Test fundamentos Big Data

Question

Big Data dedicated to

Answer 1

Enterprise Apps

Answer 2

Procesing unstructured data

Answer 3

Procesing only unstructured data

Answer 4

combine multiple unreleated datasets

Answer 5

Another DW to enrich data

Answer 6

machine generate

Answer 7

Only human generate

Answer 8

hidden data

Answer 9

Enterprise app

Answer 10

fault and fraud detection

Answer 11

slow procesing

Answer 12

complex, variety, volumen, veracity, value

Answer 13

variety, volumen, velocity, veracity, value

Answer 14

incompatibilities, value,velocity,volumen, veracity, many data

Answer 15

value, variety, veracity

Answer 16

velocity, volumen, value

Answer 17

velocity, variety, volumen

Answer 18

structured data, unstructured data

Answer 19

signal, noise

Answer 20

social network

Answer 21

micro bloggin

Answer 22

sensor data

Answer 23

more value more veracity, more value more time

Answer 24

more value less veracity, more value less time

Answer 25

more value more veracity, more value less time

Answer 26

operational optimization, noise information

Answer 27

scientific discoveries, actionable intelligence

Answer 28

accurate predictions, shipped cloud computing

Answer 29

collections or groups of related data, shares the different set of attributes

Answer 30

collections or groups of related data, shares the same set of attributes

Answer 31

discipline of gaining an understanding of data

Answer 32

discipline of gaining an understanding of data

Answer 33

process of examining data to find facts, relationships, patterns

Answer 34

collections or groups of related data, shares the same set of attributes

Answer 35

process of gaining an understanding of data

Answer 36

discipline of gaining an undestanding of data by analizing it via multitude techniques

Answer 37

collections or groups of related data

Answer 38

results can lower operational costs and facilitate strategic decison-making

Answer 39

help identify the cause of a phenomenon to improve the accuracyof predictions

Answer 40

help strengthen the focus on delivering high quality services by driving down costs

Answer 41

help strengthen the focus on delivering high quality services by driving down costs

Answer 42

results can lower operational costs and facilitate strategic decison-making

Answer 43

help identify the cause of a phenomenon to improve the accuracyof predictions

Answer 44

results can lower operational costs and facilitate strategic decison-making

Answer 45

help strengthen the focus on delivering high quality services by driving down costs

Answer 46

help identify the cause of a phenomenon to improve the accuracyof predictions

Answer 47

process of gaining an understanding of data

Answer 48

process of gaining insigths into the the workings of an enterprise

Answer 49

discipline of gaining an undestanding of data by analizing it via multitude techniques

Answer 50

utilize the consolidated data contained in data warehouses to run analytical queries

Answer 51

is a measure for gauging success within a particular context

Answer 52

discipline of gaining an undestanding of data by analizing it via multitude techniques

Answer 53

achieve regulatory compliance

Answer 54

help identify the cause of a phenomenon to improve the accuracyof predictions

Answer 55

help strengthen the focus on delivering high quality services by driving down costs

Answer 56

kilometer, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, yottabyte

Answer 57

kilobyte, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, yottabyte

Answer 58

kilobyte, megabyte, gigabyte, terabyte, petabyte, exabyte, settabytte, youtube

Answer 59

For many business, digital mediums have replaced physical mediums as the de facto

Answer 60

Based in opens sorce software tha requires little more than commodity hardware

Answer 61

machine learning algoritms, statistical techniques and data warehousing

Answer 62

Lead to an opportunity to collect further secondary data

Answer 63

Collecting and storing more data to potentially find new insigths and gain a competitive edge

Answer 64

collecting and processing large quantities of diverse data has become increasingly affordable

Answer 65

the madurity of these fields of practice inspired and enabled much of the core functionality

Answer 66

use of commodity hardware makes the adoption of Big Data solutions accessible to business

Answer 67

some examples include on-demand TV and streaming video

Answer 68

Has empowered customers to provide feedback in near-realtime via open and public mediums

Answer 69

Has resulted in massive data streams

Answer 70

are capable of providing highly scalable, on-demand IT resources that can be leased

Answer 71

A an result, business are storing increasing amounts of data on customer interaction

Answer 72

leverage the infrastructure, storage and processing capabilities provided by this enviroments

Answer 73

the broadening coverage of internet and the proliferation of cellar and WI-FI networks .

