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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Identify log data patterns and create visualizations for advanced analytics | 22% | - Perform advanced log analytics and troubleshooting - Use Logging Analytics for log ingestion and analysis - Search, filter and visualize log data |
| Topic 2: Define the pillars of Observability | 7% | - Summarize OCI Observability and Management Services |
| Topic 3: Monitor distributed components of an application stack | 7% | - Discover and monitor distributed application components - Analyze infrastructure and application metrics - Use Stack Monitoring |
| Topic 4: Monitor applications with deep visibility into end-user experience | 20% | - Use Application Performance Monitoring - Configure application instrumentation and data collection - Analyze application performance and end-user experience |
| Topic 5: Monitor cloud environments with metrics and alarms | 18% | - Explain the key concepts of Monitoring Service - Discuss enabling Metrics for monitoring OCI resources - Configure Alarm Definitions using best practices |
| Topic 6: Respond to cloud resource changes in real-time | 10% | - Analyze the key concepts of Events Service - Determine Event Structure, Event Types and Rules - Respond to Events and integration with OCI services |
| Topic 7: Centrally manage and visualize log data | 16% | - Collect, search and analyze log data - Configure logging and log connectors - Manage logs using OCI Logging |
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NEW QUESTION # 31
There are several ways to reduce Logging Analytics noise. Select the TWO options that apply. (Choose two.)
Answer: A,B
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.
Reference: Logging Analytics Search
NEW QUESTION # 32
Your on-premises private cloud environment consists of virtual machines hosting a set of application servers.
These VMs are currently monitored using a 3rd party monitoring tool for resource metrics such as CPU and Memory utilization. You have created an automation workflow to transform these application servers into Oracle Cloud Infrastructure (OCI) which will deploy a set of new compute instances. There are a few requirements to consider while running this task: Ensure continuous monitoring is enabled, so the current monitored resource metrics are continuously collected and reported; Monitor the completion of Compute Instance deployment during the workflow and notify with email on each execution; Notify with email for any new OCI Object Storage buckets created after the migration workflow. What solution would you recommend to achieve these requirements?
Answer: B
Explanation:
The solution must address continuous monitoring and event-driven notifications:
* D:
* OCI Compute agent on on-premises VMs and OCI instances: Ensures metric continuity (e.g., CPU, memory) across the migration, using Management Agents for both environments.
* Events service: Tracks launchinstance.end for deployment completion and createbucket for new buckets.
* Notifications and Events: Sends email alerts for these events.
* Why not A, B, or C?
* A: Misses on-premises monitoring continuity.
* B: Lacks bucket creation tracking.
* C: Redundant 3rd-party tool use; OCI agents suffice.
D provides end-to-end coverage.
Reference: Management Agents , Events Service
NEW QUESTION # 33
What is the function of OCI Management Agent?
Answer: B
Explanation:
OCI Management Agent is installed on a host and provides a framework through which supported OCI service plug-ins collect telemetry from monitored data sources. Depending on the deployed plug-ins and configuration, the agent can collect logs, metrics, configuration information, status information, and other management data and upload it securely to the corresponding OCI service. The Management Agent service also manages the agents, their plug-ins, registration, and lifecycle. Distributed trace analysis is primarily performed by OCI Application Performance Monitoring and its APM agents. Cost and usage optimization belongs to cost-management and analytics capabilities, while responding to resource state changes is the role of OCI Events and its configured actions. Therefore, enabling communication and data collection between managed targets and supported OCI services is the Management Agent's fundamental function. Oracle documentation
NEW QUESTION # 34
You are a cloud administrator tasked with creating a dashboard that displays the distribution of a field's values, such as the number of occurrences of each HTTP error code. Which OCI Logging Analytics visualization should you use?
Answer: D
Explanation:
A pie chart is suitable for displaying how the total number of log records is distributed among the distinct values of a selected field. For example, a query can group records by HTTP status code and calculate the count associated with each code. The pie chart displays each status code as a segment, making the relative proportion of errors such as 400, 401, 404, or 500 easy to compare. A single-number tile presents only one aggregated value and cannot communicate the distribution among multiple codes. A table can list the values and counts but provides less immediate visual comparison. Cluster visualization is a machine learning-based capability that groups log records according to their underlying patterns or shapes rather than simply presenting a categorical field distribution. Therefore, the pie chart is the best answer. Oracle documentation
NEW QUESTION # 35
In Application Performance Monitoring (APM), where is the span context information located during transfer?
Answer: C
Explanation:
In OCI APM, span context (e.g., Trace ID, Span ID) is propagated across services to track requests.
* In HTTP header (B): Span context is embedded in HTTP headers (e.g., X-B3-TraceId) during transfer between services. This allows APM to correlate spans across distributed systems for a single user request.
* Why not A, C, or D?
* Service boundaries (A): This is a conceptual term, not a location for data.
* HTTP call (C): Too vague-"HTTP call" isn't a specific storage location.
* Browser and microservices (D): Context originates here but is transferred via headers, not stored locally during transit.
This follows the OpenTracing standard used by OCI APM.
Reference: APM Traces and Spans
NEW QUESTION # 36
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