70-774 exam Dumps Source : Perform Cloud Data Science with Azure Machine Learning?
Test Code : 70-774
Test title : Perform Cloud Data Science with Azure Machine Learning?
Vendor title : Microsoft
: 37 real Questions
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KANATA, Ontario, Feb. 19, 2019 /PRNewswire/ -- HubStor these days announced modern cloud facts management capabilities that permit organizations to acquire spend of Microsoft Azure energetic directory's extended identity attributes in guidelines that control the storage, renovation, and safety of unstructured facts.
The HubStor cloud data management platform uniquely protects unstructured data workloads while incorporating a question-optimized mapping of data entry rights, clients, and neighborhood memberships. Now with prolonged identification metadata correlated into HubStor's quick-witted policy engine, organisations can streamline their administration of faultfinding counsel in the following approaches:
"We listen to the wants of their valued clientele intently as they build out the HubStor cloud data management platform," talked about Brad Janes, VP of Product management at HubStor. "improving HubStor's integration with Azure energetic listing and the HubStor policy engine to involve id metadata unlocks never-before-viewed data administration capabilities in the IT trade."
which you can connect with HubStor to start a subscription here: https://www.hubstor.web/installation-now.
HubStor is a number one innovator in cloud-primarily based storage utility. companies spend the HubStor cloud data administration platform to radically change their facts storage and insurance policy practices, backup their office 365 statistics, journal digital messages, permit cloud-tiering of file programs, and control long-term retention of unstructured statistics. HubStor is headquartered in Ottawa, Canada, and is a Microsoft Co-sell Prioritized and Gold ISV associate.
Elizabeth Lam, VP advertising and marketing
source HubStor Inc.connected links
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The driverless automobile has been a high-tech dream for many years. Now that broadband connectivity, cloud computing, and synthetic intelligence are increasingly available, independent vehicles may soundless fade mainstream in the proximate future, offered positive technical and regulatory milestones are reached. but another situation that need to subsist addressed before self-riding vehicles can attain crucial mass is the situation of facts. above all, the facts analysis and storage necessities of autonomous vehicles latest challenges past the capabilities of most existing immense information options.
autonomous cars generate a striking quantity of facts. Intel estimated one vehicle generates terabytes of information in eight hours of operation. distinctive photographs, radar/lidar, time-of-flight, accelerometers, telemetry, and gyroscope sensors generate data streams that ought to subsist analyzed with the intention to fulfill the calculations and adjustments required to soundly navigate a car. That analysis needs to befall in precise-time if the vehicle is to sustain with invariably changing using conditions (other vehicles or pedestrians relocating across the car, altering weather and light-weight conditions, traffic signals, and the like). These true-time performance necessities insinuate there is no time to upload information to a faultfinding server, conduct the necessary analytics, after which ship directions back to the vehicle for execution. records that's vital to soundly navigate the motor vehicle hold to subsist analyzed in the neighborhood via the car itself — pretty much, the car is an side machine in a cloud community.
no longer handiest does the motor vehicle should resolve statistics by itself, it ought to additionally learn to prefer and umpire between distinctive facts streams to establish those gold gauge example for evaluation at any given second to hold the car driving safely.
That ultimate requirement — the need to determine what facts is required to function an analysis — is tricky. whereas predefined filters can aid a motor vehicle's computing device getting to know routines subsist taught what statistics to spend and when to spend it, these filters are generated by means of human engineers, so that they can not subsist up to date in precise-time. as a consequence, an autonomous automobile will need to dash computing device discovering and analytics engines potent adequate to appreciate mission-important facts requiring immediate evaluation and action on their own, with out involving a human in the evaluation. once input from a person is required, resolution-making in line with information evaluation in upright time is without problems not possible.
We want analytics and machine getting to know algorithms for autonomous automobiles that can:
establish information in complete formats.
respect what records is required for mission-critical operations and function analysis of that records in the neighborhood.
Compress or aggregate non-important facts for importing to the cloud for future use.
schedule uploads of non-vital facts from the car to the cloud when much less lofty priced communications are available (as an instance, when the motor vehicle is parked overnight at domestic and may access the owner's Wi-Fi in its site of a metered cellular community).
be conscious of the passage to claim ancient information from the cloud so the AI can use it for future analytics.
