IBM Assessment: IBM AIOps v1 Advanced Practice Exam: Hard Questions 2025

An enterprise is deploying IBM Cloud Pak for AIOps across three OpenShift clusters (dev, staging, prod). They require strict workload isolation, consistent model behavior across environments, and the ability to promote configurations safely. They also want to minimize duplicate integrations and credentials sprawl. Which architecture and operating approach best meets these goals?

A team reports intermittent failure of topology-based root cause detection. Event correlation works, but the suspected root cause service often changes between runs. Investigation shows topology data is coming from two sources: a CMDB and Kubernetes discovery. Both describe the same services but use different naming conventions and occasionally stale identifiers. What is the most effective remediation to stabilize RCA outcomes?

A global bank ingests events from Netcool/OMNIbus, Prometheus Alertmanager, and multiple public cloud services. During a network partition between two data centers, duplicate events surge and correlation quality collapses: incidents split into multiple stories and auto-ticketing creates duplicates. Which change most directly improves resilience and prevents duplicates during intermittent connectivity while preserving correlation fidelity?

An SRE team complains that a single flapping dependency (intermittent DNS) causes dozens of alerts across many services. Cloud Pak for AIOps forms multiple stories because the alerts arrive with slightly different summaries and severities depending on the emitting system. The team wants one actionable incident per impact window, but without hiding genuine unrelated failures. What is the best approach?

A company integrates ServiceNow for ticketing. They enable auto-creation of incidents from AIOps stories. After go-live, operators see incidents created, then immediately resolved, then re-opened repeatedly. Event streams show frequent "clear" events arriving out of order relative to "problem" events due to upstream buffering. What is the most robust fix?

In a microservices platform, Cloud Pak for AIOps consistently identifies a downstream database as the root cause during checkout failures, but engineers prove the true issue is a misconfigured feature flag in the checkout service causing connection storms. Metrics show the database saturates only after the checkout service deploy. Which change is most likely to improve root cause accuracy?

AIOps RCA quality drops after a major platform re-architecture (new service mesh, renamed services, and new namespaces). Stories still form, but RCA frequently points to retired components that no longer exist. What should the team do first to restore reliable RCA and avoid misleading results?

During an outage, two correlated stories appear: one for API latency and one for payment failures. Engineers suspect they share a common cause but the system keeps them separate. Review shows the API story is dominated by metric anomalies, while the payment story is dominated by log-derived events; both reference the same upstream gateway service but with different resource identifiers. What is the most targeted fix to improve cross-signal correlation without over-grouping unrelated incidents?

A production Cloud Pak for AIOps environment experiences periodic ingestion backpressure. During spikes, events arrive late, correlation produces delayed stories, and some alerts time out in upstream systems. The cluster has sufficient CPU/memory headroom overall, but specific pods restart due to resource limits and uneven load. What is the best optimization strategy?

A team wants to improve signal-to-noise by filtering low-value events at ingestion. However, after aggressive filtering, the RCA engine produces less accurate root cause and more 'unknown' outcomes, even though stories are fewer. Which approach best balances noise reduction with analytical accuracy?