|Exam Name||:||IBM SPSS Data Analysis for Business Partners v2|
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|Updated On||:||July 13, 2018|
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Mitsui O.S.k. traces and its wholly-owned consolidated subsidiary MOL assistance systems, (MOLIS) to birth multi-dimensional evaluation of the explanations for incidents and issues on its operated vessels, using IBM's statistical evaluation software, "IBM SPSS Modeler".
IBM SPSS Modeler is an superior statistics analysis software that offers potential analysis from mass quantity of statistics and helps more advantageous choice making to solve enterprise considerations.
The MOL community has conventionally aggregated incidents and problems records mentioned by its operated vessels to "visualize" secure operation. And to any extent further, the community will advance extra beneficial measures to avoid incidents and assess the consequences through examining correlations and causal relationship of statistics from numerous sources (for instance, operation statistics, crewmember statistics, vessel inspection information, and so forth).
additionally, it will build a brand new analysis formulation the usage of the text mining function, for some points of unstructured statistics, corresponding to close misses gathered from crewmembers.
in advance of this analysis, the group held a three-month trial beginning in July 2017 and developed analysis fashions that check causal relationship of tips on crewmembers, corresponding to downtime problems and years of onboard adventure.The MOL community normally makes use of and applies ICT know-how in a proactive manner, with the goal guaranteeing protected, reliable cargo transport and becoming the world chief in protected operation.
catch and send sensor facts the usage of IBM Watson IoT Platform, and then analyze circulation patterns the use of SPSS Modeler
This tutorial is according to the Harlem Shake game that Romeo Kienzler developed and offered in his tutorial titled Create a enjoyable, basic IoT accelerometer online game. Kienzler's video game makes use of simple circulation information from a smartphone, streams the statistics to the cloud, captures the data in a Cloudant database, after which analyzes and determines the winner using IBM records Science event.
during this tutorial, we'll delivery with Kienzler's basics with the aid of additionally the use of IBM Cloud (previously IBM Bluemix) and the IBM Watson IoT Platform capabilities, including Node-crimson, MQTT (in Watson IoT Platform), and Cloudant. we will differ from his Harlem Shake game in these methods:
fiddling with sensor input from a smartphone is fun, but are there enterprise purposes for sensor facts? The brief reply is, sure. imagine you've got a creation facility and the sensor information tells you that anytime a truck is using close it, the production first-class lowers. that would be valuable assistance to grasp. A "stream" is nothing greater than a combination of three-d sensor data about position and velocity. bear in mind there is greater than moving sensors and you'll believe different sensor input like temperature, pressure, or even optical awareness of the grownup who enters a shop. All this statistics can impact business key efficiency warning signs (KPIs) like pleasant, throughput, earnings, or the efficiency of people working. Now, if you understand what the circumstance is, you might take action on and increase the condition.
we can build our online game in a number of steps:
Create the base application and check the statistics in the Cloudant database
this first step is a big one. As we outlined earlier than, this tutorial is based on the Harlem Shake game published by means of Romeo Kienzler. To get all started, go to Create a enjoyable, standard IoT accelerometer game, and comprehensive the steps in Steps 1 via 5. in the event you're requested to name your application, you could call it the rest you love, but for the examples in this tutorial I called my application anythinguniquewilldods123.
earlier than you come back to this tutorial to lengthen Kienzler's work, you'll have deployed a video game application the usage of one-click deployment, changed the web of things Platform carrier, ensured the MQTT message broker can get hold of data, set up a Cloudant NoSQL database to keep the facts, and streamed the records to Cloudant the use of Node-purple. (do not be troubled – it's not as a great deal work because it sounds, and or not it's enjoyable!)
After finishing these steps in Kienzler's article, we will subsequent examine if data arrives within the desk. be sure the online game app to your smartphone is still sending records through searching on the debug tab in Node-crimson.
every little thing may still seem to be first rate. The smartphone shakes, the statistics streams as much as the cloud, and the database is retaining the statistics. but at this factor, we nevertheless don't know if statistics definitely arrives within the Cloudant instance. Kienzler's approach become to use information Science event, but we're going to use SPSS Modeler. earlier than we get to that factor, however, we need to make a number of more adjustments.
bear in mind, Cloudant is a NoSQL ("no longer-simplest-SQL") database gadget that we use to save the facts. we are able to operate a basic examine to see if records is arriving through the use of the Cloudant Dashboard.