Answer 74

Businesses are also increasingly interested in incorporating publicly available datasets

Answer 75

Is either directly through online interaction or indirectly through the usage of connected devices

Answer 76

Can be leased dramatically reduces the required up-front investment of Big Data projects

Answer 77

software system that processes transaction-oriented data

Answer 78

Is a system used for processing data analysis queries

Answer 79

process of loading data from a source system into a target system

Answer 80

software system that processes transaction-oriented data

Answer 81

Store operational data that is fully normalized

Answer 82

Is a system used for processing data analysis queries

Answer 83

Representing a common source of structured analytics input

Answer 84

Can serve as both a data source as well as a data sink that capable of receiving data

Answer 85

Big data analysis results can also be fed back

Answer 86

Representing a common source of structured analytics input

Answer 87

are used in diagnostic, predictive and prescriptive analytics

Answer 88

An example ticket reservation systems and banking and POS transactions

Answer 89

that store historical data in multidimensional arrays and can answer complex queries

Answer 90

are comprised of simple insert, delete and update operations

Answer 91

that processes transaction-oriented data

Answer 92

fed with data from multiple OLTP system using regular batch processing jobs

Answer 93

data is fully normalized

Answer 94

comprised of simple C R U D

Answer 95

allows the data to be queried during any data analysis tasks that are performes later

Answer 96

Is a process of loading data from a source system into a target system

Answer 97

queries can take several minutes or even longer, depending on the complexity of query.

Answer 98

database, flat file or an application

Answer 99

on-demand TV and streaming video

Answer 100

digitalization and social media

Answer 101

Represents the main operation through wich data warehouses are fed data

Answer 102

Represents the main operation through wich datasets are fed data

Answer 103

Represents de main operation through wich database are fed data

Answer 104

Extract load transform

Answer 105

Extract transform load

Answer 106

Extract transform leave

Answer 107

Unstructure data, structure data and semi structura data

Answer 108

Only structure data and semi structura data

Answer 109

Only Unstructure data

Answer 110

is a subset of the data, that typically belongs to a deparment

Answer 111

is a framework open source

Answer 112

usually interface with an OLAP sYstem to support analytical queries

Answer 113

this allows the data to be queried during any data analysis

Answer 114

Heavily used by BI to run various analytical queries

Answer 115

software system that processes transaction-orientes data

Answer 116

social media, facebook twitter

Answer 117

OLTP, ERP, CRM and SCM systems

Answer 118

OLAP, ERP, CRM and SCM systems

Answer 119

For the amount data contained will continue to increase. The anlysis BI can suffer.

Answer 120

Is a process of loading data from a source system into a target system

Answer 121

software system that processes transaction-orientes data

Answer 122

Has established itself as a de facto industry platform for contemporary Big Data solutions

Answer 123

usually contain optimized databases, called analytical databases to handle reporting and data analysis

Answer 124

Represents de main operation through wich database are fed data

Answer 125

is a subset of the data, that typically belongs to a deparment

Answer 126

can have multiple EDWH

Answer 127

based on cleansed data, which is a prerequisite for accurate and error-free reports

Answer 128

large data storage

Answer 129

data processing

Answer 130

diagnostic, predictive and prescriptive

Answer 131

run on commodity hardware

Answer 132

denormalized data in the form of cubes

Answer 133

has established itself as a de facto industry platform for contemporary Big Data solutions

Answer 134

is a central, enterprise-wide repository

Answer 135

is always fed with data from multiple OLTP system using regular batch processing jobs

Answer 136

insert data

Answer 137

process data

Answer 138

velocity processing

Answer 139

Online transaction

Answer 140

sensor data

Answer 141

social media

Answer 142

scientific and research data

Answer 143

multiple types of data that need to be supported by Big Data solutions

Answer 144

data translates into the amount of time it takes for the data to be processed

Answer 145

data is processed by Big Data solutions is substantial and usually ever growing

Answer 146

Quality or fidelity of data

Answer 147

usefulness of data for an enterprise

Answer 148

refers to the multiple formats and types of data that need to be supported by Big Data Solutions

Answer 149

The appropriate form of data storage

Answer 150

Refers to the quality or fidelity of data

Answer 151

Refers ti the usefulness of data for an enterprise

Answer 152

refers to quality or fidelity of data

Answer 153

refers to the multiple formats and types of data that need to be supported

Answer 154

refers to usefulness of data for an enterprise

Answer 155

structured data, unstructured data, semi-structured data

Answer 156

structured data, unstructured data, semi-structured data, metadata

Answer 157

unstructured data

Answer 158

structured data

Answer 159

semi-structured data

Answer 160

semi-structured data

Answer 161

unstructured data

Answer 162

strutured data

Answer 163

has a defined level of structured and consistency can be relational in nature

Answer 164

has a defined level of structured and consistenc, but cannot be relational in nature

Answer 165

cannot be inheremtly processed or queried using SQL or traditional programming features

Answer 166

CRM or ERP

Answer 167

XML or , electronic data interchanges, e-mails, spreaddheets, RSS feeds and senso data

