An enterprise architect in a financial institute is deciding on the deployment option for Cloud Pak for
Data on their existing OpenShift Container Platform cluster.
They have decided to use an automated deployment option and install Cloud Pak for Data from the
cloud provider's marketplace. What are the limitations they may face with this decision?
D
Explanation:
According to the IBM Cloud Pak for Data 4.7 Installation Guide and official IBM documentation, when
deploying Cloud Pak for Data (CP4D) via a cloud provider marketplace (such as Red Hat OpenShift
OperatorHub or a cloud marketplace), the deployment process offers an automated installation
method that simplifies the setup. However, there are certain limitations and manual steps that may
be required during the initial installation phase.
CP4D supports installation on an existing OpenShift Container Platform cluster, so option A is
incorrect.
Some core services and operators, including foundational services, can be installed automatically via
operators, so option B is incorrect.
Cloud Pak for Data operators and IBM foundational services operators can coexist in the same
OpenShift cluster, so option C is incorrect.
The official installation documentation for version 4.7 specifies that the initial installation requires
manual intervention for partial installation — for example, manually setting up foundational services
or configuring specific operators before automated installation of the rest of the platform can
continue smoothly. This partial manual setup is especially relevant when using marketplace
deployment to ensure all prerequisites and configurations are met.
Exact extract from IBM Cloud Pak for Data 4.7 Installation documentation:
"When deploying Cloud Pak for Data via the OperatorHub or cloud marketplace, the initial setup
requires manual installation of foundational services operators and configuration of the cluster
environment before proceeding with the automated installation of Cloud Pak for Data services. This
partial manual step ensures proper configuration and avoids conflicts during automated
deployment."
— IBM Cloud Pak for Data Installation Guide v4.7, section "Installing from OperatorHub and
Marketplace"
Reference:
IBM Cloud Pak for Data 4.7 Installation Guide (https://www.ibm.com/docs/en/cloud-paks/cp-
data/4.7?topic=deployment-installing-from-operatorhub-marketplace)
IBM Knowledge Center for Cloud Pak for Data 4.7
An architect is working with a team to configure Dynamic Workload Management for a single
DataStage instance on Cloud Pak for Data.
Auto-scaling has been disabled and the maximum concurrent jobs has been set to 5.
What will happen if a sixth concurrent job is executed?
B
Explanation:
In IBM Cloud Pak for Data version 4.7, when configuring Dynamic Workload Management (DWM) for
IBM DataStage, the system controls job concurrency based on the maximum concurrent jobs setting
and auto-scaling configuration.
With auto-scaling disabled, the system does not add or remove DataStage engine pods dynamically
to handle workload changes.
The maximum concurrent jobs setting limits the number of jobs that can run simultaneously on a
single DataStage instance.
If the number of concurrent jobs reaches the maximum limit (in this case, 5), any additional job
requests (such as the sixth job) will not fail immediately; instead, these jobs are placed in a queue.
The queued jobs remain pending until one of the running jobs completes, freeing up capacity for the
next job to start.
This queuing behavior ensures workload stability and prevents resource exhaustion by enforcing the
concurrency limit strictly when auto-scaling is turned off.
Exact extract from IBM Cloud Pak for Data 4.7 documentation:
"When auto-scaling is disabled, the maximum concurrency limit set on the DataStage instance
controls how many jobs can run simultaneously. Jobs submitted beyond this limit are queued and
wait for running jobs to complete before starting execution."
— IBM Cloud Pak for Data v4.7, DataStage Dynamic Workload Management section
Reference:
IBM Cloud Pak for Data 4.7 Documentation — DataStage and Dynamic Workload Management
IBM Knowledge Center for Cloud Pak for Data v4.7: https://www.ibm.com/docs/en/cloud-paks/cp-
data/4.7?topic=management-dynamic-workload
How are Knowledge Accelerators deployed?
D
Explanation:
Knowledge Accelerators are a part of IBM Knowledge Catalog in IBM Cloud Pak for Data and provide
predefined industry-specific business terms and relationships. These accelerators are not included by
default with sample assets nor automatically deployed through the Cloud Pak for Data marketplace.
Instead, they are imported using dedicated API endpoints provided by IBM for Knowledge
Accelerators. Deployment involves uploading the accelerator assets using the IBM Knowledge
Accelerator API, and once imported, they can be customized and published within the governance
framework. This approach provides the flexibility required for various enterprise governance models.
Which Watson Pipeline component manages pipeline errors, typically used with DataStage?
C
Explanation:
In Watson Pipelines within IBM Cloud Pak for Data, error management is handled by the Error
Handling component. This feature allows developers and pipeline administrators to define how
pipeline failures are processed—whether to stop execution, continue, or trigger alternate flows. It
ensures controlled behavior in response to job failures, particularly in complex ETL pipelines like
those built with DataStage. Error Handling is a configurable element of pipeline orchestration and is
typically used to enhance fault tolerance and control error propagation in production workflows.
Which Db2 Big SQL component uses system resources efficiently to maximize throughput and
minimize response time?
D
Explanation:
StreamThrough is a high-performance component used in Db2 Big SQL within IBM Cloud Pak for Data
that is optimized to manage data streams and queries efficiently. It is designed to maximize
throughput and minimize query response times by optimizing memory usage, resource allocation,
and processing logic. Unlike Hive or Analyzer, which are used for query execution and analysis,
StreamThrough enables efficient pipeline execution by streamlining data handling. Scheduler is used
for job timing but does not influence runtime efficiency directly. StreamThrough is purpose-built to
enhance performance through optimal resource usage.