To spark off your IoT sensors on your smartphone, do here:
change the latest Cloudant database so that you can compile extended information points
We should regulate the Harlem Shake utility to no longer only list the x, y, z position facts of the smartphone, however also the acceleration statistics and a number of more statistics features.
change the existing Cloudant database
To open your Node-purple instance, do here:
right here circulate should be displayed.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image3.png" class="IBM-downsize" alt="" height="336" width="1334"/>
be aware: do not installation and test yet.
The app operating in your smartphone provides the parameters X, Y, Z, alpha, beta, gamma, and stime. The mtype parameter is decided to the first of our experimental move kinds. We go deeper into this parameter within the coming steps.
Optionally, you may give the feature a positive identify (I used flatten_for_training.). The remaining effect should still look like this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image4.png" class="IBM-downsize" alt="" height="668" width="968"/>
word: The SENSORID value should still replicate the alphanumeric identification you used in the event you activated your smartphone. don't be anxious about the layout of the timestamp. We transform it later into a greater readable format.
regulate the database to compile data for three diverse flow varieties
We listing some example data for three different movement types as illustrated in determine 1:
which you could test with your own ideas later.
To establish the flow kinds within the information data, we must tag them internal the code. We already did this for the primary stream class roll within the outdated step. For trying out, we disconnected the Cloudant node. we can finalize the pre-work now, and start the assortment of pattern facts.
be aware: in case you watch the debug tab on your Node-red move editor while doing this you could see crimson error messages. The free version of Cloudant best handles 10 events per 2nd and often the smartphone is sooner. we will ignore this moderate lack of facts.
Now that we recorded pattern facts for different flow kinds within the database, we deserve to connect our database to our evaluation tool, SPSS Modeler. We build a statistical model and "teach it" the information structure of the actions.
installation and configure the Cloudant Extension to connect SPSS Modeler to the database
before we can connect our database to our modeling device, we first should deploy the SPSS Modeler. The actual steps depend upon your version of SPSS Modeler and your operating device. in case you already have SPSS Modeler installed, you can use your installing.
SPSS Modeler is open for extensions the usage of public assets. We use one of those public property to connect with the Cloudant database. With a working installing, install these integral extensions.
The Cloudant Extension is available in a compressed file (.zip) with an instance SPSS circulation. We deserve to regulate the instance move to hook up with our personal Cloudant database.
note: The compressed file additionally incorporates documentation in PDF format within the instance folder. See the document named Mining Cloud information with SPSS.
be aware: The Cloudant node is in keeping with a familiar R node and therefore needs an input node. for this reason, we discover the user enter node right here. do not delete it, despite the fact that it does not do anything else valuable.
The circulation in SPSS Modeler now should seem like this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image8.png" class="IBM-downsize" alt="" height="319" width="431"/>
be aware: do not replica the quotation marks.
we have a working connection between our SPSS Modeler computer-based mostly analysis workbench device and a cloud-primarily based database. The records can also be analyzed identical to any local database or file.
We next create a predictive mannequin that "is aware of" the way to identify the circulate category out of the uncooked records.
Create a classification mannequin circulate in SPSS Modeler
SPSS Modeler "learns" from present data to create a mannequin and uses the ensuing mannequin to follow what it has learned to new statistics.
in this step, we train the model in regards to the facts we recorded. The ensuing mannequin "knows" the mixture of parameters to establish the distinctive move varieties.
In statistical terms, here is a classification or decision tree mannequin. The massive capabilities of SPSS Modeler is that we do not have to understand the rest greater about statistics – the tool finds the suitable model instantly.
Our tutorial displays a reality about real-life information science tasks: eighty to ninety percent of the work is getting the data and transforming it by some means. In our case, we need to operate two essential steps:
seriously change the timestamp
The timestamp is a great instance of uncooked statistics provided by a sensor that needs to be modified to be usable. The timestamp from the smartphone app is simply a serial quantity in response to 1 January 1970 as starting factor. we are able to set this reference aspect in the circulation's homes.
The outcomes should still look like this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image13.png" class="IBM-downsize" alt="" height="934" width="1884"/>
the brand new field incorporates a time and date cost that SPSS Modeler can use for graphs and analysis. We don't use it during this tutorial – it is just an example transformation – but that you can choose it up later if you wish to chart some information.
Calculate a brand new measure for the energy used
The raw facts for the flow in X, Y, and Z direction could no longer be sufficient for a fine prediction. It is very common for a data science mission to use the raw statistics to calculate a brand new measure. In his usual tutorial, Kienzler calculates the standard power (whatever thing just like the relative movement in all directions). We select up this illustration right here once more via adding an additional Derive node.
