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Oracle 1Z0-1111-25 Exam Questions
- Oracle Cloud Certifications
- Oracle Cloud Infrastructure Certifications
- Topic 1: 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.
- Topic 2: 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 3: 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 4: 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 5: 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 6: Distinguish the Key Concepts of Logging Analytics: This section evaluates the expertise of Data Analysts and Troubleshooting Specialists in using OCI Logging Analytics. It covers log ingestion methods, searching and filtering logs, performing advanced analytics, and leveraging machine learning-powered insights for proactive issue resolution.
- Topic 7: 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 8: 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.
Free Oracle 1Z0-1111-25 Exam Actual Questions
Note: Premium Questions for 1Z0-1111-25 were last updated On Apr. 04, 2025 (see below)
In Application Performance Monitoring (APM), a distributed tracing user initiates a request through a browser. What is the first span called?
In distributed tracing within OCI APM:
Root span (C): The first span in a trace, representing the entry point of a user request (e.g., an HTTP request from a browser). It has no parent span and initiates the chain of subsequent spans across services.
Why not A or B?
Ajax call (A): A type of request, not a span term.
Trace ID (B): A unique identifier for the entire trace, not a span.
The root span is foundational to tracing a request's journey.
Choose two FluentD scenarios that apply when using continuous log collection with client-side processing. (Choose two.)
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.
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)
Reducing noise in Logging Analytics improves log analysis focus:
Use parsed logs search (C): Searches based on extracted fields (e.g., severity=ERROR) filter out irrelevant logs, targeting specific issues.
Use time-picker to limit the volume of logs (D): Narrows the time range (e.g., last hour), reducing the dataset to relevant periods.
Why not A or B?
Histogram records (A): Visualizes data distribution, not a noise reduction method.
Specific keywords (B): Useful but less precise than parsed fields; raw text search isn't emphasized in Logging Analytics.
These methods enhance signal-to-noise ratio.
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?
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.
What is the correct logging CLI syntax for the log search with a query for REST call responses having status code 400, within the web Log Group and the application Log?
The correct OCI CLI syntax for searching logs must include the compartment, log group, log, and query:
B: oci logging-search search-logs --search-query 'search 'ocid1.compartment.oc1..aaaaaaaawqegmjifhni77bqm625cxioavoq775jckfn2syxqtmglabcccdxyz/web/application' | where data.statusCode = 400' --time-start 2022-02-06T00:00:00Z --time-end 2022-02-07T00:00:00Z
Specifies the compartment OCID, web Log Group, and application Log in the scope path.
Filters for data.statusCode = 400 using proper syntax (= instead of space).
Includes valid UTC time range.
Why not A, C, or D?
A: Missing log group/log; incorrect filter syntax (400 without =).
C: No filter for status code 400.
D: Incorrect command (oci log instead of oci logging-search); redundant parameters.
B aligns with OCI Logging's search syntax.
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