Quiz
P1-Modulo 2 : Big data analysis y technology concepts
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Questions in this quiz (200)
1Big data analysis differs from trditional data analysis primary because
- Avolume, value and varirety
- Bvolume, velocity and variety
- Cveracity, volume and velocity
2In big data analysis and analytics, a fundamental step-by-step process is needed to organize the task involved
- Aretrieving, processing, producing and visualization data
- Bretrieving, processing, producing and repurposing data
- Cretrieving, processing, organize and repurposing data
3which are 1,2 and 3 stages of bigData analysis lifecycle
- AData analysis, data identification and ata extraction
- Bbussines cased evaluation, data extraction and data analysis
- Cbussines case evaluation, data identificaction and data acquisition and filtering
4which are 4,5 and 6 stages of bigData analysis lifecycle
- Adata analysis, data visualization & utilization of analysis results
- Bdata extraction, data validation & cleansing and data aggregation & representation
- Cdata extraction, data aggregation & representation
5which are 7,8 and 9 stages of bigData analysis lifecycle
- Adata analysis, data visualization and utilization of analysis results
- Bdata aggregation & represntation, data analyisis and data visualization
- Cdata identifcation, data acquisition & filtering and data extraction
6The business case evaluation stage requires that a business case be ________, __________ and ______________ prior to proceeding with the actual hands-on analysis tasks.
- Aorganized, created and approbed
- Bcreated, assessed and approbed
- Ccreated, organized and analyzed
7An evaluation of a Big Data analysis bussines case helps decision-makers undertand
- Athe business resources that will need to be utilized and wich bussines challenges the analysis
- Bthe data that will need to be utilized and wich bussines challenges the analysis
- Cthe business resources that will need to be utilized and wich bussines objectives the analysis
8The KPIs is ussefull in Business Case Evaluation
- Atrue
- Bfalse
9based on the business requirements documented in the _______________________________ , it can be determined whether the business problems being addresed are really Big data problems
- Ause case
- Bbusiness case
- Crequirements case
10a bussines problem needs to be directly related to one or more of the big data characteristics
- Averacity, velocity or variety
- Bvalue, velocity or variety
- Cvolume, velocity or variety
11Another outcome in Business case evaluation is
- Adetermination of budget required to carry out the analysis project
- Bdetermination of data required to carry out the analysis project
- Cdetermination of resources required to carry out the analysis project
12The invesment can be weighed against the expected benefits of achieving the goals
- Afalse
- Btrue
13Initial iterations of the big data analysis lifecycle will not required more up-front invesment of Big Data tecnologies, products and training compared to later iterations
- Afalse
- Btrue
14The data identification stage is dedicated to identifiying the _____________ required for the analysis project
- Ametadata
- Bdatasets
- Cdatamart
15identifying a wider variety of data sources may increase the probability of finding
- Ahidden patterns and aggregations
- Bhidden patterns and correlations
- Chidden resources and datasets
16Can be beneficial to identify as many types of releated data sources and insights as possible, especilly when we don´t know exactly what we're looking for.
- ATRUE
- BFALSE
17Depending on the business scope of analysis project and nature of business problems being adressed, the required dataset and their sourcescan be
- Astructured and not structured
- Bbig or small of all enterprise
- Cinternal or external to enterprise
18Internal dataset
- Adata markets and publicly avalaible datasets
- Binternal sources, such as data marts and operational system
- Cembedded within blogs or other types of content-based websites
19external datasets
- Astrudtured data, unstructured data
- Bdata marts and operational systems
- CCase they may need to be harvested via automated tools
20data is gathered from all of data sources that were identified during the previous stage, and is then subjected to the automated filtering of corrupt data or data that has been deemed to have no value to the analysis obvjectives
- AData identification
- Bdata acquisition & filtering
- CData aggregation & representation
21Depending on the type of data source , data may come as a dump of files or may require API integration
- Afalse
- Btrue
22in many cases , especially where external, unstructured data is concerned, some or most of the acquired data may be irrelevant (noise) and can be discarded as part of the filtering process
- AData acquisition & filtering
- BData extraction
- CData validation & cleansing
23data classified as "corrupt" can include records with missing or nonsensical values or invalid data type
- Afalse
- Btrue
24Data thah is filtered out for one analysis may not be valueable for a different type of analysis
- Afalse
- Btrue
25it is advisable to store a verbatim copy of the original dataset proceeding with the filtering.