What must be created to enable the Cloud Pak for Data platform to use a company’s custom CA
certificate to validate certificates from internal servers?
D
Explanation:
To enable IBM Cloud Pak for Data to trust certificates from internal servers using a custom Certificate
Authority (CA), the correct method is to create a Kubernetes ConfigMap that contains the CA
certificate. This ConfigMap is referenced by the platform’s foundational services to include the CA in
the trusted root store. Secrets are typically used for storing sensitive data like private keys and TLS
certificates but are not used for adding trusted root CAs at the platform level. A ConfigMap is
explicitly required by the platform to inject the CA trust into the certificate validation chain.
Which statement is true about governing data lakes in IBM Knowledge Catalog?
B
Explanation:
Within IBM Knowledge Catalog as part of IBM Cloud Pak for Data, governing data lakes is enabled via
integration with Data Virtualization. This approach supports automated data discovery, cataloging,
tagging, and virtualization, allowing users to access enterprise data virtually—without physical
movement. Policies and governance metadata are applied automatically to virtualized assets,
enabling secure and efficient data consumption. Manual processes are not required for discovery,
and data is masked selectively based on policies—not completely masked without user intervention.
Thus automation and virtualization are central, making statement B correct.
Which two of the following can be used with Watson Pipelines?
B, D
Explanation:
Watson Pipelines in Cloud Pak for Data support orchestration of diverse workload types including
notebooks (Python or similar interactive environments) and scripts such as Bash. These pipeline
components allow integration of notebook cells or shell scripts as tasks. There is no built-in support
for executing PowerShell tasks directly (unless wrapped in Bash-like containers), and Postgres is used
as a data source—not a pipeline component type. While Db2 Big SQL can be invoked within a
notebook or script, it is not itself a pipeline component. Therefore the supported types in pipelines
are notebooks and Bash scripts.
Which two Cloud Pak for Data services support the multi-tenancy mechanism of installing the service
once and provisioning the workloads in tethered projects?
C, D
Explanation:
Cloud Pak for Data supports service-level multi-tenancy by enabling some services to be installed
centrally and then provisioned into user “tethered” namespaces (projects). In version 4.7, Analytics
Engine (Apache Spark) and IBM Data Virtualization services support this tethered-project model:
they can be installed once and then instantiated per project across multiple tenants. Core services
like Db2, Cognos Analytics, and Watson Pipelines in 4.7 require installation per instance and do not
yet support tethered-namespace provisioning via multi-tenant model.
Which two Cloud Pak for Data services support storage class NFS?
B, D
Explanation:
IBM Cloud Pak for Data supports NFS-backed persistent volumes (RWX storage class) for services that
require access to shared file storage. Among the available services, IBM Knowledge Catalog and
Watson Discovery are known to support NFS storage classes in installation configuration (e.g.
“managed-nfs-storage”) for persistent metadata and document storage. Other services like watsonx
Assistant, Watson Knowledge Studio, and Planning Analytics use different storage mechanisms and
do not necessarily support NFS shared storage in the standard CP4D 4.7 deployment.
How do Cloud Pak for Data administrators obtain access to Match 360?
D
Explanation:
Access to Match 360 within IBM Cloud Pak for Data is role-based and governed by service groups.
Even administrative users are not granted automatic access unless they are explicitly assigned to the
appropriate Match 360 service group. This allows fine-grained control over who can access master
data management capabilities. Service group membership defines the roles and privileges needed
for interacting with Match 360 functionalities like entity resolution and golden record management.
What endpoint will an application use to interact with Db2 Big SQL?
A
Explanation:
Applications interact with Db2 Big SQL using industry-standard protocols. The most common and
supported interface is through a REST (Representative State Transfer) API endpoint. REST endpoints
allow for external applications to query, manage, and manipulate data within Big SQL using simple
HTTP calls. None of the other options—SLEEP, SNORE, or DREAM—are valid or recognized interfaces
in IBM Cloud Pak for Data or Db2 Big SQL documentation.
How many service instances can be provisioned for Watson Discovery at one time?
D
Explanation:
"You can create a maximum of 10 instances per deployment. After you reach the maximum number,
the New instance button is not displayed in IBM Cloud Pak for Data."
Which statement describes MPP (Massively Parallel Processing) Database architecture?
C
Explanation:
MPP, or Massively Parallel Processing, is a database architecture model where data is divided and
processed across multiple compute nodes in parallel. Each node works independently on a portion of
the data, dramatically improving query performance and throughput for analytics workloads. This
model is ideal for big data and analytical queries, not transactional workloads. It differs from shared-
disk models or replication strategies like two-phase commit. The correct definition involves
distributed data and parallel query execution, as described in option C.
What does Watson OpenScale require to generate statistics?
C
Explanation:
Training Data Statistics: Watson OpenScale needs to understand the characteristics of the data the
model was trained on. This includes things like the distribution of features, sensitive attributes (for
fairness monitoring), and how the model performed on this initial data. These "training data
statistics" are crucial for:
Fairness Configuration: Recommending fairness attributes, reference, and monitored groups.
Bias Detection: Calculating fairness metrics (like disparate impact) by comparing runtime behavior to
the learned training data distribution.
Explainability: Generating explanations by understanding the distribution of values in the training
data to create meaningful perturbations.
Drift Detection: Building a drift detection model that compares runtime data to the training data to
identify shifts.
While Watson OpenScale also consumes payload data (the data sent to the deployed model for
predictions) at runtime to calculate various metrics and perform monitoring, the initial setup and the
ability to generate meaningful statistics for things like fairness and drift fundamentally rely on
understanding the training data