ORACLE EXAM 1Z0-1111-25 FORUM: ORACLE CLOUD INFRASTRUCTURE 2025 OBSERVABILITY PROFESSIONAL - NEWPASSLEADER SUPPLIES YOU BEST TEST PRACTICE

Oracle Exam 1Z0-1111-25 Forum: Oracle Cloud Infrastructure 2025 Observability Professional - NewPassLeader Supplies you best Test Practice

Oracle Exam 1Z0-1111-25 Forum: Oracle Cloud Infrastructure 2025 Observability Professional - NewPassLeader Supplies you best Test Practice

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Oracle 1Z0-1111-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Respond to Cloud Resource Changes in Real-Time: This domain measures the skills of Event Managers and Integration Specialists in using OCI Events Service. It covers event structures, types, rules, and integration with other OCI services to respond effectively to resource changes and automate workflows.
Topic 2
  • Monitor Cloud Environments with Metrics and Alarms: This section tests Oracle Cloud Operators' knowledge of configuring OCI Monitoring Service. It includes enabling metrics for resource monitoring, setting up alarms based on best practices, and responding to real-time changes in cloud environments to ensure system reliability.
Topic 3
  • Monitor Applications with Deep Visibility into End-User Experience: This domain tests the knowledge of Application Performance Engineers and DevOps Specialists in using OCI Application Performance Monitoring (APM). It includes instrumenting applications for data collection, analyzing performance metrics, visualizing distributed application components, and diagnosing issues across multi-tier architectures.
Topic 4
  • Monitor Distributed Components of an Application Stack: This section measures the skills of Stack Monitoring Specialists and Cloud Administrators in using OCI Stack Monitoring. It covers discovering resources within application stacks, monitoring metrics across distributed components, and ensuring optimal performance through effective resource management.
Topic 5
  • Summarize OCI Observability and Management Services: This section evaluates the expertise of DevOps Engineers and IT Operations Managers in utilizing Oracle Cloud Infrastructure (OCI) Observability and Management services. It covers tools for monitoring, auditing, and managing cloud resources, emphasizing automation and machine learning-driven insights to optimize IT operations.
Topic 6
  • Centrally Manage and Visualize Log Data: This section assesses the abilities of Logging Analysts and Security Engineers in managing logs across an entire IT environment. It includes enabling log collection, creating connectors for log transitions, identifying log patterns, and visualizing logs for advanced analytics using OCI Logging Service.
Topic 7
  • Define the Pillars of Observability: This section of the exam measures the skills of Cloud Architects and Site Reliability Engineers (SREs) in understanding the three pillars of observability: logs, metrics, and traces. It explains how these components provide insights into system health and performance, enabling effective monitoring and troubleshooting in distributed cloud environments.

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Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which is the correct monitoring query that will monitor the CPU utilization threshold including an alarm?

  • A. CpuUtilization[1m](shape "VM.Standard.E4.Flex").max()
  • B. cpuutilization[1m].max().grouping().sum()
  • C. CpuUtilization[1m].max()
  • D. (CpuUtilization[1m].max() > 80).grouping().sum()

Answer: D

Explanation:
An MQL query for an alarm must evaluate a condition:
(CpuUtilization[1m].max() > 80).grouping().sum() (B):
CpuUtilization[1m]: Queries CPU utilization over 1-minute intervals.
.max(): Takes the maximum value per interval.
> 80: Sets a threshold (80%).
.grouping().sum(): Aggregates across metric streams, triggering the alarm if the sum exceeds 80%.
This is a complete alarm query.
Why not A, C, or D?
A: Filters by shape but lacks a threshold or alarm condition.
C: Missing condition for alarming.
D: No threshold; case sensitivity doesn't match OCI convention.
B ensures threshold-based alerting.


NEW QUESTION # 41
You are working on a project to automate the deployment of Oracle Cloud Infrastructure (OCI) compute instances that are pre-configured with web services. As part of the deployment workflow, you also need to create a corresponding OCI object storage bucket bearing the same name as that of the compute instance. Which of these two options can help you achieve this requirement? (Choose two.)