The remaining bullet is above complete crucial. An self sufficient automobile company can subsist liable for storing mammoth amounts of information generated through vehicles operating everywhere, and a satisfactory deal of that data will probably don't hold any actual cost when at the beginning captured. although, that facts's value may subsist published in the future as the manufacturer's self sustaining using applications evolve and enhance. brand modern non-important facts can likewise subsist advantageous for future purposes, provided the records is correctly kept and simply purchasable. if they don't acquire plans in develop for the passage to acquire information accessible every time indispensable, self sufficient vehicle vendors dash the risk of creating a "dark data" issue. darkish records is the term used to define data property a firm collects but fails to engage capabilities of — as a result of they enact not know a passage to, or most likely forgot they've. This can subsist a particularly large vicissitude for self-riding automobiles as a result of the sheer volume of data they generate.
To handle the darkish data issue, self sufficient automobile providers need to circulate their statistics storage recommendations away from data warehouse models and adopt emerging data storage fashions fancy facts lakes. while an in depth examination of the divergence between a information warehouse and an information lake is past the scope of this article, as an sample the change between the two, evaluate a e-book with a library. With a ebook (records warehouse), a person has already determined what content is contained in that e-book and the passage it's formatted, while a library (records lake) allows you to deliver some thing content you desire in almost any structure. In other words, an information warehouse is a centralized platform for primary importing, exporting, and preprocessing of records gathered from a group of linked programs the spend of one statistics schema. an information lake is a distributed yet integrated information platform that helps schemaless (together with unstructured and structured) statistics and performs queries of statistics in true-time by using leveraging metadata to without dilatory find, seriously change, and cargo information between systems. data lakes' pilot for each structured and unstructured facts on the same platform is vital, as self reliant motor vehicle sensors generate datastreams in very distinctive codecs that can't without vicissitude subsist kept within the identical schema. other key ameliorations that distinguish a information lake from a data warehouse include:
Linking statistics between clusters is certainly primary for self sustaining cars, as it makes it feasible for for the mixing of separate datasets from diverse geographic places. motor vehicle OEMs are global agencies with multiple places of drudgery and statistics facilities scattered around the world. As more nations stream to assist autonomous automobiles, independent motor vehicle vendors will want to spend complete the facts generated by vehicles using locally in the self-riding AI and ML algorithms they spend to power their automobiles globally. As they see more companies enter the autonomous using market, the ones who will sooner or later win out over others could subsist those vendors optimum prepared to investigate statistics at the indigenous stage and those who hold cataloged their databases accurately — so future self sustaining functions can determine the information they need, once they want it.
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Bias comes in a number of types, complete of them doubtlessly damaging to the efficacy of your ML algorithm. Their Chief data Scientist discusses the supply of most headlines about AI failures here.
huge records ,autonomous cars ,actual-time statistics analysis ,laptop researching
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Azure Machine Learning Service is Microsoft’s latest offering for developers and data scientists in the custom cloud machine learning and abysmal learning category. Azure Machine Learning Service adds to a suite of Azure AI products that includes numerous AI toolkits, chatbot and IoT edge services, data science VMs, and pre-built services for vision, speech, language, knowledge, and search.InfoWorld
The AI toolkits involve Visual Studio Code Tools for AI, the older drag-and-drop Azure Machine Learning Studio, MMLSpark abysmal learning tools for Apache Spark, and the Microsoft Cognitive Toolkit, previously known as CNTK, which is being de-emphasized in favor of other machine learning and abysmal learning frameworks.
Using cloud resources for training abysmal learning models makes eminent sense in many cases. Using the cloud for training doesn’t necessarily replace the convenience and low operating cost of using your own computer for model building, especially if you hold one with lots of RAM and a capable GPU such as an Nvidia Titan RTX. On the other hand, using the cloud offers the opening to add compute resources as needed, potentially reducing the time it takes to complete your experiments and find a sufficiently accurate predictive model.
