Question 1

What outcomes can be achieved from Match 360?

Accepted Answer

D

Explanation: IBM Match 360 is a master data management (MDM) service within IBM Cloud Pak for Data. Its primary function is to consolidate data from disparate sources to create a single, trusted 360-degree view of master data entities (e.g., customers, products). A key outcome of this process is the ability for data stewards and analysts to use the Master Data Explorer interface. This interface provides tools to search, view, and analyze the consolidated master data, including statistics about the data entities and graphical representations of their relationships, which allows users to explore and understand the master data.

Question 2

How do Cloud Pak for Data administrators obtain access to Match 360?

Accepted Answer

D

Explanation: In IBM Cloud Pak for Data, platform-level administrative privileges do not automatically grant access to the functionalities within specific service instances like IBM Match 360. Access control for Match 360 is managed through service-specific user groups (Data Engineer, Data Steward, Publisher User, and Entity Viewer). To obtain access, a Cloud Pak for Data administrator, like any other user, must be explicitly added to one of these predefined user groups associated with the Match 360 service instance. This adheres to the principle of least privilege, ensuring that access to sensitive master data is strictly controlled and audited.

Question 3

Which statement is true about Cloud Pak for Data SIEM integration?

Accepted Answer

B

Explanation: The IBM Cloud Pak for Data Audit Logging Service is built upon the Fluentd data collector. It utilizes Fluentd's capabilities to gather audit records generated by various Cloud Pak for Data services. For external integration, it uses Fluentd's output plugins to forward these records to Security Information and Event Management (SIEM) systems. This architecture allows for flexible configuration to route audit data to one or more external monitoring solutions like QRadar or Splunk.

Question 4

What is the purpose of the IBM Data Replication service?

Accepted Answer

C

Explanation: The primary purpose of the IBM Data Replication service, particularly within IBM Cloud Pak for Data, is to capture data changes from a source system and apply them to a target system in near real-time. This continuous process ensures data is synchronized across different locations or platforms. This synchronization is a key method of data integration, supporting use cases such as high availability, disaster recovery, data migration, and offloading reporting workloads from production systems.

Question 5

What is a benefit of utilizing the IBM Data Virtualization service?

Accepted Answer

D

Explanation: IBM Data Virtualization provides a single, unified query interface across multiple, heterogeneous data sources without physically moving the data. A primary benefit of this approach is the ability to establish a centralized point for data access and governance. Administrators can define and enforce security policies, access controls, and data masking rules on virtual tables within the Data Virtualization service, ensuring consistent governance across all underlying data sources from a single location. This simplifies security management and ensures compliance.

Question 6

What capability does the Watson OpenScale API provide?

Accepted Answer

D

Explanation: The Watson OpenScale API is designed for AI model governance and monitoring. Its primary capability is to track and measure the outcomes of AI models in production. This includes evaluating model quality (e.g., accuracy), detecting data and accuracy drift over time, and ensuring fairness by identifying and mitigating biases in model predictions against protected attributes like age or gender. It also provides explainability for individual transactions, helping stakeholders understand why a model made a specific decision. These functions are crucial for building trust and transparency in AI systems.

Question 7

Which component must be enabled in order to render business lineage when installing IBM Knowledge Catalog?

Accepted Answer

B

Explanation: Business lineage in IBM Knowledge Catalog provides a graphical representation of the relationships between various assets, such as business terms, data assets, reports, and governance artifacts. This complex network of relationships and the flow of data are stored, managed, and rendered using the underlying knowledge graph technology. The knowledge graph serves as the foundational repository for the metadata and relationships that are visualized in the business lineage report. Therefore, this component must be enabled during installation to support the business lineage feature.

Question 8

What is the purpose of profiling data in Data Refinery?

Accepted Answer

A

Explanation: The primary purpose of profiling data in IBM Data Refinery is to analyze and understand its content and quality. Profiling automatically generates statistics and frequency distribution charts for each column of the dataset. This allows data professionals to quickly identify patterns, outliers, missing values, and other data quality issues. By examining these profiles, users can validate the data's integrity and determine the necessary cleansing and shaping operations required before using it for analysis or machine learning.

