C1000-188
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C1000 188 Cloud Pak AIOps
The IBM Certified Administrator – Cloud Pak for AIOps v4.6 Professional course teaches administrators how to design, integrate, and operate AIOps solutions on OpenShift, covering event, incident, log analysis, and tool integration.
90
Minutes
62
Questions
62/100
Passing Score
$200
Exam Cost
Who Should Take This
It is intended for IT professionals such as system administrators, DevOps engineers, and solution architects who already manage OpenShift environments and seek to advance their expertise in AI‑driven operations. Learners should have at least two years of experience with IBM Cloud Pak and aim to lead AIOps strategy and implementation.
What's Covered
1
Domain 1: Cloud Pak for AIOps Architecture and OpenShift Integration
2
Domain 2: Event Management and Correlation
3
Domain 3: Incident Management and Topology Analysis
4
Domain 4: Log Analysis and AI-Driven Anomaly Detection
5
Domain 5: Integration with Monitoring Tools and ITSM
6
Domain 6: Administration, User Management, and Performance Tuning
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
20 Activity Formats
Course Outline
87 learning goals
1
Domain 1: Cloud Pak for AIOps Architecture and OpenShift Integration
3 topics
OpenShift Foundation and Deployment
- Deploy Cloud Pak for AIOps v4.6 on OpenShift 4.12+ clusters with proper resource allocation and namespace configuration
- Configure OpenShift storage classes and persistent volumes for Cloud Pak for AIOps components including Cassandra, Elasticsearch, and Kafka
- Analyze OpenShift cluster performance metrics and resource utilization patterns for optimal Cloud Pak for AIOps performance
- Implement security policies and RBAC configurations for Cloud Pak for AIOps operators and custom resources
- Evaluate cluster scaling strategies and node affinity rules for high availability Cloud Pak for AIOps deployments
AI Manager Architecture Components
- Configure AI Manager components including event processing engine, topology manager, and correlation services
- Implement data ingestion pipelines for metrics, logs, and events into AI Manager using Kafka and REST APIs
- Analyze AI Manager component dependencies and service mesh communication patterns for troubleshooting
- Design scalable AI Manager architectures with distributed processing capabilities and failover mechanisms
- Optimize AI Manager performance through component tuning, memory allocation, and processing thread configuration
Infrastructure Integration and Networking
- Configure network policies and ingress controllers for Cloud Pak for AIOps external connectivity
- Implement SSL/TLS certificates and secure communication channels between Cloud Pak for AIOps components
- Analyze network latency and bandwidth requirements for distributed Cloud Pak for AIOps deployments
- Design disaster recovery strategies with backup and restore procedures for Cloud Pak for AIOps data and configurations
2
Domain 2: Event Management and Correlation
3 topics
Event Processing and Noise Reduction
- Configure event ingestion from multiple sources including Netcool/OMNIbus, Splunk, and webhook integrations
- Implement noise reduction policies using temporal grouping, deduplication rules, and severity-based filtering
- Apply machine learning models for event correlation including seasonal training and pattern recognition algorithms
- Analyze event flow patterns and correlation accuracy metrics to optimize noise reduction effectiveness
- Evaluate event management strategies for different operational scenarios including maintenance windows and incident responses
Correlation Engine Configuration
- Configure correlation policies with temporal and spatial grouping rules for related event identification
- Implement custom correlation rules using CEA (Common Event Architecture) and policy-based engines
- Apply probabilistic correlation algorithms and confidence scoring for event relationship determination
- Analyze correlation engine performance metrics including processing latency and accuracy rates
- Design correlation strategies that balance processing speed with accuracy for high-volume environments
Event Enrichment and Classification
- Implement event enrichment using topology data, CMDB lookups, and external data source integration
- Configure event classification models using natural language processing and supervised learning techniques
- Analyze event classification accuracy and adjust training datasets for improved model performance
- Optimize event enrichment pipelines for minimal latency while maximizing contextual information
3
Domain 3: Incident Management and Topology Analysis
3 topics
Incident Lifecycle Management
- Configure incident creation policies with automated severity assignment and escalation rules
- Implement incident enrichment using topology impact analysis and change correlation data
- Apply incident grouping algorithms based on temporal proximity, affected resources, and symptom similarity
- Analyze incident patterns and trends to identify recurring issues and improvement opportunities
- Design incident management workflows that integrate with ITSM tools and organizational processes
Topology Discovery and Management
- Configure topology data collection from network discovery tools, APM solutions, and CMDB systems
- Implement topology observers for dynamic infrastructure including Kubernetes, VMware, and cloud platforms
- Apply topology relationship modeling with dependency mapping and service hierarchy definitions
- Analyze topology data quality and completeness to ensure accurate impact assessment capabilities
- Evaluate topology management strategies for hybrid and multi-cloud environments
Change Risk Assessment
- Configure change risk models using historical incident data and change success rates
