Practice Free 1z0-1111-23 Oracle Cloud Infrastructure 2023 Observability Professional Exam Questions Answers With Explanation

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.

To grant the CloudOps group the ability to view and retrieve monitoring metrics only for all monitoring-enabled compute instances, you need to use a policy statement with a condition that filters by the metric namespace. The metric namespace is a unique name that identifies the source of the metrics. For compute instances, the metric namespace is oci_computeagent. Therefore, the policy statement should be: Allow group cloudops to read metrics in tenancy where target.metrics.namespace=‘oci_computeagent’

A valid statement regarding Management Agent Cloud Service is that Management Agent Cloud Service allows OCI services to collect data from on-premises and cloud assets, and transport data into OCI services, such as Logging Analytics, OCI Monitoring, or even a custom endpoint hosted in OCI. Management Agent Cloud Service does not support transporting data into AWS or GCS.

Two actions that can be performed using the Database Management Service in OCI are:

• Compare Database performance across different time periods or perform real-time monitoring of SQL statements. Database Management Service provides various tools and dashboards that let you compare and monitor the performance of your databases and SQL statements. You can use Performance Hub to view real-time and historical performance data of your databases. You can also use SQL Monitor to view real-time and historical execution details of your SQL statements.
• Analyze and Tune SQL performance issues of Oracle Databases on-premises, OCI and Multi-Cloud environment. Database Management Service provides various features and functions that let you analyze and tune SQL performance issues of your databases across different environments. You can use SQL Tuning Advisor to get recommendations for improving SQL performance. You can also use SQL Performance Analyzer to compare the performance of SQL statements before and after a change.

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.

 Two benefits of Observability Lakehouse in Operations Insights are:

• Allows Oracle Enterprise Manager’s operations data for various use-cases. Observability Lakehouse is a data lake that stores and analyzes operational data from different sources, such as Oracle Enterprise Manager, Oracle Cloud Infrastructure, and Autonomous Database. You can use Observability Lakehouse to access and query your operations data for various use-cases, such as performance analysis, capacity planning, anomaly detection, and root cause analysis.
• Enables custom analytics such as trending, forecasting, capacity planning, workload characterizations. Observability Lakehouse provides a rich set of analytical functions and tools that let you perform custom analytics on your operations data. You can use Observability Lakehouse to create trends, forecasts, capacity plans, workload profiles, and other insights that can help you optimize your operations and performance.

Database Audit Logs contain rich information to process for analytics, such as user actions, database operations, and security events. Logging Analytics can ingest and analyze these logs to provide insights into the health and performance of your databases.

The correct logging CLI syntax for the log search with a query for REST call responses having status code 400, within the web Log Group and the application Log is oci logging-search search-logs --search-query ‘search “ocidl.compartment.ocl…aaaaaaaawqegmjifhni77bqm625cxioavoq775jckfn2syxqtmglabcccdxyz/web/application” I where data.statusCode = 400’–time-start 2022-02-06T00:00:002 --time-end 2022-02-07T00:00:002. This syntax will search for log records that match the following criteria:

• The log records belong to the compartment with OCID ocidl.compartment.ocl…aaaaaaaawqegmjifhni77bqm625cxioavoq775jckfn2syxqtmglabcccdxyz
• The log records belong to the web Log Group and the application Log
• The log records have a field called data.statusCode with a value of 400
• The log records are within the time range from 2022-02-06T00:00:002 to 2022-02-07T00:00:002

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.

Two functions that the Trace Explorer allows you to do in APM are:

• View the details of specific spans. Trace Explorer is a tool that lets you view and analyze the traces and spans collected by APM. You can use Trace Explorer to drill down into the details of specific spans, such as duration, status, tags, logs, and errors.
• Select pre-defined queries for common use cases. Trace Explorer also provides a set of pre-defined queries that can help you find traces and spans based on common use cases, such as slowest traces, error traces, or traces by service name.

 You would use a vantage point in Synthetic Monitoring. Synthetic Monitoring is a feature of APM that allows you to create synthetic tests to monitor the availability and performance of your web applications or APIs from different locations around the world. A vantage point is a location from where you can run synthetic tests against your target applications or APIs. A vantage point can be public or dedicated depending on your needs.

The Management Agent group that allows the agent to upload data to the discovery service is AgentUsersGrp. AgentUsersGrp is a predefined dynamic group that contains all the Management Agents in your tenancy. You need to attach a policy to this group that grants the permission to upload data to the discovery service endpoint.

Two things that are stored in a Log Source of Logging Analytics are:

• Which parsers to use. A parser is a component that extracts fields and values from log data. A log source can have one or more parsers associated with it to enable Logging Analytics to parse and analyze the log data.
• Where to find logs. A log source defines the location, format, and frequency of the log data. A log source can specify a file path, a database connection string, or a Windows event log name to indicate where to find logs.

To start collecting a specific log for an Entity in Logging Analytics, you need to create an association of required Log Source with that Entity. An Entity is a logical representation of a resource that can generate log data, such as a host, a database, or an application. A Log Source is a definition of the location, format, and frequency of the log data. An association is a link between an Entity and a Log Source that enables Logging Analytics to collect and parse log data from that Entity.

Two FluentD scenarios that apply when using continuous Log Collection with client-side processing are:

• Managing apps/services which push logs to Object Storage. FluentD is an open source data collector that can collect and process log data from various sources. You can use FluentD to manage apps/services that push logs to Object Storage, such as Oracle Functions or Kubernetes. You can configure FluentD to read logsfrom Object Storage buckets and send them to Logging Service or Logging Analytics for analysis.
• Comprehensive monitoring for OKE/Kubernetes. FluentD is also a popular choice for monitoring Kubernetes clusters, such as Oracle Container Engine for Kubernetes (OKE). You can use FluentD to collect and process logs from Kubernetes pods, containers, and nodes, and send them to Logging Service or Logging Analytics for analysis.