Free and Premium Oracle 1z0-1111-25 Dumps Questions Answers

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.

To create a rule in the OCI Events Service, you need to define what triggers the rule and what happens when it’s triggered. The two required components are:

Actions (B): These specify the tasks to perform when an event matches the rule (e.g., invoking a function, sending a notification, or streaming to a service). Without an action, the rule has no effect.

Rule Conditions (C): These define the criteria for matching events (e.g., event type like com.oraclecloud.computeapi.launchinstance.end or resource attributes). Conditions filter which events trigger the rule.

Why not A, D, or E?

Management Agent Cloud Service (A): This is unrelated to Events Service rules; it’s for monitoring resources.

Install Key (D): This is used for agent installation, not event rules.

Service Connector (E): While it can work with Events Service, it’s a separate service and not a required component of an event rule itself.

These two elements form the core of an OCI Events Service rule, enabling event-driven automation.

In distributed tracing within OCI APM:

Root span (C): The first span in a trace, representing the entry point of a user request (e.g., an HTTP request from a browser). It has no parent span and initiates the chain of subsequent spans across services.

Why not A or B?

Ajax call (A): A type of request, not a span term.

Trace ID (B): A unique identifier for the entire trace, not a span.

The root span is foundational to tracing a request’s journey.

APM uses specific metric namespaces to categorize its telemetry data:

oracle_apm_rum, oracle_apm_synthetics, and oracle_apm_monitoring (D):

oracle_apm_rum: Metrics from Real User Monitoring (e.g., page load times).

oracle_apm_synthetics: Metrics from Synthetic Monitoring (e.g., test response times).

oracle_apm_monitoring: Metrics from distributed tracing (e.g., span counts).These namespaces align with APM’s three core features.

Why not A, B, or C?

A: Individual metric names, not namespaces.

B: Incomplete and incorrect namespace naming.

C: Mixes metric types with partial namespace names.

These namespaces enable targeted metric queries in OCI Monitoring.

A Log Source in Logging Analytics defines how logs are collected and processed:

Which Parsers to use (A): Specifies the parsers (e.g., Syslog, JSON) that extract fields from logs, enabling structured analysis.

Where to find Logs (C): Defines the log location (e.g., file path /var/log/messages, database connection string), directing the collection process.

Why not B or D?

Where to store Log data (B): Storage is managed by Logging Analytics, not defined in the Source.

Which Management Agents to use (D): Agents are associated with Entities, not specified in the Source.

These elements configure log ingestion effectively.

In OCI APM’s Synthetic Monitoring, Vantage Points are locations from which synthetic tests (e.g., HTTP requests) are run. Dedicated Vantage Points are private, user-managed instances, distinct from public ones hosted by Oracle:

Applications on-premise or on secured network cannot be tested from a public Vantage Point (B): Public Vantage Points, located in Oracle-managed regions, lack access to private networks (e.g., on-premise servers or firewalled applications). Dedicated Vantage Points, deployed within a user’s network, overcome this limitation.

Test internal customer applications (C): Dedicated Vantage Points enable testing of internal applications (e.g., intranet sites) not exposed to the public internet, ensuring performance monitoring from within the secured environment.

Why not A or D?

Test from public Vantage Point (A): Contradicts B; public Vantage Points can’t access private networks.

Test Deployment Manager and Scheduler (D): These are unrelated OCI components, not Synthetic Monitoring targets.

Dedicated Vantage Points extend monitoring to restricted environments.

FluentD is an open-source data collector used for continuous log collection with client-side processing in OCI Logging. Two applicable scenarios are:

Managing apps/services which push logs to Object Storage (A): FluentD can be configured to collect logs from applications or services (e.g., Oracle Functions) that write logs to Object Storage buckets. It processes these logs client-side and forwards them to OCI Logging or Logging Analytics.

Comprehensive monitoring for OKE/Kubernetes (B): FluentD is widely used in Kubernetes environments like Oracle Container Engine for Kubernetes (OKE) to collect logs from pods, containers, and nodes. It processes these logs locally before sending them to OCI services for analysis.

Why not C or D?

Monitoring unsupported systems (C): While possible, this is not a primary FluentD scenario in OCI—it’s more about extending Management Agent capabilities.

Log Source (D): This is a component of Logging Analytics, not a FluentD scenario.

FluentD’s flexibility makes it ideal for these use cases in OCI’s observability ecosystem.

In OCI Logging Analytics, a Source defines the origin of log data from an asset on a host.

Source (B): Represents a log-generating asset (e.g., a file, database audit log, or Windows event log), specifying its location, format, and collection frequency. It’s associated with an Entity to enable log ingestion and parsing.

Why not A, C, or D?

Association (A): Links a Source to an Entity, not the asset itself.

Entity (C): A logical representation of a resource (e.g., a host), not the log source.

Parser (D): Extracts fields from logs, not the asset providing data.

Sources are foundational to log collection in Logging Analytics.

