Free Practice Questions for the IBM SWG Technical Mastery P2090-038 Exam (2026 Updated)

The InfoSphere BigInsights credentials store is a table stored in the HBase relational database that stores passwords, tokens, and other potentially sensitive information.

The InfoSphere BigInsights credentials store is a designated folder on the distributed file system (DFS) that stores passwords, tokens, and other potentially sensitive information.

The InfoSphere BigInsights credentials store is a designated folder in the local file system (not HDFS) that stores the authorities and privileges for all users in the BigInsights environment.

The InfoSphere BigInsights credentials store is a designated file defined by an environment variable that stores the authorities and privileges for all users in the BigInsights environment.

There is no direct interaction between InfoSphere BigInsighs and data warehousing environments. However, InfoSphere Streams can be used to stream data between the two environments.

Frequently accessed data should be maintained in the BigInsights environment while old?or outdated information can be offloaded to the Frequently accessed data should be maintained in the BigInsights environment while ?old?or outdated information can be offloaded to the warehouse environment.

InfoSphere BigInsights Enterprise Edition also comes with a standard Java Database Connectivity (JDBC) connector, making it possible for organizations to quickly integrate with a wide variety of data and information systems, including Oracle, Microsoft SQL Server, MySQL and Teradata.

There is no integration between InfoSphere BigInsights and warehousing environments since they deal with different type of data.

InfoSphere Biglnsights Express Edition.

InfoSphere Biglnsights Enterprise Edition.

InfoSphere Biglnsights Extended Developer Edition.

InfoSphere Biglnsights Advanced Enterprise Edition.

Traditional data warehousing environments are mostly ideal for analyzing structured data from various systems, while a Hadoop environment is well suited to deal with structured, semi-structured, and unstructured data, as well as when a data discovery process is needed.

Hadoop environments are mostly ideal for analyzing structured and semi-structured data from a single system, while traditional data warehousing environment is well suited to deal with unstructured data, as well as when a data discovery process is needed.

Typically, data stored in Hadoop environments is cleaned up before storing in the distributed file-system.

Typically, data stored in data warehousing environments is rarely filtered and pre-processed. On the other hand, data injected into Hadoop environments is always pre-processed and filtered.

Big data solutions are ideal for analyzing not only raw structured data, but semi structured and unstructured data from a wide variety ofsources.

Big data solutions are ideal when all, or most, of the data needs to be analyzed versus a sample of the data; or a sampling of data isn nearlyBig data solutions are ideal when all, or most, of the data needs to be analyzed versus a sample of the data; or a sampling of data isn? nearlyas effective as a larger set of data from which to derive analysis.

Big data solutions are ideal for Online Transaction Processing (OLTP) environments.

Big data solutions are ideal for iterative and exploratory analysis when business measures on data are not predetermined.

Adaptive MapReduce

BigSheets

Text Analytics

BigIndex.