- ATo save on required storage space, the verbatim copy is compressed after storage
- BTo save on required storage space, the verbatim copy is compressed before storage
- CTo save on required storage space, the verbatim copy is compressed in the same time of storage
26to be persisted once it gets generated or enters the enterprise boundary
- Ainternal data
- Binternal and external data
- Cexternal data
27The data is persisted to disk prior to analysis
- Arealtime analytics
- BBatch anlytics
- Crealtime analytics and batch analytics
28The data is analyzed first and then persisted to disk
- Arealtime analytics
- Bbatch analytics
- Crealtime analytics and batch analytics
29Can be added via automation to data from both internal and external data sources to improve the classification an querying
- Ainfo data
- Bdata analysis
- Cmetadata
30Metadata example can include
- Adatamart size and structure, source information, date and time of creation or collection, language-specific information etc.
- Bdatabase size and structure, source information, date and time of creation or collection, language-specific information etc.
- Cdataset size and structure, source information, date and time of creation or collection, language-specific information etc.
31it is vital that metadata be machine-readable and passed forward along subsequent analysis stages
- Afalse
- Btrue
32This helps to maintain data provenance throughout the Big Data analysis lifecycle, wich helps establish and preserve data accuracy and quality
- Ametadata
- Bsource information
- Cdate and time of creation or collection
33Some of the data identified as input for the analysis may arrive in a format incompatible with the big data solution
- Atrue
- Bfalse
34the need to address disparate types of data is more likely with data from
- Ainternal sources
- Bexternal sources
35is dedicated to extracting disparate data and transforming it into a format that the underliying Big Data solution can use for the purpose of the data analysis
- AData acquisition & filtering
- Bdata validation & cleansing
- Cdata extraction
36The extent of extraction and transformation required depends
- Aon the types of analytics and capabilities of the Big Data solution
- Bbussines case
- CData extraction
37Estracting the required fields from delimited textual data (such as with web server log files) may not be necessary
- Acapabilities of the Big Data Solution
- Bunderlying Big Data solution can already directly process those files
- Ctransforming it into a format that underlying Big Data solution
38example of document that not need further transformation
- AXML and JSON
- Bfacebook and twitter
- Cimage and video
39The invalid data can
- ASkew an falsify analysis results
- BLose business objectives
- CLose the accuracy of the analysis
40data input into Big Data analyses can be unstructured without any indication of validity
- Afalse
- Btrue
41the complexity can further make it easy to arrive at a set of suitable validation constraint
- Afalse
- Btrue
42Is dedicated to establishing (often complex) validation rules and removing any know invalid data
- AData acquisition and filtering
- BData validation and cleansing
- CData identification
43Big Data solutions often receive redundant data across different datasets, this redundancy can be exploited to explore interconnected datasets in order to assemble validation parameters and fill in missing valid data
- Afalse
- Btrue
44For Batch analytics, data validation and cleansing can be achieved via offline
- Adata minnig
- BELT operation
- CETL operation
45Data input in Big Data can be unstructured without any indication of validity
- Afalse
- Btrue
46provenance can play an important role in determining the accuracy and quality of questionable data
- Afalse
- Btrue
47data that appears to be invalid may still be valuable in that it may posses
- Amost important data
- Bhidden patterns an trends
- Cnoise
48Data may be spread across multiple datasets, requiring that datasets be joined together via common files (date or ID)
- Afalse
- Btrue
49either way a method of data ________________ is required or the dataset representing ther correct value needs to be determined
- Aaggregation
- Breconciliaton
- Crepresentation
50Dedicated to integrating multiple datasets together to arrive at a unified view
- AData aggregation & representation
- BData extraction
- CData visualization
51Can become complicated because od differences in : although the data format may be the same, the data model may be different
- Asemantics
- BBD engine
- CData structure
52Can become complicated because od differences in : A valuethat is labelled differently in two different datasets may mean the same thing (surname and last name)
- ABD engine
- BSemantics
- CData structure
53In data Aggregation & Representation reconciling the differences can required complex logic that is executed ___________________.