The APM Java Agent collects telemetry from Java applications:

Jaeger or Zipkin (C): These are open-source distributed tracing systems. The Java Agent can integrate with Jaeger- or Zipkin-compatible applications, collecting spans and metrics for APM analysis.

Why not A, B, or D?

NginX (A): A web server; APM uses other agents (e.g., Browser Agent) for such systems.

WebLogic, etc. (B): Application servers, but not direct data sources; the agent collects from the app, not the server type.

VMware ESXi (D): A hypervisor, unrelated to Java tracing.

Jaeger and Zipkin compatibility extends APM’s reach.

Database Management Service provides advanced database oversight:

Compare database performance across different time periods or perform real-time monitoring of SQL statements (C): Uses Performance Hub for historical and real-time SQL monitoring.

Analyze and tune SQL performance issues of Oracle Databases on-premises, OCI, and multi-cloud environments (D): Offers SQL tuning across diverse deployments.

Why not A or B?

A and B: Capacity forecasting is an Operations Insights feature, not Database Management.

These actions enhance database performance management.

The SQL Warehouse in Operations Insights focuses on SQL-level analysis:

Insights into the SQL performance (A): Provides a centralized repository of SQL performance data (e.g., execution time, resource usage) from monitored databases, enabling detailed analysis and optimization.

Why not B, C, or D?

Trend and Forecast (B): Handled by Capacity Planning in Operations Insights, not SQL Warehouse.

Consolidated Databases (C): A misnomer; it’s about data, not database consolidation.

Reduced costs (D): An outcome, not a direct feature.

SQL Warehouse enhances database performance visibility.

Operations Insights collects telemetry data for analysis from:

Management Agent (C): A lightweight process that gathers metrics (e.g., CPU, memory) from hosts, databases, or applications and sends them to Operations Insights.

Enterprise Manager (D): An on-premises tool that provides database performance and configuration data to Operations Insights.

Why not A or B?

OCI Streams (A): A streaming service, not a telemetry source for Operations Insights.

OCI Functions (B): Serverless compute, not a direct telemetry source.

These sources enable comprehensive resource monitoring.

SQL Warehouse in Operations Insights categorizes SQL performance:

Degraded (A): SQL statements with declining performance over time (e.g., increased latency), indicating potential issues.

Plan changes (C): SQL statements with altered execution plans, impacting performance or resource use.

Why not B or D?

Climbing (B): Not a standard category; possibly a misnomer.

Efficient (D): Positive performance isn’t a focus category.

These help identify and resolve SQL issues.

To investigate user activity and REST API calls over the last six hours, the OCI Audit service is the appropriate tool.

Audit (E): This service automatically records all API operations (including REST API calls) performed on OCI resources. It provides detailed logs with user details, timestamps, and actions, ideal for security and compliance investigations. You can filter audit logs by time range (e.g., last six hours) and user attributes.

Why not A, B, C, or D?

Notifications (A): Sends alerts but doesn’t store or analyze API call details.

Service Connectors (B): Moves data between services, not for auditing.

Management Agent (C): Collects metrics/logs from resources, not API audit data.

Logging (D): Handles application and system logs, not API activity tracking.

Audit logs are retained for 90 days by default, making this a perfect fit.

MQL expressions in OCI Monitoring retrieve and process metric data. Optional components include:

Statistic (B): Specifies how to summarize data (e.g., avg, max). If omitted, raw data is returned.

Grouping Function (D): Aggregates data across metric streams (e.g., by resourceId). It’s optional if no grouping is needed.

Why not A, C, E, or F?

Interval (A): Defines the time window; defaults apply if omitted, but it’s typicallyrequired for meaningful queries.

Previous (C): Not a standard MQL component (possibly a typo).

Metric (E): Mandatory to specify what’s being queried.

Dimensions (F): Filters data; required if specific streams are targeted, though optional in basic queries.

Statistic and Grouping Function enhance flexibility but aren’t mandatory.

Stack Monitoring tracks application stack components:

WebLogic Servers (B): Monitors performance and health of WebLogic instances.

Oracle External Databases (C): Tracks on-premises or cloud Oracle databases outside OCI’s native DBaaS.

Why not A or D?

Object Storage Buckets (A): Not supported by Stack Monitoring; use Logging instead.

Virtual Cloud Networks (D): Network monitoring is separate (e.g., VCN Flow Logs).

These align with Stack Monitoring’s focus on application stacks.

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.

To archive logs from OCI Logging Service to Object Storage, an automated data movement solution is needed:

Service Connector (A): Part of Service Connector Hub, this capability moves data between OCI services. A Service Connector can be configured with Logging as the source and Object Storage as the target, automatically transferring logs based on filters (e.g., log level) or schedules.

Why not B, C, or D?

ObjectCollectionRule (B): Used in Logging Analytics to ingest logsfromObject Storage, not archivetoit.

IAM policy (C): Governs permissions but doesn’t perform data movement.

Logging Query (D): Analyzes logs within Logging Service, not for archiving.

Service Connector ensures seamless, scalable log archiving.