C1000-130 Exam Dumps : IBM Cloud Pak for Integration V2021.2 Administration

In IBM Cloud Pak for Integration (CP4I), IBM MQ Native High Availability (NativeHA) is a feature that enables automated failover and redundancy by maintaining multiple replicas of an MQ queue manager.

When using NativeHA, licensing in CP4I is calculated based on the total number of VPCs (Virtual Processor Cores) consumed by all MQ instances, including both active and standby replicas.

IBM MQ NativeHA uses a multi-replica setup, meaning there are multiple queue manager instances running simultaneously for redundancy.

Licensing in CP4I is based on the total CPU consumption of all running MQ replicas, not just the active instance.

Therefore, the administrator must ensure that all HA replicas are accounted for in their license entitlement.

Why Option B is Correct:

A. No licenses, because only RDQM is supported on CP4I. (Incorrect)

IBM MQ NativeHA is fully supported on CP4I alongside RDQM (Replicated Data Queue Manager). NativeHA is actually preferred over RDQM in containerized OpenShift environments.

C. A different license from the standard CP4I license must be purchased from IBM to use the NativeHA feature. (Incorrect)

No separate license is required for NativeHA – it is covered under the CP4I licensing model.

D. The administrator can use their pool of CP4I licenses. (Incorrect)

Partially correct but incomplete – while the administrator can use their CP4I licenses, they must ensure that all HA replicas are included in the license calculation, not just the active instance.

Analysis of the Incorrect Options:

IBM MQ Native High Availability Licensing

IBM Cloud Pak for Integration Licensing Guide

IBM MQ on CP4I - Capacity Planning and Licensing

IBM Cloud Pak for Integration (CP4I) v2021.2 Administration References:

In IBM Cloud Pak for Integration (CP4I) v2021.2, profiles define the resource allocation and configuration settings for deployed services. When creating a second medium-size profile, the system will allocate the resources according to the medium-size specifications, without affecting the first profile.

IBM Cloud Pak for Integration supports multiple profiles, each with its own resource allocation.

When a second medium-size profile is created, it is independently assigned the medium-size configuration without modifying the existing profiles.

This allows multiple services to run with similar resource constraints but remain separately managed.

Why Option B is Correct:

A. The first profile will be replaced by the second profile. → ❌ Incorrect

Creating a new profile does not replace an existing profile; each profile is independent.

C. The first profile will be re-configured with a medium size. → ❌ Incorrect

The first profile remains unchanged. A second profile does not modify or reconfigure an existing one.

D. The second profile will be configured with a large size. → ❌ Incorrect

The second profile will retain the specified medium size and will not be automatically upgraded to a large size.

Explanation of Incorrect Answers:

IBM Cloud Pak for Integration Sizing and Profiles

Managing Profiles in IBM Cloud Pak for Integration

OpenShift Resource Allocation for CP4I

IBM Cloud Pak for Integration (CP4I) v2021.2 Administration References:

In IBM Cloud Pak for Integration (CP4I) v2021.2, which runs on Red Hat OpenShift, logging is a critical component for monitoring cluster and application activities. To enable OpenShift Logging, two key operators must be installed:

OpenShift Logging Operator (B)

This operator is responsible for managing the logging stack in OpenShift.

It helps configure and deploy logging components like Fluentd, Kibana, and Elasticsearch within the OpenShift cluster.

It provides a unified way to collect and visualize logs across different workloads.

OpenShift Elasticsearch Operator (E)

This operator manages the Elasticsearch cluster, which is the central data store for log aggregation in OpenShift.

Elasticsearch stores logs collected from cluster nodes and applications, making them searchable and analyzable via Kibana.

Without this operator, OpenShift Logging cannot function, as it depends on Elasticsearch for log storage.

A. OpenShift Console Operator → Incorrect

The OpenShift Console Operator manages the web UI of OpenShift but has no role in logging.

It does not collect, store, or manage logs.

C. OpenShift Log Collector → Incorrect

There is no official OpenShift component or operator named "OpenShift Log Collector."

Log collection is handled by Fluentd, which is managed by the OpenShift Logging Operator.

D. OpenShift Centralized Logging Operator → Incorrect

This is not a valid OpenShift operator.

The correct operator for centralized logging is OpenShift Logging Operator.

Explanation of Incorrect Answers:

OpenShift Logging Overview

OpenShift Logging Operator Documentation

OpenShift Elasticsearch Operator Documentation

IBM Cloud Pak for Integration Logging Configuration

IBM Cloud Pak for Integration (CP4I) v2021.2 Administration References: