Oracle 1z0-1084-26 Buch - 1z0-1084-26 Vorbereitung

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Oracle 1z0-1084-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Monitoring and Troubleshooting Cloud Native Applications	10%	- OCI Monitoring 1. Use OCI Monitoring to view metrics - Monitoring, Logging, and Tracing 1. Perform monitoring, logging, and tracing tasks 2. Troubleshoot cloud native applications - OCI Logging 1. Search and analyze logs 2. Enable and manage logs
Topic 2: Cloud Native Fundamentals	11%	- Microservices Architecture 1. Microservices architecture 2. Microservices design methodology - Cloud Native Principles 1. Fundamentals of cloud native development 2. Key pillars of cloud native development
Topic 3: Cloud Native Applications and Containerization	33%	- Container Orchestration 1. Role of container orchestration 2. Deploy containerized applications on Oracle Kubernetes Engine - Oracle Cloud Infrastructure Registry 1. Push container images to OCIR 2. Pull container images from OCIR - Docker and Containerization 1. Docker architecture and components 2. Containerization concepts
Topic 4: Leveraging Serverless Technologies for Cloud Native Development	33%	- OCI Streaming 1. Integrate systems using OCI Streaming 2. Use streaming for serverless and managed application architectures - OCI Events 1. Build event-driven serverless applications - API Gateway 1. Route API traffic to backend services 2. Process traffic from API clients - OCI Queue 1. Implement asynchronous messaging for microservices - Oracle Functions 1. Develop serverless applications with Oracle Functions
Topic 5: Testing and Securing Cloud Native Applications	13%	- Cloud Native Testing 1. Testing strategies for cloud native applications 2. Cloud native testing methodologies - Cloud Native Security 1. Apply security measures for cloud native development 2. Manage sensitive configuration information 3. Use OCI Vault to securely manage encryption keys

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1z0-1084-26 Vorbereitung - 1z0-1084-26 Prüfungsvorbereitung

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Oracle Cloud Infrastructure Developer Professional 1z0-1084-26 Prüfungsfragen mit Lösungen (Q148-Q153):

148. Frage
Which statement is NOT valid regarding custom metrics and the OCI Monitoring PostMetricData API?

A. The minimum supported ingestion frequency for posting raw custom metric data points to the service is one minute.
B. The custom metric namespace name specified in the payload must avoid using reserved system prefixes such as oci_ or oracle_.
C. The timestamp of any posted custom metric data point must be less than two hours in the past and less than 10 minutes in the future.
D. The PostMetricData API enforces a strict per-call limit of a maximum of 20 dimensions per metric group for the request.

Antwort: A

Begründung:
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.

149. Frage
Your development team has deployed a fleet of web application servers across three fault domains in a specific compartment in OCI. To simplify operational dashboards, you must construct a single Monitoring Query Language (MQL) expression that calculates the average CPU utilization for each individual fault domain, rather than plotting each server individually. The expression must output exactly three lines, one for each fault domain, on the resulting Metrics Explorer chart.
Which MQL expression correctly fulfills these requirements?

A. CpuUtilization[1m].filter(faultDomain).mean()
B. CpuUtilization[1m].split(faultDomain).mean()
C. CpuUtilization[1m].grouping().mean()
D. CpuUtilization[1m].groupBy(faultDomain).mean()

Antwort: D

Begründung:
Oracle Monitoring documents groupBy(x) specifically for aggregating metric streams according to a shared dimension. Oracle ' s own fault-domain example uses the exact expression CpuUtilization[1m].groupBy (faultDomain).mean(). The resulting chart contains one aggregated metric stream for each fault domain, which means a compartment containing three fault domains produces three lines rather than one line per Compute instance. grouping() combines all selected metric streams into a single aggregate instead of retaining separate fault-domain groups. filter(faultDomain) is not the appropriate MQL grouping operation, and split() is not the documented operator for this requirement. Because the scenario requires mean CPU utilization separately aggregated for each value of the faultDomain dimension, option B exactly matches Oracle ' s documented MQL pattern.

150. Frage
Which of the following is defined as a configurable, low-latency infrastructure layer that controls the interaction between a network of microservices? (Choose the best answer.)

A. Service Mesh
B. Containers
C. DevOps
D. Kubernetes
E. CI/CD Pipelines

Antwort: A

Begründung:
The correct answer is " Service Mesh. " A service mesh is a configurable, low-latency infrastructure layer that controls the interaction between a network of microservices. It provides functionalities such as service discovery, load balancing, traffic management, security, and observability for microservices-based applications. It is designed to improve communication and manage the complex interactions between services within a distributed system. Service mesh frameworks like Istio and Linkerd are commonly used to implement service mesh architecture.