All of the major cloud services now proffer machine learning and abysmal learning development environments. On AWS, that’s primarily Amazon SageMaker, which I reviewed in May 2018. On the Google Cloud Platform, that’s primarily Cloud Machine Learning Engine and the beta Cloud AutoML. On the IBM cloud, that’s primarily IBM Watson Studio. I’ll compare Azure Machine Learning Services with Amazon SageMaker later in this review.
The tools used for data science are rapidly changing at the moment, according to Gartner, which said we’re in the midst of a “big bang” in its latest report on data science and machine learning platforms.
“The data science and ML market is hale and vibrant, with a broad blend of vendors offering a scope of capabilities,” Gartner says in its Magic Quadrant for Data Science and Machine Learning Platforms published January 28. “The market is experiencing a ‘big bang’ that is redefining not only who does data science and ML, but how it is done.”
The analyst group defines a data science platform as an integrated site where data scientists, national data scientists, and developers can secure complete of the core capabilities that they need to not only build data science application, but to embed them into existing commerce processes and manage and maintain them over time.
Data science and ML platforms must meet minimum requirements, and involve tools for
Integration and cohesion are keys, in Gartner’s view, and applications that simply bundle various packages and libraries – especially open source offerings — are not considered upright platforms.
While these core requirements set the stage for data science and ML platforms, there are immense differences in how the various suppliers secure there. Gartner notes that expert data scientists may prefer writing code in Python or R, while others fancy the ease of spend of data science notebooks, such as Jupyter. soundless other less technical folks prefer more intuitive point and click interfaces.Leader’s Quadrant
Gartner placed four vendors in the Leader’s Quadrant, including KNIME, RapidMiner, TIBCO Software, and SAS.
KNIME ranked highly in Gartner’s assessment as a result of stalwart uphold from customers, a broad product set, and having “one of the most balanced” visions in the market. The Zurich company’s product lineup – which consists of the open source KNIME Analytics offering and the commercial KNIME Server product — were lauded as the “Swiss Army Knife” of analytics. uphold for advanced features fancy abysmal learning, ease of spend by intermediate users, and integration with other packages were lauded. However, performance and scalability were seen as weaknesses, as well as limited traction in IoT.
Rapid Miner likewise ranked highly in the leader’s quadrant thanks to its balance between ease of spend and supporting sophisticated data science capabilities. The software supports abysmal learning technology and deploys to GPUs, and Gartner seemed to fancy how Rapid Miner’s delivers more transparency for machine learning deployments. Its integration with open source tools will subsist advantageous to data scientists, it says. The main concerns are around data prep and visualization; licensing and pricing; and model operationalization.
TIBCO made a immense glide up from the Challenger’s Quadrant by purchasing a scope of analytics properties, including Jaspersoft, Spotfire, Statistica, and Alpine Data, and integrating them into a lone cohesive platform. Gartner liked the end-to-end workflow integration that TIBCO delivers, and its IoT capabilities – particularly with the integration of streaming analytics. Potential concerns involve performance and stability, data management, and questions around operationalization.
SAS is a perennial contender on this list, and in fact has multiple platforms that were assessed. Its Enterprise Miner offering delivers strong, liable performance across a scope of metrics, while Visual Data Mining and Machine Learning (VDMML) had lofty scores for data prep and augmentation. lofty customer satisfaction levels and stalwart market presence bolster SAS’s position as a leader. But Gartner likewise listed some downsides of SAS’s approach, particularly around pricing and product coherence. The SAS EM user savor hasn’t kept up with expectations, and SAS’ approach to open source is a question tag for Gartner.Challenger’s Quadrant
The Challenger’s Quadrant was fairly empty, with just Alteryx and Dataiku occupying that space.
Alteryx dropped from the Leader’s Quadrant by maintaining its “ability to execute” (the Y axis) but losing some of its “completeness of vision” (the X axis). Gartner heralded the Irvin, California company’s national data science capabilities within an end-to-end pipeline. Despite its capabilities, the market perceives Alteryx as just a data preparation tool, which obscures its value, the analyst group says.
