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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Monitor applications with deep visibility into end-user experience | 20% | - Instrument applications for data collection - Examine visualizing and analyzing performance data - Explore the key concepts of Application Performance Monitoring (APM) |
| Topic 2: Monitor cloud environments with metrics and alarms | 18% | - Discuss enabling Metrics for monitoring OCI resources - Explain the key concepts of Monitoring Service - Configure Alarm Definitions using best practices |
| Topic 3: Monitor distributed components of an application stack | 7% | - Identify the key concepts of Stack Monitoring - Analyze discovering resources and monitoring with metrics |
| Topic 4: Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
| Topic 5: Respond to cloud resource changes in real-time | 10% | - Determine Event Structure, Event Types and Rules - Analyze the key concepts of Events Service - Respond to Events and integration with OCI services |
| Topic 6: Identify log data patterns and create visualizations for advanced analytics | 22% | - Explore log ingestion methods for Logging Analytics - Distinguish the key concepts of Logging Analytics - Present advanced analytics and features for troubleshooting - Analyze, search, filter and visualize logs |
| Topic 7: Centrally manage and visualize log data | 16% | - Explore managing and searching logs from the entire log estate - Create Connectors for Log Transitions - Distinguish log categories and enable log collection from sources |
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NEW QUESTION # 43
Which two components are optional while creating the Monitoring Query Language (MQL) expressions in the Oracle Cloud Infrastructure (OCI) Monitoring service? (Choose two.)
Answer: C,F
Explanation:
MQL expressions in OCI Monitoring retrieve and process metric data. Optional components include:
* Statistic (B): Specifies how to summarize data (e.g., avg, max). If omitted, raw data is returned.
* Grouping Function (D): Aggregates data across metric streams (e.g., by resourceId). It's optional if no grouping is needed.
* Why not A, C, E, or F?
* Interval (A): Defines the time window; defaults apply if omitted, but it's typically required for meaningful queries.
* Previous (C): Not a standard MQL component (possibly a typo).
* Metric (E): Mandatory to specify what's being queried.
* Dimensions (F): Filters data; required if specific streams are targeted, though optional in basic queries.
Statistic and Grouping Function enhance flexibility but aren't mandatory.
Reference: MQL Reference
NEW QUESTION # 44
In Application Performance Monitoring (APM), a distributed tracing user initiates a request through a browser. What is the first span called?
Answer: C
Explanation:
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.
Reference: APM Traces and Spans
NEW QUESTION # 45
Which is the correct monitoring query that will monitor the CPU utilization threshold including an alarm?
Answer: C
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.
Reference: Creating Alarms with MQL
NEW QUESTION # 46
Which Machine Learning-based visualization is helpful in analyzing extremely large volumes of log records by grouping them based on their shape?
Answer: A
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.
Reference: Logging Analytics Cluster
NEW QUESTION # 47
What are two examples of a Stack Monitoring deployment model? (Choose two.)
Answer: C,D
Explanation:
Stack Monitoring monitors application stacks across environments:
* Resources running on OCI compute instances (B): Monitors components (e.g., WebLogic, databases) on OCI VMs using Management Agents.
* Resources running on-premises (D): Tracks on-premises resources (e.g., Oracle Databases) via Enterprise Manager Bridge or agents.
* Why not A or C?
* A: Management Gateway is a proxy, not a resource host.
* C: Network appliances aren't typical Stack Monitoring targets.
These models cover cloud and on-premises stacks.
Reference: Stack Monitoring Deployment
NEW QUESTION # 48
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