PASS GUARANTEED QUIZ 2025 ORACLE LATEST 1Z0-1111-25 VALID DUMPS PDF

Pass Guaranteed Quiz 2025 Oracle Latest 1Z0-1111-25 Valid Dumps Pdf

Pass Guaranteed Quiz 2025 Oracle Latest 1Z0-1111-25 Valid Dumps Pdf

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Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q39-Q44):

NEW QUESTION # 39
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. Log Source
  • C. Comprehensive monitoring for OKE/Kubernetes
  • D. Monitoring systems that are not currently supported by Management Agent

Answer: A,C

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

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

Answer: A

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
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)

  • A. Use specific keywords
  • B. Use time-picker to limit the volume of logs
  • C. Use histogram records
  • D. Use parsed logs search

Answer: B,D

Explanation:
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.


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

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

Answer: A,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
What are the two items required to create a rule for the Oracle Cloud Infrastructure (OCI) Events Service? (Choose two.)

  • A. Actions
  • B. Service Connector
  • C. Rule Conditions
  • D. Install Key
  • E. Management Agent Cloud Service

Answer: A,C

Explanation:
To create a rule in the OCI Events Service, you need to define what triggers the rule and what happens when it's triggered. The two required components are:
Actions (B): These specify the tasks to perform when an event matches the rule (e.g., invoking a function, sending a notification, or streaming to a service). Without an action, the rule has no effect.
Rule Conditions (C): These define the criteria for matching events (e.g., event type like com.oraclecloud.computeapi.launchinstance.end or resource attributes). Conditions filter which events trigger the rule.
Why not A, D, or E?
Management Agent Cloud Service (A): This is unrelated to Events Service rules; it's for monitoring resources.
Install Key (D): This is used for agent installation, not event rules.
Service Connector (E): While it can work with Events Service, it's a separate service and not a required component of an event rule itself.
These two elements form the core of an OCI Events Service rule, enabling event-driven automation.


NEW QUESTION # 44
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