151. Frage
You are deploying a serverless data integration pipeline where an OCI Function needs to consume real-time events from an OCI Stream in the prod-compartment compartment. Your corporate security policy mandates strict least-privilege access, meaning the function must only be permitted to consume (pull) messages, with absolutely no capability to publish (push) messages to the stream. Which TWO configurations are required to establish this granular authorization using a Dynamic Group named analytics-function-dg?

A. Create a dynamic group rule to match the function: ALL {resource.type = ' fnfunc ' , resource.
compartment.id = ' ocid1.compartment.oc1..example ' }
B. Create an IAM policy statement: Allow dynamic-group analytics-function-dg to use stream-push in compartment prod-compartment
C. Create an IAM policy statement: Allow dynamic-group analytics-function-dg to use streams in compartment prod-compartment
D. Create a dynamic group rule to match the function: ALL {resource.type = ' compute ' , resource.
compartment.id = ' ocid1.compartment.oc1..example ' }
E. Create an IAM policy statement: Allow dynamic-group analytics-function-dg to use stream-pull in compartment prod-compartment

Antwort: A,E

Begründung:
Oracle Streaming exposes separate IAM resource types for producers and consumers. Granting use stream- pull provides STREAM_CONSUME, covering operations such as GetMessages, CreateCursor, group cursors, heartbeats, and commits without granting STREAM_PRODUCE. Granting use streams would be broader because that resource type includes both production and consumption capabilities. Oracle Functions can be placed in a dynamic group with a rule using resource.type = ' fnfunc ' together with the Function ' s compartment OCID. compute would not match an Oracle Function resource, and stream-push provides the opposite permission by allowing message publication. Therefore, A establishes consume-only Streaming permission while C correctly associates the Functions resources with the dynamic group.

152. Frage
You developed a microservices-based application that runs in an Oracle Cloud Infrastructure (OCI) Container Engine for Kubernetes (OKE) cluster. Your security team wants to use SSL termination for this application.
What should you do to create a secure SSL termination for this application using the fewest steps possible?

A. Add these annotations to the kubernetes service: annotations: service.beta.kubernetes.io/oci-load- balancer-ssl-ports: " 443 " service.beta.kubernetes.io/oci-load-balancer-ssl-secret-key: ssl secret-key
B. Generate a self-signed certificate using Let ' s Encrypt. Use that certificate on OCI Load Balancer.Create the Kubernetes service using this load balancer.
C. Create a self-signed certificate and its corresponding key. Create a Kubernetes secret using the certificate and the key. Then add these annotations to the Kubernetes service: annotations: service.beta.
kubernetes.io/oci-load-balancer-ssl-ports: " 443 " service.beta.kubernetes.io/oci-load-balancer-tls- secret: ssl certificate-secret
D. Create a self-signed certificate and its corresponding key. Create a Kubernetes secret using the certificate and the key. Then add these annotations to the Kubernetes service: annotations: service.beta.
kubernetes.io/oci-load-balancer-ssl-ports: " 443 " service.beta.kubernetes.io/oci-load-balancer-security- list management-mode: " Frontend "

Antwort: C

Begründung:
The correct answer is: " Create a self-signed certificate and its corresponding key. Create a Kubernetes secret using the certificate and the key. Then add these annotations to the Kubernetes service: annotations: service.
beta.kubernetes.io/oci-load-balancer-ssl-ports: ' 443 ' service.beta.kubernetes.io/oci-load-balancer-tls-secret:
ssl certificate-secret. " To create a secure SSL termination for your microservices-based application running in an OCI Container Engine for Kubernetes (OKE) cluster, you can follow these steps: Create a self-signed certificate and its corresponding key: Generate a self-signed SSL certificate and its private key using a tool like OpenSSL. Create a Kubernetes secret: Create a Kubernetes secret using the certificate and key obtained in the previous step. This secret will securely store the certificate and key within the Kubernetes cluster. Add annotations to the Kubernetes service: Modify the Kubernetes service that exposes your application and add the following annotations to enable SSL termination: annotations: service.beta.kubernetes.io/oci-load- balancer-ssl-ports: ' 443 ' (specify the SSL port as 443) annotations: service.beta.kubernetes.io/oci-load- balancer-tls-secret: ssl certificate-secret (specify the name of the Kubernetes secret containing the certificate and key) By following these steps, you can create a secure SSL termination for your application using a self- signed certificate and Kubernetes secret. The annotations added to the Kubernetes service ensure that the SSL port is configured correctly and the TLS secret is utilized for SSL termination when traffic reaches the load balancer. The other options provided are not the most suitable approaches for achieving secure SSL termination in an OCI Container Engine for Kubernetes (OKE) cluster: Adding annotations related to the OCI load balancer SSL secret key is not the correct approach for SSL termination in this scenario. Using Let ' s Encrypt to generate a self-signed certificate and configuring it on the OCI Load Balancer is not necessary when you can create and manage the SSL certificate within the Kubernetes cluster using a Kubernetes secret.

153. Frage
......

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