  • A. Cloud Agent Plugin for the compute instance
  • B. Oracle Functions
  • C. Events Service
  • D. Service Connector Hub
  • E. OCI CLI command, oci os bucket create auto

Answer: B,D

Explanation:
To automate the creation of an OCI Object Storage bucket with the same name as a compute instance during deployment, you need a mechanism to detect the instance creation event and trigger an action to create the bucket. Two OCI services that can achieve this are Service Connector Hub and Oracle Functions, used in conjunction with the Events Service.
Service Connector Hub (B): This service acts as a cloud message bus that facilitates data movement between OCI services. You can configure a service connector with the Events Service as the source (to detect compute instance creation events, e.g., com.oraclecloud.computeapi.launchinstance.end) and Oracle Functions as the target. The service connector filters and routes the event to trigger a function.
Oracle Functions (C): This is a serverless platform that allows you to write and execute code in response to events. You can create a function that retrieves the compute instance name from the event payload and uses the OCI SDK or API to create an Object Storage bucket with the same name.
Why not A, D, or E alone?
Cloud Agent Plugin (A): This is used for monitoring and managing compute instances but does not directly support bucket creation automation.
OCI CLI command (D): The command oci os bucket create auto is not a valid OCI CLI command (oci os bucket create is valid but requires manual invocation or scripting, not event-driven automation).
Events Service (E): While critical for detecting instance creation, it alone cannot execute the logic to create a bucket-it needs a target like Functions or Notifications.
This solution leverages the event-driven architecture of OCI, combining Events Service (implicitly used with Service Connector Hub) and Oracle Functions for execution.


NEW QUESTION # 42
What are the TWO benefits of Observability Lakehouse in Operations Insights? (Choose two.)

  • A. Identifies future resource usage Oracle Cloud
  • B. Allows Oracle Enterprise Manager's operations data for various use-cases
  • C. Enables custom analytics such as trending, forecasting, capacity planning, workload characterizations
  • D. Provides data based on a statistical analysis of AI data

Answer: B,C

Explanation:
The Observability Lakehouse in Operations Insights is a data repository for operational analytics:
Enables custom analytics (B): Supports trending (e.g., usage patterns), forecasting (e.g., resource needs), capacity planning, and workload profiling using advanced analytical tools, enhancing resource optimization.
Allows Oracle Enterprise Manager's data (D): Integrates operational data from Enterprise Manager (e.g., database metrics) for use cases like performance analysis and anomaly detection.
Why not A or C?
Statistical analysis of AI data (A): Too vague; Lakehouse focuses on operational data, not AI-specific stats.
Identifies future resource usage (C): Partial benefit of B, but not a standalone feature.
These capabilities improve operational decision-making.


NEW QUESTION # 43
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?

  • A. Summary Table
  • B. Word Cloud
  • C. Cluster

Answer: C

Explanation:
In Logging Analytics, ML-driven visualizations aid log analysis:
Cluster (A): Uses machine learning to group logs by structural similarity ("shape"), reducing noise and highlighting patterns or anomalies in large datasets.
Why not B or C?
Summary Table (B): Aggregates data tabularly, not ML-based or shape-focused.
Word Cloud (C): Displays word frequency, not structural grouping.
Cluster is ideal for large-scale log pattern recognition.


NEW QUESTION # 44
Choose two FluentD scenarios that apply when using continuous log collection with client-side processing. (Choose two.)

  • A. Managing apps/services which push logs to Object Storage
  • B. Comprehensive monitoring for OKE/Kubernetes
  • C. Monitoring systems that are not currently supported by Management Agent
  • D. Log Source

Answer: A,B

Explanation:
FluentD is an open-source data collector used for continuous log collection with client-side processing in OCI Logging. Two applicable scenarios are:
Managing apps/services which push logs to Object Storage (A): FluentD can be configured to collect logs from applications or services (e.g., Oracle Functions) that write logs to Object Storage buckets. It processes these logs client-side and forwards them to OCI Logging or Logging Analytics.
Comprehensive monitoring for OKE/Kubernetes (B): FluentD is widely used in Kubernetes environments like Oracle Container Engine for Kubernetes (OKE) to collect logs from pods, containers, and nodes. It processes these logs locally before sending them to OCI services for analysis.
Why not C or D?
Monitoring unsupported systems (C): While possible, this is not a primary FluentD scenario in OCI-it's more about extending Management Agent capabilities.
Log Source (D): This is a component of Logging Analytics, not a FluentD scenario.
FluentD's flexibility makes it ideal for these use cases in OCI's observability ecosystem.


NEW QUESTION # 45
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