- AETL process
- Bhuman intervention
- Cautomatically
54Future data analysis requirements need to be considered during the stage ___________________ to help foster data reusability
- AData extraction
- BData aggregation & REpresentation
- CData validation and cleansing
55whether ___________________ is required or not, it is important to understand that the same data can be stored in many different forms. One form may be better suited for a particular type of analysis than another
- Adata cleansing
- Bdata aggregation
- Cfiltering
56A data structured standarized by the Big Data solution can require establishing a central, standard analysis repository, such as a
- Auntructured database
- Bstructured database
- CNoSQL database
57the data analysis stage is dedicated to carriying out the actual analysis task, which typically involves one or more types of analytics
- Adata validation %cleansing
- BData Analysis
- CUtilization os analysis results
58This stage can be iterative in nature, because repeated until appropiated pattern or correlation is uncovered
- Adata aggregation & representation
- BData analysis
- CData extraction
59The approach taken when carrying out this stage, data analysis, an be classified as ______________________________
- Aacquisition analysis and filtering analysis
- Bconfirmatory analysis and exploratory analysis
- Cvalidation analysis and cleansing
60___________________________ adata analysis is a deductive approach where the cause of the phenomenon being investigated is proposed beforehand
- AConfirmatory analysis
- BExploratory analysis
- CData analysis
61the proposed cause or assumption is called a
- Apattern and trend
- Bdeductive approach
- Chypotesis
62data samples are tipically used
- Aexploraty analysis
- Bconfirmatory analysis
63unexpected findings or anomalies are usually ignored since a predetermined cause was assumed
- Atrue
- Bfalse
64is an inductive approach that is closely associated to data mining
- Aexploratory data analysis
- Bconfirmatory data analysis
- Ccorrelation analysis
65this analysis provides a general direction that can facilitate the discovery of patterns or annomalies
- Aconfirmation analysis
- BExploratory analysis
66Large amounts of data and visual analysis are typically used
- AConfirmatory analysis
- BExploratory analysis
67is dedicated to using _____________________ techniques and tools to graphically communicate the analysis results for effective interpretation by bussines users
- AData analysis
- BData visualization
- CUtiolization of analysis results
68Bussines users needs to be able to understand the results in order to obtain value from analysis and subsequently have de ability to provide feedback from_______________ back to stage __________________
- AData validation and cleaning, data extraction
- BData analysis, data aggregation & representation
- CData visualization, Data analysis
69the same results may be presented ina a number a number of different ways.
- Afalse
- Btrue
70another aspect to keep in mind is that providing a method of drilling down to comparatively simple statistics is crucial, in order for users to understand how to statistics were generated
- Atrue
- Bfalse
71support businessdecission-making, there may be further opportunieties to utilize the analysis results
- AUtilization of analysis results
- BData visualization
- CData analysis
72The utilization os analysis results is dedicated to determining how and where processed analysis data can be further leveraged
- AUtilization of analysis results
- BData visualization
- CData analysis
73"models" that encapsulated new insights and understandings about the nature of the patterns and realationships that exist within data that was just analyzed
- Autilization of analysis results
- BData analysis
- CData validation &cleansing
74A "model" may look like a
- Amathematical equation or a set of rules
- Bstructred database
- Cthe differents datasets
75Models can be used to improved bussines process logic
- Anew dataset
- Bform the basis of a new system or software program
- Capplication system logic
- Dnew bussines case
76the data analysis results may be automatically or manually fed directly into enterprise systems to enhace and optimize their behavior and performance
- Ainput for enterprise systems
- BBussines process optimization
- CAlerts
77The identiffied patterns correlations and anomalies discovered during the data analysis are used to refine business process
- Ainput for enterprise input
- Balerts
- CBussines process optimization
78Data analysis results can be used as input for existing events that requires them to take corrective action
- Ainput for enterprise input
- Bbusiness process optimization
- Calerts
79Big data nalysis concepts
- Astatical
- Baggregation
- Cvisual
- Dmachine learning
- ESemantic
- FTopic mapping
- Gfeelings
80statistical analysis
- AA/B Testing
- Bheat maps
- Ccorrelation
- DRegression
- Efiltering
81visual Analysis
- Aheat maps
- Boutlier detection
- Ctime series analysis
- DSpatial Data Analysis
- ENetwork analysis
82machine learning
- Acorrelation
- Bclasification
- Cclustering
- Doutlier detection
- Efiltering
- Fregression
83semantic analysis
- Aclassification
- Bnetwork analysis
- CNatural language processing
- Dtext analytics
- Esentiment analysis
84use statistical methods based on mathematical formulas as means for analizing data
- Avisual analysis
- Bstatistical analysis
- Cmachine learning
85it can also be used to infer patterns ans relationships within the dataset, such as regression and correlation
- Astatistical analysis
- Bsemantic analysis
- Canalysis topic mapping
86also know as split or bucket testing, compares two versions of an element to determine wich version is superior based on a predefined metric
- Acorrelation
- BA/B testing
- Cregression
87A/B testing: the current version of the element is called the ______________ version, whereas the modified version is called the ____________
- Aofficial, non official
- Bcontrol,reatment
- Cprincipal, copy
88both version, are subjected to an experiment simultaneously. The observationsare recorded to determine wich version is more sccessful
- Acorrelation
- BRegression
- CA/B testing
89Athough ________________________can be implemented in almost domain, it is most often used in marketing
- AA/B Testing
- BRegression
- CCorrelation
90Generally, the objective is to gauge human behavior with the goal of increasing sales (as per the example below)
- ARegression
- BA/B testing
- CCorrelation
91is the new version of a drug better than the old one?
