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

An Ingress Controller is a Kubernetes resource that provides advanced routing and load balancing for your applications running on a Kubernetes cluster1. An Ingress Controller allows you to define rules that specify how to route traffic to different services in your cluster based on the host name or path of the incoming request1. By deploying an Ingress Controller and using it to expose multiple application endpoints, you can achieve the following benefits1:

Cost-effectiveness: You only need to create one load balancer instance per cluster, instead of one per service, which reduces the cost of exposing your applications.

Simplicity: You only need to manage one set of routing rules for all your services, instead of configuring each service separately, which simplifies the application deployment and maintenance.

Flexibility: You can use different types of Ingress Controllers, such as NGINX or Traefik, that offer various features and customization options for your routing needs.

Explanation

In the given scenario, the developer should use the approach of creating an authorizer function using token-based authorization. Token-based authorization is a commonly used approach for authenticating API requests. It involves generating and issuing tokens to API callers, which they can then include in the requests they make to the API. The tokens serve as proof of authentication and are validated by the server to ensure the caller ' s identity and access rights. By creating an authorizer function using token-based authorization, the developer can implement a custom scheme that accepts string-based parameters from the API caller. This allows the developer to define their own authentication logic and validate the provided tokens according to their requirements. The authorizer function can be configured in the OCI API Gateway to be invoked before forwarding the request to the web application. It will perform the necessary token validation and authentication checks, allowing only authorized requests to access the protected resources of the web application.

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.

Explanation

The correct answer is: Test using an API mock of service B. To test service A without deploying all of its dependencies, including service B, you can use an API mock of service B. An API mock is a simulated version of the API that mimics the behavior of the actual service. By using an API mock, you can isolate the testing of service A and simulate the responses and behavior of service B ' s APIs. With an API mock, you can define the expected responses and behavior of service B ' s APIs, allowing you to test the integration between service A and the mocked version of service B. This approach enables you to verify the functionality of service A without relying on the availability or changes in the actual service B. By decoupling the dependencies and using an API mock, you can perform independent testing of service A, ensuring its functionality in isolation.

The supplied Word answer key requires correction here. Current Oracle Queue documentation explicitly states that adead letter queue is automatically created when a queue is created. Therefore, option C is correct and option B is not. Oracle also provides the Maximum delivery attempts setting, which controls how many unsuccessful deliveries can occur before a problematic message is transferred to the DLQ. Setting that value to 5 implements the requested retry threshold. No separately provisioned target queue is required, consumer groups are optional and unrelated to enabling DLQ functionality, and administrators do not create an IAM policy authorizing a source queue resource to write to its automatically associated DLQ. Consequently, the verified current answer isA, C, not the Word file ' s A, B.

Option C reverses a fundamental cloud-native characteristic. Oracle describes cloud-native applications as being designed for automated scale-up and scale-down in response to changing workload demand. Technologies including containers, Kubernetes, serverless architectures, microservices, and automated DevOps processes are specifically intended to reduce manual infrastructure management. Oracle also identifies scalability, resilience, reduced operational overhead, and cost optimization through cloud consumption models as major cloud-native advantages. Applications migrated to cloud infrastructure without substantial architectural modernization can retain monolithic dependencies, fixed capacity assumptions, and manual provisioning practices. Therefore, claiming that cloud-native applications require manual host provisioning while cloud-based applications automatically scale by default is technically inverted. C is consequently the NOT valid statement among the available choices.

OCI Monitoring provides anabsencetrigger for alarms that must fire when a metric stops emitting data. Oracle ' s current documentation states that an absence alarm uses the MQL absent() statistic. It returns 1 when the metric is absent for the complete evaluation interval and 0 when data is present. An expression can therefore take the form CpuUtilization[1m].absent() or use groupBy() before absent() when separate dimensional streams must be evaluated. A threshold trigger, by contrast, evaluates numeric metric values against limits such as greater than 90 percent. missing and non-zero are not OCI Monitoring trigger-rule names for the required behavior. Thus, the correct trigger rule for detecting a lack of incoming metric data is absence.

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.

Idle-timeout is the parameter that is used to configure the time period during which an idle function will remain in memory before Oracle Functions removes its container image from memory2. The idle-timeout parameter is specified in seconds and can be set when creating or updating a function2. The default value for idle-timeout is 30 seconds and the maximum value is 900 seconds (15 minutes)2. If a function has the --provisioned-concurrency option enabled, the idle-timeout parameter is ignored and the function instances are always kept in memory3.Verified References: Creating Functions, Provisioned Concurrency

The Saga Design Pattern is specifically intended for distributed transactions across microservices without the extended locking associated with Two-Phase Commit. Oracle explains that a Saga decomposes a business transaction into a sequence of independent local transactions. After completing its local operation, each service publishes an event or message that triggers the next participant. If a subsequent step fails, corresponding compensating transactions reverse previously completed operations and restore consistent business state. This approach shortens lock duration and supports eventual consistency across separate databases. Transactional Outbox solves reliable database-update-and-message-publishing coordination but does not by itself manage the entire distributed transaction sequence. Therefore, the required pattern is the Saga Design Pattern.