1z0-1084-26 Oracle Cloud Infrastructure Developer Professional 学習ガイド、1z0-1084-26勉強資料

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Oracle 1z0-1084-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|
| Monitoring and Troubleshooting Cloud Native Applications | 10% | - OCI Monitoring
- 1. Use OCI Monitoring to view metrics
- Monitoring, Logging, and Tracing
- 1. Troubleshoot cloud native applications
- 2. Perform monitoring, logging, and tracing tasks
- OCI Logging
- 1. Search and analyze logs
- 2. Enable and manage logs
|
| Cloud Native Fundamentals | 11% | - Cloud Native Principles
- 1. Fundamentals of cloud native development
- 2. Key pillars of cloud native development
- Microservices Architecture
- 1. Microservices architecture
- 2. Microservices design methodology
|
| Leveraging Serverless Technologies for Cloud Native Development | 33% | - Oracle Functions
- 1. Develop serverless applications with Oracle Functions
- OCI Queue
- 1. Implement asynchronous messaging for microservices
- OCI Events
- 1. Build event-driven serverless applications
- API Gateway
- 1. Route API traffic to backend services
- 2. Process traffic from API clients
- OCI Streaming
- 1. Use streaming for serverless and managed application architectures
- 2. Integrate systems using OCI Streaming
|
| Testing and Securing Cloud Native Applications | 13% | - Cloud Native Testing
- 1. Cloud native testing methodologies
- 2. Testing strategies for cloud native applications
- Cloud Native Security
- 1. Manage sensitive configuration information
- 2. Use OCI Vault to securely manage encryption keys
- 3. Apply security measures for cloud native development
|
| 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
|
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Oracle Cloud Infrastructure Developer Professional 認定 1z0-1084-26 試験問題 (Q34-Q39):
質問 # 34
Which statement is NOT valid regarding testing methodologies for cloud native applications?
- A. Performance testing evaluates system responsiveness under concurrent workloads to identify potential resource bottlenecks.
- B. Unit testing focuses on validating individual blocks of code in complete isolation from database and external API dependencies.
- C. Integration testing is conducted exclusively on isolated local workstations and does not validate network-based communications.
- D. API mocking allows developers to validate a service ' s logic by simulating the behavior of dependent microservices that are offline.
正解:C
解説:
Statement D is NOT valid. Oracle ' s cloud application architecture guidance explicitly describes integration testing as validating interactions and data flow between components, modules, and services. In a cloud-native architecture, those interactions commonly occur through network-based APIs, service endpoints, databases, and messaging systems. Oracle recommends test environments that resemble production rather than restricting integration testing to isolated developer workstations. Unit testing does focus on individual units of code and typically isolates dependencies. API mocking can emulate unavailable dependent services, while performance testing evaluates responsiveness, scalability, and stability under varying workloads such as load and stress conditions. Therefore, the claim that integration testing is exclusively local and does not validate network communication contradicts the actual purpose of integration testing.
質問 # 35
Your team has deployed a high-throughput order processing microservice that consumes messages from an OCI Stream with 8 partitions. To handle peak operational hours, you have scaled out your deployment to run
12 identical microservice consumer instances under a single named consumer group. During execution, you notice that 4 of the 12 consumer instances remain completely idle and are not assigned any stream data.
Which OCI Streaming architectural rule explains this behavior?
- A. The instances are idling because a single consumer group cannot support more than 8 active consumer instances regardless of partition count.
- B. The instances are idling because each partition in a stream can be consumed by at most one consumer instance within a single consumer group.
- C. The instances are idling because OCI Streaming requires a dedicated stream pool for every four active consumer instances in a consumer group.
- D. The instances are idling because you must configure OCI Service Connector Hub to explicitly map each of the 12 consumer instances to its own partition.
正解:B
解説:
OCI Streaming consumer groups assign each stream partition exclusively to one consumer-group instance at a time. Oracle states that every partition is owned by one instance following a successful rebalance and that instances exceeding the number of available partitions remain idle. With eight partitions, no more than eight of the twelve consumer instances can actively consume simultaneously, leaving four without assignments.
