[2023年12月] 試験1z0-1111-23最新ブレーン専門問題集はここ
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質問 # 39
Which response contains rich information to process for analytics?
- A. Database Audit Logs
- B. Log Sources
- C. Entity types
- D. Logging Analytic Entities
正解：A
解説：
Explanation
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.
質問 # 40
Which pillars of Observability are available as a single view from the Dashboard?
- A. Logs, Metrics, and Traces
- B. Logging Analytics, Database Management, Stack Monitoring
- C. Compute, Storage, and Network
- D. Log data, Query language, Dashboard widgets
正解：A
解説：
Explanation
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.
質問 # 41
Which of the following feature of Logging Analytics is used for identifying and tagging the problem logs during ingestion time?
- A. Labels
- B. Log Origin
- C. Extended Fields
- D. Entity Type
正解：A
解説：
Explanation
Labels are a feature of Logging Analytics that allow you to identify and tag the problem logs during ingestion time. Labels are predefined tags that can be applied to log records based on certain conditions or patterns. For example, you can create a label called "Error" that is applied to any log record that contains the word "error" or "exception". Labels can help you filter, search, and analyze your log data more efficiently.
質問 # 42
Which TWO future resource usages are identified by Exadata Warehouse insights custom analytics under Operations Insights? (Choose two.)
- A. Memory
- B. CPU
- C. Network usage
- D. AIOps
正解：A、B
解説：
Explanation
Two future resource usages that are identified by Exadata Warehouse insights custom analytics under Operations Insights are Memory and CPU. Exadata Warehouse insights custom analytics is a feature of Operations Insights that provides advanced analytics and visualization of Exadata performance data. You can use Exadata Warehouse insights custom analytics to create scenarios based on historical trends, growth rates, and what-if analysis. You can also use Exadata Warehouse insights custom analytics to forecast future resource usages, such as Memory or CPU, and plan capacity for your Exadata systems.
質問 # 43
Which step is essential while building a reliable log monitoring environment?
- A. Creation of the Key Performance Indicators (KPIs) to monitor
- B. Noise baseline determination
- C. Determination of the Machine Learning models you need to program
- D. Define permissions for the user roles in the region
正解：B
解説：
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.
質問 # 44
Which pillars of Observability are available as a single view from the Dashboard?
- A. Logs, Metrics, and Traces
- B. Logging Analytics, Database Management, Stack Monitoring
- C. Compute, Storage, and Network
- D. Log data, Query language, Dashboard widgets
正解：A
解説：
Explanation
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.
質問 # 45
What happens in Stack Monitoring after Management agents are set up and resources are discovered?
- A. OCI Notifications send email notifications
- B. Metric data is immediately collected
- C. Management agents discover resources that are running locally on the instance
- D. Alarm rules will trigger when resources are down or performance thresholds are crossed.
正解：B
解説：
Explanation
After Management agents are set up and resources are discovered, metric data is immediately collected.
Management agents are lightweight processes that collect and send metric data from your resources to OCI services, such as Monitoring, Logging, or Stack Monitoring. Once the resources are discovered by the discovery service, the metric data is immediately collected and available for monitoring and analysis.
質問 # 46
Which of the following statement is NOTIvalid regarding Management Agent Cloud Service?
- A. Management Agent Cloud Service allows OCI services to collect data from on-premises and cloud assets, except that it can only transport data into AWS or GCS.
- B. Management Agent Cloud Service is self monitored.
- C. Management Agent Cloud Service transport customer data to Logging Analytics, OCI Monitoring, or even a custom endpoint hosted in OCI.
- D. Management Agent Cloud service enables on demand execution of operations against monitored resources.
正解：A
解説：
Explanation
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.
質問 # 47
What are the two items required to create a rule for the Oracle Cloud Infrastructure (OCI) Events Service?
(Choose two.)
- A. Rule Conditions
- B. Actions
- C. Service Connector
- D. Install Key
- E. Management Agent Cloud Service
正解：A、B
解説：
Explanation
Two items required to create a rule for the OCI Events Service are:
* Actions. Actions are the tasks that you want to perform when an event matches your rule condition. For example, you can create an action that sends a notification, invokes a function, or streams an event.
* Rule Conditions. Rule Conditions are the criteria that you use to filter events based on their attributes or patterns. For example, you can create a rule condition that matches events related to instance creation or deletion.
質問 # 48
In Application Performance Monitoring (APM), a distributed tracing user initiates a re-quest through a browser. What is the first span called?
- A. Trace id
- B. Root span
- C. Ajax call
正解：B
解説：
Explanation
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.
質問 # 49
From the following, select the different metric namespaces used for APM.?
- A. AjaxDownloadTime, TotalTraceCount, Oracle_pm_rum
- B. oracle_apm_rum, oracle_apm_synthetics, and oracle_apm_monitoring.
- C. RUM metrics, oracle_apm_monitoring, Oracle_apm_synthetic
- D. oracle_apm_monitoring namespace, synthetics, and monitoring
正解：B
解説：
Explanation
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.
質問 # 50
What are the TWO benefits of Observability Lakehouse in Operations Insights? (Choose two.)
- A. Enables custom analytics such as trending, forecasting, capacity planning, workload characterizations
- B. Provides data based on a statistical analysis of AI data
- C. Allows Oracle Enterprise Manager's operations data for various use-cases
- D. Identifies future resource usage Oracle Cloud
正解：A、C
解説：
Explanation
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.
