1z0-1111-23 Practice Exam Questions with Answers Oracle Cloud Infrastructure 2023 Observability Professional Certification

To archive logging data into OCI Object Storage, you can use Service Connector Hub, which is a cloud message bus platform that offers a single pane of glass for describing, running, and monitoring interactions when moving data between OCI services. You can create a service connector that has Logging as the source service and Object Storage as the target service. This service connector will automatically move your logging data from Logging to Object Storage based on your configuration.

 One of the primary use cases for the OCI Observability and Management (O&M) Logging Analytics service is to monitor, aggregate, index, and analyze log data. Logging Analytics is a feature of O&M that provides a unified platform for managing and analyzing log data from various sources, such as OCI services, on-premises systems, or custom applications. Logging Analytics can help you reduce noise, identify patterns, detect anomalies, and troubleshoot issues from your log data.

The data sources from where the APM Java agent can collect spans and metrics data are Jaeger or Zipkin. Jaeger and Zipkin are open source distributed tracing systems that provide end-to-end visibility into complex transactions across multiple services and components. The APM Java agent can collect spans and metrics data from Jaeger or Zipkin compatible applications and send them to APM for analysis and visualization.

Two features that are available for an Autonomous Database that is enabled with Database Management are:

• Monitor and analyze optimizer statistics advisor tasks and implement recommendations. Database Management provides a feature called OptimizerStatistics Advisor that analyzes the optimizer statistics collection tasks on your Autonomous Database and provides recommendations for improving performance and efficiency. You can use Database Management to monitor and analyze the advisor tasks and implement the recommendations.
• Generate AWR report for selected time period. Database Management also provides a feature called Automatic Workload Repository (AWR) Report that generates a report of the performance statistics and workload information of your Autonomous Database for a selected time period. You can use Database Management to generate AWR reports and view them in HTML or text format.

To create a corresponding OCI object storage bucket bearing the same name as that of the compute instance, you can use Oracle Functions and Service Connector Hub. Oracle Functions is a serverless platform that lets you create and run functions that respond to events. Service Connector Hub is a cloud message bus platform that lets you move data between OCI services. You can create a service connector that has Events as the source service and Functions as the target service. You can also specify a filter to select only the events related to compute instance creation (com.oraclecloud.computeapi.launchinstance.end). The function can then create an object storage bucket with the same name as the compute instance.

An invalid option for troubleshooting issues in OCI is to leverage OCI Cloud Guard to extract and visualize debug logs generated by your application. OCI Cloud Guard is a service that provides security posture management and threat detection for your OCI resources. Cloud Guard does not extract or visualize debug logs generated by your application. To troubleshoot issues using debug logs, you can use OCI Logging or Logging Analytics services.

Two examples of a Stack Monitoring deployment model are:

• Resources running on OCI compute instances. Stack Monitoring can monitor resources that are running on OCI compute instances, such as web servers, containers, or functions. Stack Monitoring can discover these resources using Management Agents or Functions Discovery Service.
• Resources running on-premises. Stack Monitoring can also monitor resources that are running on-premises, such as databases or applications. Stack Monitoring can discover these resources using Enterprise Manager Bridge or Management Agents.

An essential step while building a reliable log monitoring environment is noise baseline determination. Noise baseline is the level of noise or irrelevant log data that is present in your environment. By determining the noise baseline, you can filter out the noise and focus on the signal or meaningful log data that can help you monitor and troubleshoot your environment. For more information, see Reducing Noise.

The purpose of using Resolution in a Monitoring Query Language expression is to control the total length of each time window. Resolution is a parameter that specifies how often the query is evaluated and how the metric data is aggregated. For example, a resolution of 1m means that the query is evaluated every minute and the metric data is aggregated into one-minute intervals. Resolution affects the granularity and accuracy of the query results and the alarm condition.

The pillars of Observability are Logs, Metrics, and Traces. Logs are records of events that occur in your system or application. Metrics are numerical measurements that describe the behavior and performance of your system or application. Traces are collections of spans that represent a single user request or transaction across different services and components. You can use Dashboard to create a single view that shows logs, metrics, and traces from various sources in one place.

To ensure continuous monitoring of both on-premises VMs and OCI compute instances, you need to configure OCI Compute agent on both sources to collect the required resource metrics. The OCI Compute agent is a lightweight process that monitors the host and the processes running on the host. You can use OCI Events service to track the end-to-end deployment process (com.oraclecloud.computeapi.launchinstance.end) and creation of new buckets (com.oraclecloud.objectstorage.createbucket) in your target environment. You can use OCI Notifications and Events services to notify these changes via email or other supported protocols.

• Exadata Insights. Exadata Insights is a feature of Operations Insights that provides comprehensive monitoring and analysis of your Exadata systems, both on-premises and in the cloud. You can use Exadata Insights to view performance, utilization, availability, and configuration metrics of your Exadata systems and components. You can also use Exadata Insights to troubleshoot issues, optimize performance, and plan capacity for your Exadata systems.
• Capacity Planning. Capacity Planning is a feature of Operations Insights that helps you forecast the future resource consumption and capacity needs of your databases. You can use Capacity Planning to create scenarios based on historical trends, growth rates, and what-if analysis. You can also use Capacity Planning to compare different scenarios and identify potential risks and opportunities for your database capacity.

In APM, a distributed tracing user initiates a request through a browser. The first span is called the root span. A root span is the span that represents the entry point of a trace, such as an HTTP request from a browser or a message from a queue. A root span has no parent span and can have zero or more child spans.

To resend notifications at specified intervals if the alarm continues to be in the firing state, you need to enable repeat notification while creating the alarm. Repeat notification is an optional feature that allows you to specify how often you want to receive notifications for an alarm that remains in the firing state. For more information, see Creating Alarms.