QUESTIONS 1Z0-1111-25 PDF | PDF 1Z0-1111-25 DOWNLOAD

Questions 1Z0-1111-25 Pdf | PDF 1Z0-1111-25 Download

Questions 1Z0-1111-25 Pdf | PDF 1Z0-1111-25 Download

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Oracle Cloud Infrastructure 2025 Observability Professional Sample Questions (Q50-Q55):

NEW QUESTION # 50
What happens in Stack Monitoring after Management Agents are set up and resources are discovered?

  • A. Alarm rules will trigger when resources are down or performance thresholds are crossed
  • B. Metric data is immediately collected
  • C. OCI Notifications send email notifications
  • D. Management Agents discover resources that are running locally on the instance

Answer: B

Explanation:
In OCI Stack Monitoring, once Management Agents are deployed and resources (e.g., databases, applications) are discovered, the immediate next step is the collection of metric data.
Metric data is immediately collected (A): Management Agents are lightweight processes that continuously collect performance and health metrics from discovered resources (e.g., CPU usage, memory utilization) and send them to OCI services like Monitoring or Stack Monitoring. This data becomes available for visualization and analysis right after discovery.
Why not B, C, or D?
Alarm rules (B): Alarms are configured separately in the OCI Monitoring service and only trigger after metric data is collected and thresholds are breached-not an immediate post-discovery action.
Resource discovery (C): Discovery happens before this stage, as the question assumes resources are already discovered. Agents don't rediscover resources post-setup.
Notifications (D): Notifications require separate configuration (e.g., via the Notifications service) and are not an automatic outcome of agent setup and discovery.
This aligns with Stack Monitoring's purpose of providing real-time visibility into resource performance.


NEW QUESTION # 51
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 specific keywords
  • 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 # 52
Which TWO actions can be performed using the Database Management Service in Oracle Cloud Infrastructure (OCI)? (Choose two.)

  • A. Analyze and tune SQL performance issues of Oracle Databases on-premises, OCI, and multi-cloud environments
  • B. Forecast capacity issues of your Database services in OCI
  • C. Compare database performance across different time periods or perform real-time monitoring of SQL statements
  • D. Forecast capacity issues of Oracle Databases in on-premises, OCI, and multi-cloud environments

Answer: A,C

Explanation:
Database Management Service provides advanced database oversight:
Compare database performance across different time periods or perform real-time monitoring of SQL statements (C): Uses Performance Hub for historical and real-time SQL monitoring.
Analyze and tune SQL performance issues of Oracle Databases on-premises, OCI, and multi-cloud environments (D): Offers SQL tuning across diverse deployments.
Why not A or B?
A and B: Capacity forecasting is an Operations Insights feature, not Database Management.
These actions enhance database performance management.


NEW QUESTION # 53
What are the TWO benefits of Observability Lakehouse in Operations Insights? (Choose two.)

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

Answer: C,D

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 # 54
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 # 55
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