- Implement change detection from ServiceNow, Jenkins, and other change management systems
- Apply risk scoring algorithms that consider change timing, scope, and historical impact patterns
- Analyze change-incident correlation patterns to improve risk assessment model accuracy
- Design change risk strategies that balance operational agility with stability requirements
4
Domain 4: Log Analysis and AI-Driven Anomaly Detection
3 topics
Log Ingestion and Processing
- Configure log data ingestion from Elasticsearch, Splunk, Fluentd, and syslog sources
- Implement log parsing rules with regex patterns and structured data extraction for unstructured logs
- Apply log data normalization and timestamp standardization across multiple log sources
- Analyze log ingestion performance and implement backpressure handling for high-volume environments
- Optimize log processing pipelines with parallel processing and data partitioning strategies
AI-Powered Anomaly Detection
- Configure anomaly detection models using unsupervised learning algorithms and baseline establishment
- Implement log anomaly detection with natural language processing and pattern recognition techniques
- Apply seasonal and trend analysis for time-series log data anomaly detection
- Analyze false positive rates and tune anomaly detection sensitivity for optimal alert quality
- Evaluate anomaly detection strategies for different application types and log patterns
Log Analytics and Insights
- Implement log clustering and grouping algorithms to identify similar error patterns
- Configure log-based metric generation and KPI calculation for operational dashboards
- Apply root cause analysis techniques using log correlation and timeline reconstruction
- Analyze log analytics performance and optimize query execution for large datasets
5
Domain 5: Integration with Monitoring Tools and ITSM
3 topics
Monitoring Tool Integration
- Configure integrations with Prometheus, Grafana, New Relic, and Dynatrace for metrics collection
- Implement webhook and REST API connections for bidirectional data exchange with monitoring systems
- Apply data transformation and mapping rules for metrics normalization across different monitoring tools
- Analyze integration performance and implement retry mechanisms for reliable data collection
- Design monitoring integration architectures that support multiple tool ecosystems and data formats
ITSM Platform Integration
- Configure ServiceNow integration for incident creation, updates, and closure synchronization
- Implement BMC Remedy, Jira Service Management, and other ITSM tool integrations
- Apply field mapping and data transformation rules for consistent ITSM record management
- Analyze ITSM integration workflows and optimize for reduced manual intervention
- Evaluate ITSM integration strategies that align with organizational service management processes
API Management and Automation
- Implement custom API endpoints and automation scripts for Cloud Pak for AIOps operations
- Configure authentication and authorization mechanisms for API access including OAuth and JWT tokens
- Apply API rate limiting, throttling, and security policies for external integration protection
- Analyze API usage patterns and performance metrics for capacity planning and optimization
- Design API governance strategies that ensure security, reliability, and maintainability
6
Domain 6: Administration, User Management, and Performance Tuning
3 topics
User Management and Security
- Configure LDAP, SAML, and OpenID Connect authentication for Cloud Pak for AIOps access
- Implement role-based access control with custom roles and permission assignments for different user types
- Apply security policies including password complexity, session timeout, and multi-factor authentication
- Analyze user access patterns and implement principle of least privilege for security optimization
- Design identity management strategies that integrate with enterprise directory services and compliance requirements
System Administration and Maintenance
- Configure backup and restore procedures for Cloud Pak for AIOps databases and configuration data
- Implement log rotation, data retention policies, and cleanup procedures for storage management
- Apply system health monitoring and alerting for Cloud Pak for AIOps component availability
- Analyze system resource utilization and implement preventive maintenance schedules
- Evaluate maintenance strategies that minimize downtime while ensuring system reliability
Performance Tuning and Optimization
- Configure JVM heap sizes, garbage collection settings, and thread pool parameters for optimal performance
- Implement database tuning for Cassandra, Elasticsearch, and PostgreSQL components
- Apply caching strategies and query optimization techniques for improved response times
- Analyze performance bottlenecks using profiling tools and implement targeted optimizations
- Design performance optimization strategies that balance throughput, latency, and resource consumption
Scope
Included Topics
- All domains of C1000-188 IBM Certified Administrator - Cloud Pak for AIOps v4.6 Professional: Cloud Pak for AIOps v4.6: architecture on OpenShift, AI manager components; event management, correlation, noise reduction; incident management, topology, change risk; log analysis, anomaly detection .
- Exam-specific technical content covering with AI; integration with monitoring tools, ITSM; administration, user management, performance tuning..
Not Covered
- Topics outside the C1000-188 exam scope and other certification levels.
- Current pricing, promotional offers, and vendor-specific values that change over time.
- Implementation details for competing vendor products and platforms.
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