Exadata Warehouse Insights in OCI Operations Insights provides advanced analytics to forecast resource usage for Exadata systems.

Memory (A): Tracks and predicts memory utilization based on historical trends, aiding capacity planning.

CPU (D): Forecasts CPU usage, helping identify potential bottlenecks or over-provisioning.

Why not B or C?

Network usage (B): While monitored, it’s not a primary focus of Exadata Warehouse Insights’ future usage predictions.

AIOps (C): This is a methodology, not a resource usage metric.

These forecasts leverage historical data and what-if analysis for proactive management.

Logging Analytics is a core O&M service:

Monitor, aggregate, index, and analyze log data (A): Collects logs from OCI and external sources, indexes them for search, and provides analytics (e.g., clustering) to monitor and troubleshoot systems.

Why not B or C?

B: Log centralization occurs, but “subscription model” isn’t a feature.

C: Resource creation is an Events Service use case, not Logging Analytics.

This is its primary observability role.

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.

An MQL query for an alarm must evaluate a condition:

(CpuUtilization[1m].max() > 80).grouping().sum() (B):

CpuUtilization[1m]: Queries CPU utilization over 1-minute intervals.

.max(): Takes the maximum value per interval.

> 80: Sets a threshold (80%).

.grouping().sum(): Aggregates across metric streams, triggering the alarm if the sum exceeds 80%.This is a complete alarm query.

Why not A, C, or D?

A: Filters by shape but lacks a threshold or alarm condition.

C: Missing condition for alarming.

D: No threshold; case sensitivity doesn’t match OCI convention.

B ensures threshold-based alerting.

OCI Application Performance Monitoring (APM) provides tools to monitor application performance:

Distributed Tracing (A): Tracks requests across microservices, showing latency and dependencies via traces and spans.

Real User Monitoring (C): Captures real user interactions with web applications (e.g., page load times) using a Browser Agent.

Why not B or D?

Capacity Planning (B): Available in Operations Insights, not APM.

Java Management (D): Not a feature of APM; Java Agent is a tool, not a feature.

These features align with APM’s focus on performance and user experience.

APM tracks request flows using:

Using Trace ID (A): A unique identifier assigned to a trace (collection of spans) for a single user request. Propagated via HTTP headers, it links all spans across services.

Why not B or C?

User ID (B): Identifies users, not request flows.

Application Name (C): Too broad; doesn’t correlate specific requests.

Trace ID ensures end-to-end visibility in distributed systems.

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.