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
What is the default schedule for the diagnostics monitor when using the Alerting APIs?
D
Explanation:
You can use the IBM Software Hub monitoring and alerting framework to monitor the state of the
platform. You can set up events to alert when action is needed, based on thresholds that you define.
By default, IBM Software Hub is initialized with one monitor that runs every ten minutes. The
diagnostic monitor records the status of deployments, StatefulSets, and persistent volume claims. It
also tracks your system usage of virtual processors (vCPUs) and memory. The data that is collected
can be used for analysis and to alert customers in a production environment based on set alert rules.
Insurance industry datasets frequently include personally identifiable information (PII) and many
data analysts need access to datasets but not to PII.
Which Cloud Pak for Data services leverage Data Protection Rules?
C
Explanation:
IBM Cloud Pak for Data includes built-in Data Protection Rules to enforce access control on sensitive
data, such as PII. These rules are integrated directly into services like IBM Data Virtualization, Data
Privacy, and IBM Knowledge Catalog. When analysts or applications access data through these
services, the platform automatically masks, obfuscates, or restricts access to sensitive fields based on
the defined policies. This ensures compliance with data privacy regulations and organizational
security policies without manual intervention.
What is the role of a "Connection" in the Cloud Pak for Data connectivity framework?
C
Explanation:
In IBM Cloud Pak for Data, a "Connection" is a configuration object that defines how to access an
external data source, such as Db2, Oracle, or an S3 bucket. It does not involve moving or copying
data. Instead, it acts as a reference to the external data source environment, enabling users to
browse, query, and analyze the data in place. This approach supports virtualized access and
governance while avoiding data duplication and ensuring data stays within its source system, aligning
with enterprise data security policies.
What are two ways to customize Knowledge Accelerators to meet specific requirements?
B, D
Explanation:
Customization of Knowledge Accelerators in IBM Cloud Pak for Data is a structured process to
preserve the integrity of base content while allowing for extension. The recommended approaches
include:
Creating a separate project for customizations, so that changes are isolated and easily managed
without affecting the source accelerator.
Using a "development" vocabulary where custom terms and structures are created. This is separate
from the "enterprise vocabulary," which contains the unmodified, original Knowledge Accelerator
content.
Inline editing of the original content is discouraged. Use of GitHub or namespaces is not part of the
official customization workflow.
Which data processing engine is used for Data Privacy Masking flows?
C
Explanation:
Data Privacy Masking flows in IBM Cloud Pak for Data utilize Apache Spark as the underlying data
processing engine. Spark enables large-scale, distributed data masking operations for structured
data, supporting high-performance transformations and compliance with privacy regulations. While
DataStage can perform similar operations, the default and recommended engine for Data Privacy
flows in CP4D is Spark. dbt and Presto are not used for this masking functionality.