- Acorrelation
- BRegression
- CA/B testin
92is an analysis tecnique used to determine whether two variables are related to each other
- ARegression
- BCorrelation
- CA/B testing
93an example of a relationship between two variables:
The value of variable A increases whenever the value of variable B increases- ARegression
- BA/B testing
- CCorrelation
94Helps to develop an understanding of a dataset and find relationships that can assist in explaining a phenomenon
- ACorrelation
- BRegression
- CA/B testing
95commonly used for data mining where the identification between variables in a dataset leads to the discovery of patterns ans anomalies
- Aregression
- Bcorrelation
- CA/B testing
96When two variables are considered to be correlated they are considered to be aligned based on a linear relationship
- Afalse
- Btrue
97This mean that when one variable changes, the other variable also changes proportionally and constantly
- AA/B testing
- Bregression
- Ccorrelation
98______________________ is expresed a a decimal number between -1 to 1, which is know as the correlation coeficient
- ACorrelation
- BRegression
- CA/B testing
99Correlation +1
- ASuggest that there is a strong positive relationship between the two variables
- Bsuggests that there is no relationship at between two variables
- CSuggest that there is a strong negative relationship between the two variables (hipotesis)
1000 Correlation
- ASuggest that there is a strong positive relationship between the two variables
- Bsuggests that there is no relationship at between two variables
- CSuggest that there is a strong negative relationship between the two variables (hipotesis)
101-1 Correlation
- ASuggest that there is a strong negative relationship between the two variables (hipotesis)
- Bsuggests that there is no relationship at between two variables
- CSuggest that there is a strong positive relationship between the two variables
102sample: "Do students who perform well at elementary school perform equally well at high school"
- Aregression
- BCorrelation
- CA/B testin
103explores how a dependent variable is related to an independent variable within a dataset
- ACorrelation
- BRegression
- CA/B Testing
104Helpss determine how the value od dependent variable changes in relation to changes in the value of the independent varible
- ACorrelation
- BRegression
- CA/B testing
105what the analysts discover is that 15% of additional stock in required for enery 5-degree increase in temperature
- Aregression
- Bcorrelation
- CA/b testing
106more than one independent variable can be tested at the same time
- AA/B testing
- BRegression
- Ccorrelation
107in such cases only one independent variable may change. The others are kept constants
- AA/B testing
- BCorrelation
- CRegression
108can help enable a better understanding of what a phenomenin is and why it ocurred
- ACorrelation
- BRegression
- CA/B testing
109represents a constant rate of change
- Alinear regression
- BNon-linear regression
110Represents the variable rate of change
- Alinear regression
- Bnon-linear regression
111what will be the grades of a student studying at a high school based on her primary school grades
- Acorrelation
- Bregression
- CA/B testing
112_________________does not imply a causation. The change in the value of one variable may not be responsible for the change in the value of the second variable. although both may change at the same rate
- AA/B testing
- Bcorrelation
- CRegression
113assumes that both variables are independent
- ARegression
- Bcorrelation
- CA/B testing
114Deals with already identified dependent and independent variables
- ACorrelation
- BRegression
- CA/B Testing
115_________________ can be applied to further explore the relationship and predict the values of the dependent variable, based on the know values of the independent variable
- Acorrelation
- BRegression
- CA/B testing
116is a form of data analysis that involves the graphic representation of data to enable or enhace its visual perception
- Astatistical analysis
- Bvisual analysis
- Csemantic analysis
117develop a deeper understanding of the data being analyzed. Specifically, it helps identify and highlight hidden patterns, correlations and anomalies.