Answer 168

image or adio files

Answer 169

provides information about the analysis

Answer 170

provides information about a datasets characteristics and structure

Answer 171

provided the author and creation date of a document, file size and resolution of a digital photograph

Answer 172

audio binary file

Answer 173

videobinary file

Answer 174

quantitative analysis

Answer 175

cientics analysis

Answer 176

qualitative analysis

Answer 177

data mining

Answer 178

quantifying analysis the patterns and correlations found in the data

Answer 179

phenomrnon in the data

Answer 180

quantitave analysis

Answer 181

qualitative analysis

Answer 182

quantitative analysis

Answer 183

data mining

Answer 184

qualitative analysis

Answer 185

descriptions

Answer 186

patterns and correlations

Answer 187

identify patterns and trends

Answer 188

types of analytics

Answer 189

types of analisys

Answer 190

diagnostic

Answer 191

diagnostic analytics

Answer 192

descriptive analytics

Answer 193

predictive analytics

Answer 194

descriptive analytis

Answer 195

diagnostic analytics

Answer 196

preescriptive analytics

Answer 197

descriptive analytics

Answer 198

diagnostic analytics

Answer 199

predictive analytics

Answer 200

descriptive analytics

Answer 201

diagnostic analytics

Answer 202

preescriptive analytics

Answer 203

preescriptive analytics

Answer 204

predictive analytics

Answer 205

descriptive analytics

Answer 206

descriptive analytics

Answer 207

prescriptive analytics

Answer 208

predictive analytics

Answer 209

descriptive analytics

Answer 210

prescriptive analytics

Answer 211

diagnostic analytics

Answer 212

is the process of teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.

Answer 213

is the discipline of teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.

Answer 214

is the framework refers to teaching computers to learn from existing data and apply the acquired knowledge to formulate predictions about unknow data.

Answer 215

supervised learning

Answer 216

unsupervised learning

Answer 217

supervised learning

Answer 218

unsupervised learning

Answer 219

descriptive and diagnostic

Answer 220

diagnostic and predictive

Answer 221

descriptive an prescriptive

Answer 222

tradictional Big Data

Answer 223

traditional BI

Answer 224

the focus is a usually on a specific area of the bussines

Answer 225

the focus is view of key bussines areas in real time or near to real time

Answer 226

facilitate the development of an enterprise-wide understanding of the way a bussines works

Answer 227

focus on indivicual bussines processes

Answer 228

descriptive and diagnostic to facilitate the development of an an enterprise-wide understanding of the way a bussines works

Answer 229

charts, maps, data grids, infographics and alerts

Answer 230

ad-hoc reports drill, down

Answer 231

dashboards

Answer 232

in-disk analytical tchnologies

Answer 233

in-memory analytical technologies

Answer 234

groups data across multiple categories to show subtotals and totals

Answer 235

global an sumarized view of data across multiple context

Answer 236

enables a detail view of the data of interest by focusing in on a data subset

Answer 237

enables a detail view of the data of interest by focusing in on a data subset

Answer 238

global an sumarized view of data across multiple context

Answer 239

groups data across multiple categories to show subtotals and totals

Answer 240

global an sumarized view of data across multiple context

Answer 241

groups data across multiple categories to show subtotals and totals

Answer 242

enables a detail view of the data of interest by focusing in on a data subset

Answer 243

diagnostic and descriptive

Answer 244

predictive a descriptive

Answer 245

predictive and prescriptive

	Criado por Carolina Colorado mais de 7 anos atrás

Próximo

Test fundamentos Big Data

Descrição

Resumo de Recurso

Questão 1

Questão 2

Questão 3

Questão 4

Questão 5

Questão 6

Questão 7

Questão 8

Questão 9

Questão 10

Questão 11

Questão 12

Questão 13

Questão 14

Questão 15

Questão 16

Questão 17

Questão 18

Questão 19

Questão 20

Questão 21

Questão 22

Questão 23

Questão 24

Questão 25

Questão 26

Questão 27

Questão 28

Questão 29

Questão 30

Questão 31

Questão 32

Questão 33

Questão 34

Questão 35

Questão 36

Questão 37

Questão 38

Questão 39

Questão 40

Questão 41

Questão 42

Questão 43

Questão 44

Questão 45

Questão 46

Questão 47

Questão 48

Questão 49

Questão 50

Questão 51

Questão 52

Questão 53

Questão 54

Questão 55

Questão 56

Questão 57

Questão 58

Questão 59

Questão 60

Questão 61

Questão 62

Questão 63

Questão 64

Questão 65

Questão 66

Questão 67

Questão 68

Questão 69

Questão 70

Questão 71

Questão 72

Questão 73

Questão 74

Questão 75

Questão 76