This is not a fixed eight-consumer limitation: increasing the stream to twelve or more partitions could allow all twelve instances to become active. Consumer groups automatically coordinate partition assignment, offsets, commits, heartbeats, and rebalancing, so Service Connector Hub is unnecessary. Stream pools likewise do not determine the consumer-to-partition scaling relationship. Therefore, B explains the observed behavior precisely.
質問 # 36
As a Cloud Native developer, you have written a web service for your company. However, your security team has suggested that your web service should address Distributed Denial-of-Service (DDoS) attack. You are time-constrained and you need to ensure that this is implemented as soon as possible. What should you do in this scenario? (Choose the best answer.)
- A. Use a third party service integration to Implement DDoS attack mitigation.
- B. Re-write your web service and implement rate limiting.
- C. Use the OCI Virtual Cloud Network (VCN) segregation to control DDoS.
- D. Use the OCI API Gateway service and configure rate limiting.
正解:D
解説:
The correct answer in this scenario is to use the OCI API Gateway service and configure rate limiting. Using the OCI API Gateway service and configuring rate limiting is an effective approach to address Distributed Denial-of-Service (DDoS) attacks. By implementing rate limiting, you can control the number of requests that can be made to your web service within a specific time frame. This helps to prevent overload and ensures that your service can handle legitimate traffic while mitigating the impact of DDoS attacks. By leveraging the OCI API Gateway service, you can easily configure rate limiting rules to restrict the number of requests per second or per minute. This allows you to set appropriate thresholds and safeguard your web service from being overwhelmed by excessive requests. The API Gateway acts as a protective layer, filtering out malicious traffic and ensuring the smooth operation of your service. While options like OCI Virtual Cloud Network (VCN) segregation and third-party service integrations may contribute to overall security, they do not specifically address DDoS attacks as efficiently as rate limiting. VCN segregation focuses more on network segmentation and isolation, while third-party service integration may introduce additional dependencies and complexities.
Re-writing your web service and implementing rate limiting is a viable option, but it may not be feasible considering the time constraints mentioned. Leveraging the OCI API Gateway service provides a quicker and easier solution to implement DDoS attack mitigation through rate limiting.
質問 # 37
As a developer, you have been tasked with implementing a microservices-based application. Which THREE technologies are best suited to accomplish the task? (Choose three.)
- A. Big Data
- B. Terraform
- C. Service Mesh
- D. Anomaly Detection
- E. Kubemetes
- F. Docker
正解:C、E、F
解説:
The three technologies best suited for implementing a microservices-based application are: Service Mesh: A service mesh is a dedicated infrastructure layer that provides features like service discovery, load balancing, encryption, authentication, and observability for microservices. It helps in managing the communication and interactions between microservices in a scalable and secure manner. Kubernetes: Kubernetes is an open- source container orchestration platform that enables the deployment, scaling, and management of containerized applications. It provides features like automated scaling, service discovery, load balancing, and self-healing capabilities, which are essential for managing microservices in a distributed environment.
Docker: Docker is a popular containerization platform that allows packaging applications and their dependencies into lightweight containers. It provides a consistent and portable environment for running microservices, enabling easy deployment and scalability. Docker also facilitates isolation and resource efficiency, making it an ideal choice for deploying microservices. While Big Data, Anomaly Detection, and Terraform are valuable technologies, they are not specifically focused on enabling the implementation of microservices-based applications.
質問 # 38
Which testing strategy achieves high velocity of deployments and releases of cloud native applications?
(Choose the best answer.)
- A. A/B testing
- B. Integration testing
- C. Penetration testing
- D. Automated testing
正解:D
解説:
The testing strategy that achieves high velocity of deployments and releases of cloud native applications is " Automated testing. " Automated testing involves the use of automated tools and frameworks to execute tests, validate functionality, and detect issues or bugs in an application. By automating the testing process, developers and DevOps teams can rapidly test and validate code changes, ensuring that new features and updates are functioning correctly before being deployed to production. This approach helps increase the speed and efficiency of the testing process, allowing for faster and more frequent deployments of cloud native applications.
質問 # 39
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