Latest training guide for Oracle 1z0-1084-26

Individuals who pass the Oracle Cloud Infrastructure Developer Professional certification exam demonstrate to their employers and clients that they have the knowledge and skills necessary to succeed in the industry. Exam4PDF is aware that preparing with outdated 1z0-1084-26 Study Material results in a loss of time and money.

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. Enable and manage logs 2. Search and analyze logs
Topic 2: Cloud Native Fundamentals	11%	- Microservices Architecture 1. Microservices design methodology 2. Microservices architecture - Cloud Native Principles 1. Fundamentals of cloud native development 2. Key pillars of cloud native development
Topic 3: Leveraging Serverless Technologies for Cloud Native Development	33%	- API Gateway 1. Route API traffic to backend services 2. Process traffic from API clients - OCI Events 1. Build event-driven serverless applications - OCI Queue 1. Implement asynchronous messaging for microservices - OCI Streaming 1. Integrate systems using OCI Streaming 2. Use streaming for serverless and managed application architectures - Oracle Functions 1. Develop serverless applications with Oracle Functions
Topic 4: Cloud Native Applications and Containerization	33%	- Container Orchestration 1. Deploy containerized applications on Oracle Kubernetes Engine 2. Role of container orchestration - Oracle Cloud Infrastructure Registry 1. Push container images to OCIR 2. Pull container images from OCIR - Docker and Containerization 1. Containerization concepts 2. Docker architecture and components
Topic 5: 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. Apply security measures for cloud native development 3. Use OCI Vault to securely manage encryption keys

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Oracle Cloud Infrastructure Developer Professional Sample Questions (Q53-Q58):

NEW QUESTION # 53
Which open source engine is used by Oracle Cloud Infrastructure (OCI) to power Oracle Functions?

A. Fn Project
B. Kubeless
C. Apache OpenWhisk
D. Knative

Answer: A

Explanation:
Fn Project is the open source engine that is used by OCI to power Oracle Functions1. Fn Project is an open source, container native, serverless platform that can be run anywhere - any cloud or on-premises1. Fn Project is easy to use, extensible, and performant. You can download and install the open source distribution of Fn Project, develop and test a function locally, and then use the same tooling to deploy that function to Oracle Functions1.Verified References: Overview of Functions

NEW QUESTION # 54
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?

A. TransactionLatency[15m].groupBy(resourceId).mean() > 500
B. TransactionLatency[15m].filter(resourceId).mean() > 500
C. TransactionLatency[15m].grouping().mean() > 500
D. TransactionLatency[15m].absent().mean() > 500

Answer: C

Explanation:
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.

NEW QUESTION # 55
Which limit represents the maximum write throughput supported per partition of an Oracle Cloud Infrastructure stream when designing high-volume data producer workloads?

A. 8 MB per second
B. 4 MB per second
C. 2 MB per second
D. 1 MB per second

Answer: D

Explanation:
Oracle ' s current OCI Streaming limits specify that each stream partition supports a total data write rate of 1 MB per second. Partition count is therefore a fundamental capacity-planning parameter for producer workloads. If an application is expected to produce more than 1 MB per second, the stream must contain enough partitions to distribute the required ingress throughput appropriately. Oracle ' s partitioning guidance likewise states that expected throughput should be calculated before stream creation because each partition is limited to the 1 MB-per-second write rate. The 2 MB figure is not the documented write limit, while 4 MB and 8 MB exceed the per-partition ingress capacity. Therefore, the maximum write throughput represented by the available choices is 1 MB per second.

NEW QUESTION # 56
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. Terraform
B. Docker
C. Service Mesh
D. Big Data
E. Anomaly Detection
F. Kubemetes

Answer: B,C,F

Explanation:
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.

NEW QUESTION # 57
Which statement is NOT valid regarding the configurations and behaviors of Oracle Cloud Infrastructure (OCI) Queue?

A. The visibility timeout of a message is configurable from 1 second to 12 hours at the queue level and can be extended by a consumer during processing.
B. The maximum payload size of a single message published to an Oracle Cloud Infrastructure (OCI) Queue can be configured up to 1 MB.
C. The maximum number of delivery attempts before a message is transferred to a dead letter queue is configurable to values from 1 to 20.
D. The maximum retention period of a message is configurable from 10 seconds to 7 days and cannot be changed after the queue has been created.

Answer: B

Explanation:
Statement C is NOT valid because OCI Queue currently limits an individual message to256 KB, not a configurable 1 MB. Oracle ' s current functional limits specify a maximum message size of 256 KB, while a PutMessage batch request can contain up to 20 messages with a combined request size of 512 KB. Message retention can be configured from 10 seconds through seven days and cannot be changed after queue creation.
Queue-level visibility timeout ranges from one second to twelve hours, and visibility can be adjusted while processing. Oracle also documents maximum delivery attempts as configurable from 1 through 20 before failed messages are moved to the dead letter queue. Therefore, C alone contradicts the current Queue functional limits.

NEW QUESTION # 58
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