Pass the Oracle Cloud Infrastructure 1z0-1084-26 Questions and answers with ExamsMirror

OCI Vault natively supports scheduled secret rotation and integration with OCI Functions. The RotationConfig model contains is_scheduled_rotation_enabled, rotation_interval, and target_system_details. Oracle specifies that enabling scheduled rotation requires a rotation interval represented using ISO 8601 duration syntax, so P90D expresses 90 days. For Function-based rotation, the target system is configured as a Function and the target details identify the Function OCID. Oracle further states that Secret Service invokes the configured rotation function as part of the rotation process. A SecretExpiryRule controls expiration rather than scheduled function-based rotation, while Monitoring alarms and Events rules would introduce separate orchestration rather than use Vault ' s native feature. Therefore, A correctly configures automatic 90-day Function-based rotation.

When configuring an OCI API Gateway deployment, you need to specify the backend type for each route in your API deployment specification3. The backend type determines how the API gateway handles requests to that route and forwards them to the appropriate backend service3. The following backend types are valid options for an OCI API Gateway deployment3:

HTTP_BACKEND: The API gateway forwards requests to an HTTP or HTTPS URL as the backend service.

ORACLE_FUNCTIONS_BACKEND: The API gateway invokes an Oracle Functions function as the backend service.

STOCK_RESPONSE_BACKEND: The API gateway returns a stock response without invoking any backend service. ORACLE STREAMS_BACKEND is not a valid backend type for an OCI API Gateway deployment. Oracle Streams is a fully managed, scalable, and durable messaging service that you can use to ingest and consume large amounts of data in real-time4. However, Oracle Streams is not supported as a backend service for an OCI API Gateway deployment.

OCI Application Performance Monitoring is specifically designed fordistributed tracingof application transactions across multiple components and services. Oracle states that APM collects and processes trace and span data, allowing administrators to analyze transaction performance, latency, volume, and errors across distributed applications. Trace Explorer can visualize traces containing multiple spans and help identify bottlenecks as requests move between services, databases, functions, and asynchronous components. OCI Monitoring concentrates primarily on metric streams, while Logging Analytics focuses on log aggregation and analysis. Service Mesh can provide service-to-service traffic telemetry and may participate in observability, but APM is the service designed to collect, correlate, and analyze distributed transaction traces. Therefore, D directly satisfies the end-to-end tracing requirement.

The OCI VCN-Native Pod Networking CNI plugin assigns pod IP addresses directly from the OCI VCN address space instead of allocating addresses from a separate overlay network. Oracle specifically states that these pod IP addresses are directly routable from other VCNs connected through VCN peering and from on-premises networks. This architecture makes pods first-class participants in OCI networking and enables native VCN functionality such as network security groups, security lists, and VCN flow logs to be applied to pod traffic. Flannel uses an overlay model and therefore does not provide the required directly routable VCN pod addresses. Calico is commonly associated with Kubernetes NetworkPolicy enforcement rather than being the required OCI native routing CNI. Therefore, D is correct.

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

Statement C is invalid because Oracle permits custom metrics to be postedas frequently as every second. The one-minute value applies to the minimum aggregation interval when metric data is subsequently queried, not to the ingestion frequency. Oracle also verifies the remaining statements. PostMetricData requires timestamps to be less than two hours in the past and less than ten minutes in the future. Custom namespaces must not use reserved prefixes such as oci_ or oracle_. The API additionally allows a maximum of 20 dimensions per metric group in a single posting operation. Therefore, interpreting one minute as the minimum interval between raw metric submissions incorrectly confuses ingestion frequency with Monitoring ' s aggregation/query interval. Option C is consequently the NOT valid statement.

Oracle ' s Logging Query Language specification defines logContent as aspecial column representing the text of the whole original message. This field is particularly valuable when the required information is not available through a known indexed JSON field or when administrators need full-text matching across the original log payload. For example, Oracle documents queries such as where logContent = ' *ERROR* ' to return log lines containing the specified text. messageBody, rawPayload, and eventSource are not documented LQL special columns representing the complete original message. Individual services can contain fields such as data.message, but those fields are part of the service-specific structured payload rather than the universal raw-message special column. Therefore, A is correct.

The docker inspect command retrieves detailed low-level metadata for Docker objects and returns the information in JSON format by default. Oracle ' s Docker documentation explicitly states that docker inspect can obtain full information about a container in JSON format and can expose specific configuration elements through formatting options. This includes network configuration, mapped ports, mounted storage, environment details, container state, and other runtime properties. docker stats provides live CPU, memory, network, and I/O utilization rather than static configuration metadata. docker history examines the image layer history and Dockerfile-derived changes. docker system provides system-wide administration commands and cleanup operations rather than detailed inspection of an individual container. Therefore, docker inspect is the appropriate troubleshooting command.

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 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.