IBM A1000-138 Advanced Practice Exam: Hard Questions 2025

An enterprise has deployed IBM AIOps with multiple data sources including Prometheus, Splunk, and ServiceNow. The correlation engine is generating excessive noise with a 40% false positive rate for incident grouping. The team notices that events from different microservices with similar error patterns are not being correlated, while unrelated infrastructure alerts are being grouped together. What is the MOST effective approach to resolve this issue?

A financial services organization is implementing event-driven automation with IBM AIOps. They require that specific runbook automations execute only when events meet complex conditions: severity is critical AND the affected service has active customer transactions AND the error rate exceeds baseline by 3 standard deviations within a 2-minute window. However, the automation is triggering prematurely on individual condition matches. What architectural pattern should be implemented?

During a major incident, the IBM AIOps system correlated 847 alerts into a single incident, but the SRE team identified that three critical alerts from the payment gateway microservice were incorrectly grouped with a separate database performance incident. Post-incident analysis reveals this has happened twice in the past month. The topology shows correct service dependencies. What is the MOST likely root cause and remediation?

An organization's AIOps implementation processes 2 million events per hour across a hybrid cloud environment. They're experiencing a 4-second delay in anomaly detection, which violates their SLA requirement of sub-2-second detection. The system uses a distributed architecture with 8 data processing nodes. CPU utilization averages 45%, memory at 60%, but network I/O shows intermittent spikes to 95% on the aggregation layer. What optimization strategy addresses the bottleneck?

A multinational corporation is designing an AIOps architecture to support 50+ business applications across 6 geographic regions with varying data residency requirements. GDPR requires EU customer data remain in-region, while global correlation is needed for shared infrastructure services. The architecture must support both regional autonomy and cross-regional incident detection. Which architectural pattern BEST satisfies these requirements?

An AIOps automation runbook for database failover has a 15% failure rate in production despite 100% success in testing. Investigation shows failures occur when the runbook executes during active backup operations or when connection pools are saturated. The runbook uses fixed 30-second wait times between steps. How should the automation be redesigned for production resilience?

A company's AIOps incident management system shows an MTTA (Mean Time to Acknowledge) of 45 seconds but an MTTR (Mean Time to Resolve) of 4.5 hours, despite 60% of incidents being classified as known issues with existing runbooks. Analysis reveals that while incidents are quickly acknowledged, the correct runbook is identified and executed only 40% of the time initially. What is the PRIMARY issue and solution?

An enterprise AIOps deployment uses machine learning for anomaly detection across application logs. After a major application update that introduced new microservices and changed logging formats, the false positive rate increased from 5% to 45%, and two critical incidents were missed entirely. The ML models were last trained 6 months ago. What is the MOST comprehensive approach to restore and improve detection accuracy?

A retail company's AIOps platform must handle 10x normal event volume during Black Friday sales while maintaining sub-3-second correlation times. Their current architecture uses a monolithic correlation engine that processes events sequentially. During load testing at 5x volume, correlation time degraded to 18 seconds and memory usage reached 92%. What architectural transformation is MOST appropriate?

An AIOps implementation has automated remediation for 30 common incident types. A recent incident caused a cascade failure when the automated remediation for high memory usage (restart service) conflicted with another automation handling database connection pool exhaustion (scale up connections), creating an infinite loop of restarts. Both automations fired simultaneously for 12 minutes before manual intervention. How should the automation framework be redesigned to prevent this class of failure?