E20-065 Advanced Analytics Specialist Exam for Data Scientists Questions and Answers

Processing can be broken into smaller pieces

Processing a large number of small files

Processing in real time is required

Processing a small subset of data

C-E

E-F

B-C

G-l

Part-of-speech tagging

Word sense disambiguation

Speech recognition

Discourse

A 32-bit operating system was used

The same number of trials was used

A linear congruential generator (LCG) was used (or pseudo-random number generation

Different seeds tor the random number generator were used.

Edge list

Adjacency matrix

Eigenvector centrality

Adjacency list

A random subset of features that are chosen at each split in the decision tree

Filtration of data that does not meet a pre-defined weighting thrsehold

The creation of out-of-bag (OOB) data that is used to select features

Removal of highly correlated variables to randomize the features

Color

Area

Gradient

Position

Use self-explanatory colors so a legend is unnecessary

Maximize use of color to make a more lasting impression

Use high contrast colors such as red and blue

Minimize use of color except for emphasis

Unable to run map - > reduce execution plans

Supports applications written in Python, Java, and Scala

Less efficient processing small files than Hadoop MapReduce

Supports workflows that can return to previous work steps

Storing files that are updated frequently

Storing files that are written once and read many times

Storing results between Map steps and Reduce steps

Storing application files in memory

Visualization Planning

Data Preparation

Visualization Building

Discovery

Pig and Hive

Hive and HBase

Yarn and Spark

All MapReduce jobs

Used in term frequency analysis

Include words such as " a " , " an " , and " the "

Meaningful words requiring a parser to stop and examine them

Don ' t occur often in text

0

5

10

15

Bipartite

Weighted

Directed acyclic

Ranked

Encryption

Replication

Namenode federation

Snapshots