- Astatistical analysis
- Bvisual analysis
- Csemantic analysis
118visual analysis
- AHeat maps
- Btime series analysis
- Coutlier detectition
- Dnetwork analysis
- Espatial data analysis
119Are an effective visual analysis technique for expressing patterns, data compositions via part-whole relations and geographic distribution of data
- Atime series analysis
- Bheat maps
- Cspatial data analysis
120They also facilitate the identification of areas of interest ans the discovery of extreme (high/low) values wihin a dataset
- ANetwork analysis
- Bheat maps
- Cspatial data analysis
121___________ itself is a visual, color-coded representation of data values
- Anetwork analysis
- Bheat-maps
- Cspatial data analysis
- Dtime series analysis
122A _______________ can be in the form of a chart or a map, as shown in the following pages
- Aheat maps
- Btime series analysis
- Cnetwork analysis
- Dspatial data analysis
123A___________ represents a matrix of values in which each cell is color-coded according to the value
- Achart
- Bmap
124 A ___________ represents a geographic measure by wich different regions are color-code according to certain theme
- Achart
- Bmap
125How can i visually identify any patterns related to carbon emission across a large number of cities around the world
- AHeat maps
- Btime series analysis
- Cnetwork analysis
- Dspatial data analysis
126____________is the analysis of data that is recorded over periodic intervals of time
- Aheat maps
- Btime series analysis
- Cnetwork analysis
- Dspatial data analysis
127Helps to uncover patterns within data that are time-dependent. Once identified, the patterns can be axtrapollated for future predictions
- Aheat maps
- Btime series analysis
- Cnetwork analysis
- Dspatial data analysis
128time series analyses are usually used for forecasting by identifiying long-term trends. seasonal periodic patterns and irregular short-term variations in the dataset
- Atime series analysis
- Bheat map
- Cnetwork analysis
- Dspatial data analysis
129always includes time as a comparision variable
- Anetwork analysis
- Bheat maps
- Ctime series analysis
- Dspatial data analysis
130is generally expressed using a line chart, with time plotted on the x-axis and the recorded data values plotted on the y-axis
- Atime series analysis
- Bheat map
- Cnetwork analysis
- Dspatial data analysis
131how much yield should the farmer expect based on historical yield data
- Anetwork analysis
- Bspatial data analysis
- Cheat maps
- Dtime series analysis
132is an interconected collection of entities
- Aheat maps
- Btime series analysis
- Cnetwork analysis
- Dspatial data analysis
133An entity can be a person a group or some other business domain object such as a product
- Aspatial data analysis
- Bheat maps
- Ctime series analysis
- Dnetwork analysis
134some conectios may only be one-way, so that transversal in the reverse direction is nor possible
- Atrue
- Bfalse
135is a techniquethat focuses on analizing relationships between entities within the network
- Atime series analysis
- Bheat maps
- Cnetwork analysis
- Dspatial Data analysis
136There are specialized variations of network analysis
- AGraphs
- Broute optimization
- Csocial network analysis
- Dspread predictions
137is used to find the shortest routes between the central warehouse and remote stores in order to minimize the durations of deliveries
- Aheat map
- Bnetwork analysis
- Cspatial data analysis
- Dtime series analysis
138How can identify interaction patterns among a very large number of protein-to-protein interactiona?
- Aspatial data analysis
- Bnetwork analysis
- Cheat maps
- Dtime series analysis
139is focused on analizing location-based data in order to find different geographic relationships and patterns between entities
- Anetwork analysis
- Bspatial data analysis
- Ctime series analysis
- DHeat maps
140____________________________ is manipulated through a geographical information system (Gis) that plots spatial data on a map generally using its longitude and latitude coordinates
- ASpatial data
- Bstructured data
- Cunstructured data
141no two stores can be within a distance of 5 kilometers of each other to prevent the stores from competing with each other.
- Atime series analysis
- Bnetwork analysis
- Cheat map
- Dspatial data analysis
142how far do customers have to commute in order to get to a supermartket?
