1z0-1111-23 Questions and Answers

The Machine Learning based visualization that is helpful in analyzing extremely large volumes of log records by grouping them based on their shape is Cluster. Cluster is a feature of Logging Analytics that uses machine learning algorithms to group log records into clusters based on their similarity or dissimilarity. Cluster can help you reduce noise, identify patterns, detect anomalies, and troubleshoot issues from your log data.

The required step to enable Stack Monitoring is to create a dynamic group for discovery service. A dynamic group is a group of OCI resources that match certain rules or criteria. You need to create a dynamic group for discovery service to allow Stack Monitoring to discover and monitor the resources in your stack. You also need to attach a policy to the dynamic group that grants the required permissions for Stack Monitoring.

To enable Operations Insights for an Autonomous Database, you need to enable Operations Insights for the database in the Autonomous Database details page. This will allow you to monitor and analyze the performance, resource utilization, and SQL activity of your Autonomous Database.

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.

The recommended method to continuously monitor and ingest logs from Object Storage buckets is ObjectCollection Rule. ObjectCollection Rule is a feature of Logging Analytics that allows you to collect log data from objects stored in Object Storage buckets. You can create an ObjectCollection Rule that specifies the bucket name, object name pattern, entity type, and log source for your log data.

To quickly identify the details of all users who were active in the last six hours along with any REST API calls that were executed, you can use Audit Analysis Dashboard. Audit Analysis Dashboard is a feature of Audit service that provides a graphical representation of audit events and trends. You can use Audit Analysis Dashboard to view charts and tables that show various metrics and dimensions of audit events, such as user activity, resource activity, operation types, or error codes.

The different metric namespaces used for APM are oracle_apm_rum, oracle_apm_synthetics, and oracle_apm_monitoring. A metric namespace is a unique name that identifies the source of the metrics. For APM, there are three metric namespaces that correspond to the three features of APM: Real User Monitoring (oracle_apm_rum), Synthetic Monitoring (oracle_apm_synthetics), and Application Performance Monitoring (oracle_apm_monitoring). You can use these metric namespaces to query and analyze metrics from APM. For more information, see APM Metrics.

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.

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 reasons why dedicated Vantage points matter are:

• Test internal customer applications. Dedicated Vantage points are private Vantage points that you can deploy in your own network or cloud environment. Dedicated Vantage points allow you to test internal customer applications that are not accessible from the public internet, such as intranet applications or applications behind a firewall.
• Applications on-premise or on secured network cannot be tested from a public Vantage Point. Public Vantage points are Vantage points that are hosted and managed by Oracle in various regions around the world. Public Vantage points allow you to test external customer applications that are accessible from the public internet, such as web applications or APIs. However, public Vantage points cannot test applications that are on-premise or on secured network, as they do not have access to those environments.

 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.

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.

Two APM agents that Application Performance Monitoring can use to collect data are Browser Agent and Java Agent. Browser Agent is a JavaScript snippet that can be embedded in web pages to collect Real User Monitoring (RUM) data, such as page load time, network latency, or user interactions. Java Agent is a Java library that can be attached to Java applications to collect Application Performance Monitoring (APM) data, such as traces, spans, metrics, or errors.

Two use cases of OCI Events Service are:

• Perform cleanup tasks when an OCI resource is terminated. For example, you can create an event rule that triggers a function when an instance is terminated (com.oraclecloud.computeapi.terminateinstance.end). The function can perform some cleanup tasks, such as deleting associated resources or sending notifications.
• Process files when they are uploaded in an Object Storage bucket. For example, you can create an event rule that triggers a function when an object is created in a bucket (com.oraclecloud.objectstorage.createobject). The function can process the file, such as resizing an image or converting a document format. For more information, see Events Overview.

The section within a Log that contains the Log Group’s OCID is Unified Envelope. Unified Envelope is a standard format that wraps around any log record collected by Logging Service. Unified Envelope contains metadata about the log record, such as timeCreated, compartmentId, logGroupId, or source. The logGroupId field contains the OCID of the Log Group that the log record belongs to.