The result may still appear to be this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image14.png" class="IBM-downsize" alt="" height="452" width="533"/>
inform SPSS Modeler what the target is to be anticipated
during this step, we tell SPSS Modeler what exactly should still be envisioned – what is our goal? this is performed the use of a type node.
After the second Derive node called energy, add a new category node.
notice: We want the timestamp and date and time primarily for graphing consequences later. These fields aren't considered within the automated creation of the mannequin.
Add a classification model
We use a classification model to "study" the connection between the raw records, the calculated energy, and the stream forms. The mannequin learns which combination of input statistics is typically accompanied for the three different flow varieties.
SPSS Modeler knows diverse algorithms for a classification (or resolution tree) and even can locate the foremost working model automatically. as a result of we need to install the mannequin later to IBM desktop learning in IBM Cloud we use one selected algorithm here.
notice: I chosen the C&R Tree model as an instance. In some situations – reckoning on your particular person facts – this model could now not be usable and you'll get an error message. you could are trying different fashions, however be aware that now not all fashions run within the cloud deployment. that you could, for instance, use the Logistic model (regression). This could not be the most useful algorithm, but it should still work in many cases.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image16.png" class="IBM-downsize" alt="" height="508" width="1646"/>
install and test the classification model manually
we now have all of the abilities about relocating the smartphone programmed into the classification mannequin nugget. we can now collect some new information (with out the flow class) and let the model tell us which circulate class this could be. We call this the "deployment of the mannequin."
during this step, we compile some new facts and find out how the smartphone moved.
Create a Cloudant database to assemble scoring records
We set up a new Cloudant database to collect the scoring statistics (raw information as before, but devoid of the stream category identification - think about a person moved the smartphone in a hidden place) and run it manually in SPSS Modeler.
First, create the brand new database illustration.
regulate the existing Cloudant node
We created a new empty database illustration and we redirect the sensor information accumulated from the smartphone into this new instance.
observe: For technical motives, don't delete the road!
test to be sure the circulate is being acquired by way of the database
we've got created the database and given it the code to compile the facts. Now or not it's time to test it.
note: in case you do, do not forget to installation!
Create a new circulate in SPSS Modeler to investigate the movement information (scoring)
We deserve to create a working deployment for predicting the movement kinds.
observe: the brand new mannequin became automatically linked to the original information. we're changing this connection here with the new move half with the scoring records.
observe: If crucial, you could are looking to first move the nugget node on the canvas by way of dragging it.
note: The nearer this ranking is to 1.0, the stronger the prediction.
we've a working deployment for finding a "top-rated guess" on the move forms. occasionally it might possibly be greater beneficial to ranking the sensor records "on the fly" in its place of recording it in a database before scoring.
Add a new department to the SPSS flow for reside scoring
The IBM Cloud platform permits you to right away and simply deploy a predictive records stream to the cloud. it is automatically accessible for scoring devoid of the need for your own software infrastructure.
The computing device studying carrier in IBM Cloud can make use of a SPSS Modeler circulate file (.str) for cloud-based mostly scoring. which you could use a Cloud Foundry App (like the one we're already the use of) to feed statistics into this move. We once again use Node-pink to install the mandatory information move. The machine getting to know service omits (or cuts off) the first and the remaining node from the stream and places the is still into the Node-pink facts circulation. we will are trying this once again to fully be mindful the concept.
evaluate the latest SPSS Modeler movement. It currently seems like this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image19.png" class="IBM-downsize" alt="" height="171" width="553"/>
think about how the circulate would seem to be if the primary and the last node are bring to a halt. This wouldn't work. The person enter node initially is solely technical (be aware, the Cloudant node needs an input) and devoid of the remaining node there isn't any output at all. What we are not looking for is the Cloudant node for the deployment since the facts flows in at once from the Cloud Foundry App. although, we nevertheless want the same structure of fields for the relaxation of the move. there is a straightforward aid for this.
word: We could delete the two person input and the Cloudant nodes at the left, but I all the time keep these fragments because it documents what I did before.