- Aspatial data analysis
- Bheat maps
- Ctime series analysis
- Dnetwork analysis
143if the human knowledge can be combined with the processing speed of machines, machines will be able to process large amounts of data without requiring much human intervention
- Astatisctical analysis
- Bvisual nalysis
- Cmachine learning
- Dsemantic analysis
144machine learning
- Aclassification
- Btime series analysis
- Cclustering
- Doutlier detection
- Efiltering
145Two fundamental laws that pertain to machine learning
- Alaw of large numbers
- Blaw commutative
- CLaw of dimishing marginal utility
146the law _____________________________states that the confidence with wich predictions can be made increases as the size of data that is being analyzed increases
- Alaw of large numbers
- Blaw of dimishing marginal utility
147in other words the accuracy and applicability of the patterns and relationshipsthat are found in a large dataset will be higher that of a smaller dataset
- ATrue
- BFalse
148the greater the amount of data available for analysis, the better we become of making correct decisions
- ATrue
- BFalse
149in the context of traditional data analysis, ___________________________ states that, starting with a reasonably large sample size, the value obtained from the analysis of additional data decreases as more data is successively added to the original sample
- Athe law of diminishing marginal utility
- Bthe law of large number
150The law of dimishing marginal utility does not apply to big data
- ATrue
- BFalse
151The greater the volume and variety of data that Big Data solutions can process allows for each additional batch of data to carry greater potential of unearthing new patterns and anomalies. Therefore, the value of each additional batch does not diminish value: rather, it provides more value
- ATrue
- BFalse
152is a supervised learning technique by witch data is classified into relevant, previously learned categories
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
153Step 1: The system is fed data that is already categorized or labeled, so that it can develop an understanding of different categories
- Aclustering
- Bclassification
- Cfiltering
- Doutlier detection
154step 2: The system is fed unknow (but similar) data for classification, based on the understanding it developed
- Aclassification
- Bfiltering
- Coutlier detection
- Dclustering
155A common application of this techniques is for the filtering of e-mail spam. Note that classification can be performed for two or more categories
- Afiltering
- Bclustering
- Cclassification
- Doutlier detection
156Based on old data, a training dataset is compiled that contains tagged examples of customers that have or not previously defaulted
- Aclustering
- Bfiltering
- Cclassification
- Doutlier detection
157Does a fingerprint belong to a suspect based on a record of this previous fingerprints
- Aoutlier detection
- Bclustering
- Cclassification
- Dfiltering
158Is an unsupervised learning technique by wich data is divided into different groups so that the data in each group has similar properties
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
159There is no prior learning of categories required: instead categories are implicity generated based on the data groupings
- Aoutlier detection
- Bclustering
- Cfiltering
- Dclassification
160Is generally used in data minig to get an understanding of properties of a given dataset. Afterdeveloping this understanding, classificatioin can be used to make better predictions about similar, but new or unseen data
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
161In a bank each group is the introduced to one or more financial products most suitable to the characteristics of the overall profile of the group
- Aclustering
- Bfiltering
- Coutlier detection
- Dclassification
162How many different categories of elements are there in the periodic table
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
163Detection is the process of finding data that is significantly different from or inconsistent with the rest of the data within a given dataset
- Afiltering
- Bcalssification
- Cclustering
- Doutlier detection
164this machine learning tecnique is used to identify anomalies, abnormalities and deviations that can be opportunities or risks
- Aoutlier detection
- Bclassification
- Cclustering
- Dfiltering
165it can be bsaed on either supervised or unsupervised learning
- Aclustering
- Boutlier detection
- Cclassification
- Dfiltering
166include fraud detection, medical diagnosis, network data analysis and sensor data analysis
- Afiltering
- Boutlier detection
- Cclassification
- Dclustering
167In order ti find if a transaction is likely to be fraudulent or not, the bank´s IT team builds a sustem emplying ____________________ technique that is based on supervised learning
- Aclassificaction
- Bclustering
- Coutlier detection
- Dfiltering
168are there any wrongly identified fruits and vegetables in the training dataset used for classification task
- Aclassification
- Boutlier detection
- Cclustering
- Dfiltering
169is the automated process of finding relevant items from a pool of items
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
170items can be filtered either based on a users own behavior or by matching the behavior of multiple users
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
171_________________ is generally applied viat the following two approaches
- Acollaborative filtering
- Buser behavior
- Ccontent-based filtering
172items can be filtered either based on a users own behavior or by matching the behavior of multiple users
