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最新的 Oracle Cloud Infrastructure 1Z0-1111-25 免費考試真題 (Q33-Q38):

問題 #33
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?

A. Word Cloud
B. Cluster
C. Summary Table

答案：B

解題說明：
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.

問題 #34
Which step is essential while building a reliable log monitoring environment?

A. Creation of the Key Performance Indicators (KPIs) to monitor
B. Noise baseline determination
C. Define permissions for the user roles in the region
D. Determination of the Machine Learning models you need to program

答案：B

解題說明：
A reliable log monitoring environment filters signal from noise:
Noise baseline determination (B): Establishes the typical level of irrelevant log data, allowing filters (e.g., severity levels) to focus on meaningful events, improving monitoring effectiveness.
Why not A, C, or D?
A: ML models are advanced, not essential for baseline setup.
C: KPIs are useful but secondary to noise reduction.
D: Permissions are administrative, not core to reliability.
Noise baseline is foundational.

問題 #35
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)

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

答案：B,C

解題說明：
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.

問題 #36
Which is one of the primary use cases for the Oracle Cloud Infrastructure (OCI) Observability and Management (O&M) Logging Analytics service?

A. Monitor, aggregate, index, and analyze log data
B. Create OCI resources automatically based on log events and reports
C. Centralize and relocate any log based on a subscription model

答案：A

解題說明：
Logging Analytics is a core O&M service:
Monitor, aggregate, index, and analyze log data (A): Collects logs from OCI and external sources, indexes them for search, and provides analytics (e.g., clustering) to monitor and troubleshoot systems.
Why not B or C?
B: Log centralization occurs, but "subscription model" isn't a feature.
C: Resource creation is an Events Service use case, not Logging Analytics.
This is its primary observability role.

問題 #37
You need to find the Log Group from a Log line using Oracle Cloud Infrastructure (OCI) Logging Service. Which section within a Log contains the Log Group's OCID?

A. Source Section
B. Oracle section
C. Data section
D. Unified Envelope

答案：D

解題說明：
In OCI Logging, logs are wrapped in a Unified Envelope:
Unified Envelope (D): Contains metadata like logGroupId (the OCID of the Log Group), timeCreated, and compartmentId. This identifies the Log Group for each log entry.
Why not A, B, or C?
Source Section (A): Indicates log origin, not the group OCID.
Data section (B): Holds the log content, not metadata like OCID.
Oracle section (C): Not a valid log section.
The Unified Envelope standardizes log metadata.

問題 #38
......

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熱門的1Z0-1111-25新版題庫上線，Oracle Oracle Cloud Infrastructure認證1Z0-1111-25考試題庫提供免費下載

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