We prepared the SPSS flow file for deployment in the IBM Cloud laptop studying provider. Now we now have everything prepared for the deployment itself.
observe: don't click the link within the Route column.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image23.png" class="IBM-downsize" alt="" height="306" width="592"/>
The record now should still look corresponding to this. IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image29.png" class="IBM-downsize" alt="" height="280" width="1964"/>
install the information movement for live scoring
we will now join all of the pieces:
Log in to IBM Cloud and launch the Cloud Foundry App in Node-pink
First, we have to work in Node-purple once more.
substitute the present database with the online scoring database
We replace the present database storage with an internet scoring. if you're commonplace with the atmosphere, you could additionally establish each database storage and on-line scoring.
The final influence may still look like this.IBM.com/developerworks/library/iot-analyze-device-data-spss-modeler/image33.png" class="IBM-downsize" alt="" height="710" width="984"/>
After replacing the database with the cloud-primarily based scoring, we are able to examine to see if the scoring is working.
Make the JSON object more usable
transform the ensuing JSON object into a greater usable layout that carries handiest the imperative counsel.
ultimately, for later reporting you could wish to monitor the outcomes on an internet page, assemble it for a different database, or both. For the database, remember to know now what to do. For the web page, this could be an interesting tutorial for you to write yourself.Conclusion
This tutorial become born from my very own confusion about IoT, cloud, sensors, and especially the question "where is the magic?" Technically, we connected a sensor (our smartphones) to the cloud, saved the produced statistics within the cloud, and analyzed the facts using SPSS Modeler. We created a model this is able to come to a decision about "what happened with the device" simply by way of searching at the sensor information. And, we learned other ways to convey the created mannequin to lifestyles.
For me, the optimum researching became about all the interconnection on the IBM Cloud platform. earlier than IBM Cloud I did many an identical look at various setups and a whole lot of the configuration and simple setup steps have been puzzling. There are loads of moving constituents to agree with and it helps to know that IBM Cloud is one place to go and to get all started. furthermore, it was superb to use the statistical and predictive workbench SPSS Modeler in connection with a cloud setup.
What in the event you do next or how are you able to be trained extra? believe free to lengthen in all directions. the first logical step would be to make the model more accurate. believe working in your personal real-world illustration. if you discover your personal sensible case with real-world sensor facts, birth with something small to get a fundamental knowing of the facts and particularly the business case behind it. For me, there is fully no should work with or on information devoid of a correct goal or enterprise case.
From a enterprise standpoint, the query could be very down to earth, as an example, "how to get sensor records from older machines devoid of replacing them?" On the records aspect, ask your self "can we actually need all of the particular sensor information that all the world will produce in the subsequent years?" in that case, how can we decide what data is vital? One reply to this query can be to lengthen the architecture by using cognitive methods for you to also find in IBM Cloud.Downloadable materials linked topics
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The business will use “IBM SPSS Modeler”, a data evaluation application that gives potential analysis from mass quantity of facts to support more desirable decision making.
The group has conventionally aggregated incidents and problems statistics suggested through its operated vessels to “visualize” secure operation. to any extent further, it'll increase extra useful measures to evade incidents and examine the results by examining correlations and causal relationship of facts from numerous sources (for example, operation information, crewmember facts, vessel inspection facts, and so forth).
furthermore, it is going to build a new evaluation method the usage of the text mining function, for some elements of unstructured records, reminiscent of near misses gathered from crewmembers.
text mining function retrieves positive advice with the aid of dividing information comprised of files with the aid of note and paragraph, and examining the correlation of frequencies of words, frequency of favourite phrases, and time sequence.
ahead of this evaluation, the group held a 3-month trial starting in July 2017 and constructed evaluation models that investigate causal relationship of counsel on crewmembers, such as downtime problems and years of onboard event.