- Aclustering
- Bfiltering
- Cclassification
- Doutlier detection
173A common medium by wich ________________is implemented is via the use of a recomender system
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
174technique based on the collaboration of users past behavior
- Acollaborative filtering
- Bclassification
- Cclustering
- Doutlier detection
- Econtent-based filtering
175based on the similarityof users behavior, items are filtered for the target user
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
176is solely based on the similarity between users behavior, and requires a large amount of user behavior data in order to accurately
- Afiltering
- Bclassification
- Cclustering
- Doutlier detection
- Efiltering collaborative
177collaborative filtering is an example of application of law of large numbers
- ATrue
- BFalse
178technique focused on the similarity between users an items
- Aclassification
- Bclustering
- Coutlier detection
- Dfiltering
- Econtent-based filtering
179A user profile is created based on the users past behavior (likes, ratings, purchase history, etc)
- Acollaborative filtering
- Bcontent_based filtering
180Contrary to collaborative filtering, content-based filtering is solely dedicated to individual user preferences and does not require data about other users
- ATrue
- BFalse
181A recomender system predicts user preferences and generate suggestions for the user accordingly
- Afiltering
- Bclassification
- Cclustering
- Doutlier detection
182suggestions commonly pertain to recomending items, such as movies, books, web pages, people etc
- Aclustering
- Bclassification
- Cfiltering
- Doutlier dtection
183A recomender system typically uses either collaborative filtering or content-based filtering to generate suggestions
- ATrue
- BFalse
184recommender system may also be based on a hybrid of both collaborative filtering and content-based filtering to fine-tune the accuracy and effectiveness of generated suggestions
- ATrue
- BFalse
185Based on matches found between financial product purchased by customers and the properties of similar financial products, the recommnder system automates seggestion for potential financial products that customers may also be interested in
- Aclustering
- Bclassification
- Cfiltering
- Doutlier detection
186Wich holiday destinations can be recommended based on the travel history of a holiday makes?
- Aclustering
- Bclassification
- Coutlier detetcion
- Dfiltering
187A fragment of text or speech data can carry different meanings in different contexts, whereas a complete sentence may retain its meaning, even if structured in different ways. In order for the machines to extract valuable information, text and speech data needs to be understood by the machines in the same way as humans do. Semantic analysis represents practices for extracting meaningful information from textual and speech data
- Astatistical analysis
- Bsemantic analysis
- Cvisual analysis
- Dmachinne learning
188types of semantic analysis
- Anatural language processing
- Bhuman behavior language
- Ctext analytics
- Dsentimental analysis
189Is a computers ability to comprehend human speech and text as naturally understood by humans
- Atext analytics
- BNatural language Processing
- Csentiment analysis
190This allows computers to perfom a variety of useful task, such as full-text searches
- AText analysis
- Bsentiment analysis
- CNatural language processiing (NLP)
191instead of hard-coding the required learning rules, either supervised or unsupervised machine learning is applied to develop the computer undestanding of the natural language
- Atext analysis
- Bnatural language processing
- Csentiment analysis
192in general the more learning data the computer has, the more correctly it can decipher human text and speech
- Anatural language Processing
- Btext analytics
- Csentiment analysis
193Natural language processing includes both text and speech recognition
- ATrue
- BFalse
194For speech recognition the system attempts to comprehend the speech and then performs an action, such as transcribing text
- Atext analytics
- Bsentiment analysis
- CNatural language processing
195How can grammatical mistakes be automaticalle identified?
- Atext analytics
- BNatural Language processing
- Csentiment analysis
196Unstructured text is generally much more difficult to analyze and search, compared to structured text
- ATrue
- BFalse
197is the specialized analysis of text through the application of data mining, machine learning and natural language processing techniques to extract value out of unstructured text. Text analytics essentially provides the ability to discover text rather than just search it
- ANatural language processing
- Btext analytics
- Csentimente analysis
198useful insights from text-based data can be gained by helping business develop an understanding of the information that is contained within a large body of text
- ATrue
- BFalse
199the basic tenet of text analytics is to turn unstructured text into data that can be searched and analyzed
- ANatural language processing
- Btext analytics
- Csentiment analysis
200As the amount of digitized documents, e-mail, social media posts and log files increases, businesses have an increasing need to leverage any value that can be extracted from these forms of semi-structured and unstructured data
- Atext analytics
- Bnatural language processing
- Csentiment analysis
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- Description
- modulo 2 Big Data
- Author
- Carolina Colorado
- Language
- Español
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