IBM Big Data Fundamentals Technical Mastery Test v1 Set 1

InfoSphere Data Explorer provides powerful navigation capabilities across all the important information
stored exclusively into Hadoop Distributed File System in a single view. No other file systems are supported. ✘ InfoSphere Data Explorer is not able to mirror pre-existing security frameworks, therefore it doesn?tmake use of industry-standard authentication and authorization processes already in place. ✘ InfoSphere Data Explorer can find, extract and deliver content regardless of format or where it resides. ✘ InfoSphere Data Explorer uses a vector-based index for unique search and indexing flexibility. ✔

LDAP authentication Secure file transfers through SFTP protocol. Trusted Context. Kerberos authentication protocol.

InfoSphere Streams is a powerful analytic computing platform capable of gathering large quantities of data, manipulating the data, and storing it on disk. ✘ InfoSphere Streams is a powerful analytic computing platform capable of analyzing data in real timewith micro-latency. ✘ InfoSphere Streams is an extract, transform, and load (ETL) platform that is capable of integratingsmall volumes of data across a wide variety of data sources and target applications. ✘ InfoSphere Streams is web administration graphical user interface (GUI) capable of setting up a secure communication channel to stream post-processed data from a Hadoop cluster into a relational database, such as IBM DB2. ✔

Intranet toolkit. ✘ Finance toolkit ✘ Standard toolkit. ✘ Database toolkit. ✔

Hadoop Distributed File System. ✘ Hive. ✘ Pig. ✘ BigSheets. ✔

InfoSphere Streams provides support for reuse of existing Java or C++ code, as well as Predictive Model Markup Language (PMML) models. ✘ InfoSphere Streams supports communications to Internet Protocol version 6 (IPv6) networks. ✘ InfoSphere Streams jobs must be coded using either HiveQL or Jaql languages. ✘ InfoSphere Streams supports both command line and graphical interfaces to administer the Streamsruntime and maintain optimal performance and availability of applications. ✔

Big data solutions must substitute for the existing enterprise infrastructure; therefore there is no
interaction between them. ✘ Big data solutions are placed on top of the existing enterprise infrastructure, acting as a transparentlayer converting unstructured raw data into structured, readable data, and storing the final results in a traditional data warehouse. ✘ Big data solutions must be isolated into a separate virtualized environment optimized for sequential workloads, so that it doesn?t interact with existing infrastructure. ✘ Big data solutions works in parallel with the existing enterprise infrastructure leveraging all the unstructured raw data that cannot be processed
and stored in a traditional data warehouse solutions. ✔

InfoSphere Streams submits queries to structured static data. ✘ InfoSphere Streams submits queries to structured dynamic data. ✘ InfoSphere Streams submits queries to unstructured dynamic data. ✘ InfoSphere Streams submits dynamic data to pre-existing queries. ✔

Big data solutions are ideal for analyzing not only raw structured data, but semi- structured and unstructured data from a wide variety of
sources. ✘ Big data solutions are ideal when all, or most, of the data needs to be analyzed versus a sample of thedata; or a sampling of data isn,t nearly as effective as a larger set of data from which to derive analysis. ✘ Big data solutions are ideal for Online Transaction Analytical Process (OLTP) environments. ✘ Big data solutions are ideal for iterative and exploratory analysis when business measures on data arenot predetermined. ✔

MapReduce jobs are used to process vast amounts of data in-parallel on large clusters of commodity hardware in a reliable, fault-tolerant manner. ✘ MapReduce jobs are used to process small amounts of data in-parallel on expensive hardware, withoutfault-tolerance. ✘ MapReduce jobs are used to process structured data in sequence, with fault-tolerance. ✘ MapReduce jobs are used to execute sequential search outside the Hadoop environment using a built-in UDF to access information stored in non-relational databases. ✔