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C2090-012 Certification Brain Dumps Source : IBM SPSS Data Analysis for Business Partners v2
Test Code : C2090-012
Test Name : IBM SPSS Data Analysis for Business Partners v2
Vendor Name : IBM
Q&A : 25 Real Test Questions/Answers
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ACFE [1 Certification Exam(s) ]
ACI [3 Certification Exam(s) ]
Acme-Packet [1 Certification Exam(s) ]
ACSM [4 Certification Exam(s) ]
ACT [1 Certification Exam(s) ]
Administrat [1 Certification Exam(s) ]
Admission-Tests [12 Certification Exam(s) ]
ADOBE [90 Certification Exam(s) ]
AFP [1 Certification Exam(s) ]
AICPA [1 Certification Exam(s) ]
AIIM [1 Certification Exam(s) ]
Alcatel-Lucent [13 Certification Exam(s) ]
Alfresco [1 Certification Exam(s) ]
Altiris [3 Certification Exam(s) ]
American-College [2 Certification Exam(s) ]
Android [4 Certification Exam(s) ]
APC [2 Certification Exam(s) ]
APICS [1 Certification Exam(s) ]
Apple [69 Certification Exam(s) ]
Arizona-Education [1 Certification Exam(s) ]
ARM [1 Certification Exam(s) ]
Aruba [6 Certification Exam(s) ]
ASIS [2 Certification Exam(s) ]
ASQ [3 Certification Exam(s) ]
ASTQB [6 Certification Exam(s) ]
Autodesk [2 Certification Exam(s) ]
Avaya [85 Certification Exam(s) ]
Axis [1 Certification Exam(s) ]
Banking [1 Certification Exam(s) ]
BEA [5 Certification Exam(s) ]
BICSI [2 Certification Exam(s) ]
BlackBerry [17 Certification Exam(s) ]
BlueCoat [2 Certification Exam(s) ]
Business-Objects [11 Certification Exam(s) ]
Business-Tests [4 Certification Exam(s) ]
CA-Technologies [20 Certification Exam(s) ]
Certification-Board [9 Certification Exam(s) ]
Certiport [3 Certification Exam(s) ]
CheckPoint [31 Certification Exam(s) ]
CIPS [4 Certification Exam(s) ]
Cisco [270 Certification Exam(s) ]
Citrix [35 Certification Exam(s) ]
CIW [17 Certification Exam(s) ]
Cloudera [10 Certification Exam(s) ]
Cognos [19 Certification Exam(s) ]
College-Board [2 Certification Exam(s) ]
CompTIA [33 Certification Exam(s) ]
ComputerAssociates [6 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CWNP [12 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [7 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
ECCouncil [18 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [122 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [39 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [19 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [35 Certification Exam(s) ]
Fortinet [10 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [7 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
Google [4 Certification Exam(s) ]
GuidanceSoftware [2 Certification Exam(s) ]
H3C [1 Certification Exam(s) ]
HDI [9 Certification Exam(s) ]
Healthcare [3 Certification Exam(s) ]
HIPAA [2 Certification Exam(s) ]
Hitachi [27 Certification Exam(s) ]
Hortonworks [1 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [712 Certification Exam(s) ]
HR [1 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [20 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IBM [1491 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Informatica [2 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISACA [4 Certification Exam(s) ]
ISC2 [6 Certification Exam(s) ]
ISEB [24 Certification Exam(s) ]
Isilon [4 Certification Exam(s) ]
ISM [6 Certification Exam(s) ]
iSQI [7 Certification Exam(s) ]
Juniper [54 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [21 Certification Exam(s) ]
LSI [3 Certification Exam(s) ]
Magento [3 Certification Exam(s) ]
Maintenance [2 Certification Exam(s) ]
McAfee [8 Certification Exam(s) ]
McData [3 Certification Exam(s) ]
Medical [25 Certification Exam(s) ]
Microsoft [228 Certification Exam(s) ]
Mile2 [2 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [35 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
Nokia [2 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [9 Certification Exam(s) ]
Oracle [232 Certification Exam(s) ]
P&C [1 Certification Exam(s) ]
Palo-Alto [3 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [10 Certification Exam(s) ]
PEOPLECERT [4 Certification Exam(s) ]
PMI [15 Certification Exam(s) ]
Polycom [2 Certification Exam(s) ]
PostgreSQL-CE [1 Certification Exam(s) ]
Prince2 [6 Certification Exam(s) ]
PRMIA [1 Certification Exam(s) ]
PTCB [2 Certification Exam(s) ]
QAI [1 Certification Exam(s) ]
QlikView [1 Certification Exam(s) ]
Quality-Assurance [7 Certification Exam(s) ]
RACC [1 Certification Exam(s) ]
Real-Estate [1 Certification Exam(s) ]
RedHat [8 Certification Exam(s) ]
RES [5 Certification Exam(s) ]
Riverbed [8 Certification Exam(s) ]
RSA [13 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [3 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [78 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [9 Certification Exam(s) ]
SCP [6 Certification Exam(s) ]
SDI [3 Certification Exam(s) ]
See-Beyond [1 Certification Exam(s) ]
Siemens [1 Certification Exam(s) ]
Snia [6 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [132 Certification Exam(s) ]
Teacher-Certification [3 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [5 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [25 Certification Exam(s) ]
Vmware [51 Certification Exam(s) ]
Wonderlic [1 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [5 Certification Exam(s) ]
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