful for handling simple requests or returning predefined responses. " HTTP BACKEND " is another valid backend-type that enables routing requests to an HTTP backend service. " ORACLE_FUNCTIONS_BACKEND " is a valid backend-type that allows you to route requests to Oracle Functions, which are serverless functions that can be used to execute specific logic or operations. However, " ORACLE_STREAMS_BACKEND " is not a valid backend-type in OCI API Gateway. The API Gateway does not have native support for Oracle Streams as a backend service. Oracle Streams is a feature of Oracle Database that provides a publish-subscribe mechanism for data replication and distribution within an Oracle Database environment. It is not directly integrated as a backend service in OCI API Gateway.
質問 # 51
(CHK_4 > 2) Which TWO statements are NOT valid regarding the Oracle Cloud Infrastructure (OCI) Streaming service? (Choose two.)
正解：B、E
解説：
The two statements that are NOT valid regarding the Oracle Cloud Infrastructure (OCI) Streaming service are: A stream can be configured with either a public or a private endpoint with support for customer managed encryption keys. This statement is not valid because the OCI Streaming service currently supports only private endpoints. Customer managed encryption keys are not currently supported for OCI Streaming. OCI Streaming can support up to 2,000 requests per second to each partition. This statement is not valid because the throughput of a stream is not defined by the partition in terms of requests per second. The throughput of a stream is defined in terms of data input and output rates. Each partition provides 1 MB/sec data input and 2 MB/sec data output, but it does not correspond to a specific number of requests per second. The other statements are valid: OCI Streaming stores all data for 24 hours by default, but that can be extended up to 7 days. Although OCI Streaming automatically encrypts all data while in transit, it is the developer ' s responsibility to encrypt data at rest, if needed.
質問 # 52
Your organization has mandated that all deployed container images used for microservices must be signed by a specified master encryption key (MEK). You have appropriately signed the container images as part of your build process, but must now ensure that they are automatically verified when they are deployed to Oracle Cloud Infrastructure (OCI) Container Engine for Kubemetes (OKE) clusters. Which option should be used to mandate image verification when deploying to OKE clusters, assuming that MEK is already stored in an available OCI Vault? (Choose the best answer.)
正解：B
解説：
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.
質問 # 53
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?
正解：A
解説：
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.
質問 # 54
In the DevOps lifecycle, what is the difference between continuous delivery and continuous deployment?
(Choose two.)
正解：B、C
解説：
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.
質問 # 55
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