Question 9

Which Watson Pipeline component manages pipeline errors, typically used with DataStage?

Accepted Answer

C

Explanation: In IBM Watson Pipelines, the Error Handling feature is the designated component for managing failures within a pipeline node. When configuring a node, such as "Run DataStage job," the "Error handling" tab allows a user to define the pipeline's behavior upon encountering an error. This includes options like terminating the entire pipeline immediately, continuing the run despite the error, or proceeding down a specific conditional path. This mechanism provides robust control over the pipeline's execution flow in case of job failures.

Question 10

What endpoint will an application use to interact with Db2 Big SQL?

Accepted Answer

A

Explanation: Db2 Big SQL on IBM Cloud Pak for Data provides a REST (Representational State Transfer) API endpoint for application interaction. This allows developers to programmatically submit SQL statements, monitor the status of their execution, and retrieve the results using standard HTTP methods. This RESTful interface is a common method for integrating modern applications with data services, offering a stateless and scalable way to interact with the Db2 Big SQL engine without requiring traditional database drivers like JDBC or ODBC.

Question 11

What are two ways to customize Knowledge Accelerators to meet specific requirements?

Accepted Answer

B, D

Explanation: IBM recommends two primary strategies for customizing Knowledge Accelerators to prevent custom modifications from being overwritten by future updates. The first approach involves creating a separate, parallel vocabulary (often called an "enterprise vocabulary") within Watson Knowledge Catalog. This keeps the original Knowledge Accelerator content pristine in its own categories while custom terms are developed separately and linked where necessary. The second approach involves using a Cloud Pak for Data project as a staging area. In this method, the Knowledge Accelerator CSV files are modified or merged with custom content within the isolated project environment before being imported and published to the catalog.

Question 12

Which Watson Assistant service captures telephone voice interactions for use by a cognitive agent or as a transcription for analytics processing?

Accepted Answer

D

Explanation: IBM Voice Gateway is the service that connects a public or private telephone network, using the Session Initiation Protocol (SIP), to IBM Watson services. It orchestrates the IBM Watson Speech to Text service to transcribe the caller's speech and the Text to Speech service to synthesize the agent's responses. This enables a direct, real-time voice conversation with a cognitive agent like Watson Assistant. The resulting transcriptions can also be stored and used for analytics processing, fulfilling all requirements mentioned in the question.

Question 13

How are Knowledge Accelerators deployed?

Accepted Answer

B

Explanation: IBM Knowledge Accelerators are deployed (installed) from the Cloud Pak for Data marketplace. This is the designated central location for users to discover, access, and add industry-specific accelerators to their Cloud Pak for Data environment. Once an accelerator is added from the marketplace, its content, such as business terms and data classes, can then be imported into Watson Knowledge Catalog, typically via an API call, to populate the governance catalog. The initial deployment step, however, is acquisition from the marketplace.

Question 14

Which type of OpenShift route configuration supports client certificate authentication?

Accepted Answer

C

Explanation: The Passthrough route configuration is the only type that supports client certificate authentication handled by the end application. In this mode, the OpenShift router does not terminate the TLS connection. Instead, it passes the encrypted TCP traffic directly to the destination pod. This allows the application within the pod to manage the entire TLS handshake, which includes the process of requesting, receiving, and validating a client's certificate for mutual TLS (mTLS) authentication. The other route types terminate the TLS session at the router, preventing the backend application from participating in the client-side certificate exchange.

Question 15

Which Cloud Pak for Data service is used to cleanse and shape tabular data?

Accepted Answer

C

Explanation: Data Refinery is a self-service data preparation client within IBM Cloud Pak for Data designed for data scientists, data engineers, and business analysts. It provides a graphical interface and a rich set of operations to discover, cleanse, and transform raw, tabular data into a consumable, high-quality format ready for analysis or machine learning model training. Users can build interactive data preparation flows that can be scheduled and run on large data sets.

Free IBM C1000-173 Actual Exam Questions