質問 # 51
You have been asked to run queries against the OracleCloud Infrastructure (OCI) monitoring data. The data request is an aggregated query. You get the following error: query has exceeded the maximum number of metric streams Which step would you take to run the aggregated query correctly against OCI?
- A. Ignore the error because aggregating multiple metric streams is not supported.
- B. Check the OCI Identity and Access Management (IAM) policy and re-run the query.
- C. Reduce the query and limit the number of streams by specifying dimensions.
- D. Increase the query limits and the number of streams by specifying dimensions.
正解：C
解説：
Explanation
To run the aggregated query correctly against OCI, you need to reduce the query and limit the number of streams by specifying dimensions. Dimensions are key-value pairs that define the characteristics of a metric.
By adding dimensions to your query, you can filter out the irrelevant metric streams and avoid exceeding the maximum number of metric streams. For more information, see Querying Metrics.
質問 # 52
What are two examples of a Stack Monitoring deployment model? (Choose two.)
- A. Resources running on OCI compute instances
- B. Resources running on-premises
- C. Resources running on Management gateway
- D. Resources running on a network appliance
正解：A、B
解説：
Explanation
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.
質問 # 53
Which Machine Learning based visualization is helpful in analyzing extremely large vol-umes of log records by grouping them based on their shape?
- A. Cluster
- B. Word Cloud
- C. Summary Table
正解：A
解説：
Explanation
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.
質問 # 54
Which of the following capabilities does the performance management feature of Database Management Services offer to a managed database?
- A. Visualizes and performs trend analysis from AWR data to detect issues using AWR explorer
- B. Automatically invokes full stats gathering of objects to improve performance of regressed SQLS
- C. Dynamically modifies database initialization parameters to improve performance
正解：A
解説：
Explanation
The performance management feature of Database Management Services offers the capability to visualize and perform trend analysis from AWR data to detect issues using AWR explorer. AWR explorer is a tool that lets you view and analyze the Automatic Workload Repository (AWR) data from your databases. You can use AWR explorer to compare performance across different time periods, identify performance bottlenecks, and troubleshoot issues.
質問 # 55
Which is one of the primary use case for the circleCloud Infrastructure (OCI) Observabilityand Management (O&M) Logging Analytics service?
- A. Centralize and relocate any log based on a subscription model
- B. Create OCI resources automatically based on log events and reports
- C. Monitor, aggregate, index, and analyze log data
正解：C
解説：
Explanation
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.
質問 # 56
What two APM agents can Application Performance Monitoring use to collect data? (Choose two.)
- A. Cloud Agent
- B. Browser Agent
- C. Java Agent
- D. Management Agent
正解：B、C
解説：
Explanation
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.
質問 # 57
When would you use a vantage point in Application Performance Monitoring (APM)?
- A. Distributed Tracing
- B. Synthetic Monitoring
- C. Application Insights
- D. Java Management
正解：B
解説：
Explanation
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.
質問 # 58
Which TWO resources can be monitored by Stack Monitoring? (Choose two.)
- A. WebLogic Servers
- B. Virtual Cloud Networks
- C. Object Storage Buckets
- D. Oracle External Databases
正解：A、D
解説：
Explanation
Stack Monitoring can monitor Oracle External Databases and WebLogic Servers as resources. Stack Monitoring is a feature of Application Performance Monitoring that allows you to monitor the performance and availability of your entire application stack, including databases, servers, containers, and functions. You can create custom stacks by selecting the resources that you want to monitor and view their metrics, alerts, and topology in a single dashboard. For more information, see Stack Monitoring.
質問 # 59
How does a user start collecting a specific log for an Entity in Logging Analytics?
- A. Configure a path for the Log File
- B. Enable a Parser for the Log
- C. Identify Fields to extract
- D. Create an Association of required Log Source with that Entity
正解：D
解説：
Explanation
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.
質問 # 60
Which two features are provided by Application Performance Monitoring? (Choose two.)
- A. Distributed Tracing
- B. Real User Monitoring
- C. Java Management
- D. Capacity Planning
正解：A、B
解説：
Explanation
Application Performance Monitoring provides two features: Distributed Tracing and Real User Monitoring.
Distributed Tracing allows you to monitor and troubleshoot the performance of your microservices applications by tracing the requests across different services and components. Real User Monitoring allows you to measure and improve the user experience of your web applications by capturing and analyzing the real user sessions, page load times, errors, and feedback.
質問 # 61
Which is a valid Log Category name in Oracle Cloud Infrastructure (OCI) Logging Service?
- A. Custom Logs
- B. System Logs
- C. OCI Agent Logs
- D. VCN Logs
正解：A
解説：
Explanation
A valid Log Category name in OCI Logging Service is Custom Logs. Custom Logs are logs that are generated by your own applications or services that are not part of OCI services. Custom Logs can be collected by Logging Service using various methods, such as Unified Monitoring Agent, SDKs, or APIs. Custom Logs can help you monitor and troubleshoot your custom applications or services.
質問 # 62
Which of the following is required to enable Stack Monitoring?
- A. Dynamic group for discovery service
- B. User group for VNC collection
- C. Machine Learning group for resource associations
正解：A
解説：
Explanation
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.
質問 # 63
Choose two FluentD scenarios that apply when using continuous Log Collection with cli-ent-side processing.?
(Choose two.)
- A. Managing apps/services which push logs to Object Storage
- B. Monitoring systems that are not currently supported by Management agent
- C. Comprehensive monitoring for OKE/Kubernetes
- D. Log Source
正解：B、C
解説：
Explanation
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.
質問 # 64
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