Dataiku‘s Data Science Studio (DSS) offering received lofty marks for the passage it fosters collaboration among different stakeholders, from data engineers to scientists. Gartner likewise liked the automation it brings to the machine learning workflow, as well as the management and monitoring of models once they’re in production. Some concerns involve scalability, pricing, and uphold for streaming analytics and IoT spend cases, it says.Visionaries Quadrant
The Visionaries Quadrant was crowded, with modern fewer than seven vendors jockeying for position.
Databricks, which inked $250 million in venture funding this week, impressed Gartner with its uphold for the full analytics life cycle, its uphold for hybrid cloud strategies, and its capability to uphold a variety of users. Users spoke highly of the Spark-based cloud offering, and documentation was a plus, per Gartner. Pricing and contract negotiations were potential feeble spots for Databricks, along with monitoring, management, and troubleshooting and debugging potential problems.
DataRobot debuted on the quadrant in the Visionaries, thanks to the fact that it “sets the gauge for augmented data science and ML,” Gartner says. Customers bask in a “strong experience,” which is helping the company to gain traction with an already solid installed base. Sales execution, pricing, scalability concerns, and the feasible commoditization of the “augmented analytics” space are cocerns.
H2O.ai, which held its H2O World conference this week, dropped from the Leader’s Quadrant in 2019 into the Visionaries Quadrant as a result of stalwart competition, and some concerns from customers about capabilities. The performance of its core open source machine learning components remain a force for H2O.ai, and Gartner was impressed with its GPU-based abysmal learning and the automated ML capabilities of Driverless AI. But a abrupt learning curve for non-developers, a lack of management capabilities, and a lack of data access and data prep features were concerns.
MathWorks made a huge lateral move, from the Challengers to the Visionaries Quadrant, thanks to “a remarkable strength” in serving the demands of its customers in asset-centric industries, according to Gartner (the company has a long legacy among manufacturers and engineering organizations). Its MATLAB offering was hailed for its “citizen engineer” capabilities, and integrated data prep and uphold for real-time streaming, abysmal learning, and simulation impressed the G man. Dings were vicissitude of spend by non-engineers, no uphold for Google Cloud Platform, and a lack of automated machine learning capabilities were downsides.
Microsoft scored well with its cloud-based offerings, which involve Azure Machine Learning, Azure Data Factory, Azure HDInsight, Azure Databricks, and Power BI. Gartner liked how Microsoft works with third-parties, in particular Databricks’ Spark offering. uphold for diverse data personas, including entry-level ML enthusiasts, was likewise a plus. Automation in the ML process was a concern, as was the coherence of complete the different tools. A lack of on-prem capabilities likewise limits its applicability.
IBM stays in the Visionaries Quadrant for 2019, but it has lost ground. Gartner praised the comprehensive nature of IBM’s Watson Studio offering, which serves expert and national data scientists. Integration of the SPSS modeler into Watson Studio was likewise praised. But the frequency that IBM rebrands products and shifts strategy is a concern to Gartner, as is the need to license multiple products to secure complete end-to-end capabilities.
Google did pretty well in the data science and ML platform ranking, thanks largely to the wide breadth of tools available on its cloud. Its core data science platform consists of Cloud ML Engine, Cloud AutoML, TensorFlow, and BigQuery ML. But Google likewise offers unique hardware, with the Tensor Processing Unit (TPU), crowdsourcing with Kaggle, and a scope of other offerings. Scalability and hasten are strengths. But a lack of end-to-end cohesion among the tools was a concern, as well as a lack of reusability. The lack of an on-prem offering was likewise a concern.Niche Players Quadrant
Four vendors found themselves in the Niche Players Quadrant.
SAP’s Predictive Analytics (PA) offering is tightly integrated with HANA, which makes it suitable for SAP HANA customers. The capability to process large HANA datasets and deploy models to SAP applications are strengths. So is SAP’s vision of a unified ML fabric, which is tied to its Leonardo Machine Learning Foundation. However, product coherence, a changing AI strategy, and the customer savor were marks against the German giant.
Domino Data Lab was downgraded from the Visionaries Quadrant, which reflected mostly a drop in its perceived faculty to execute. Gartner likes Domino’s product strategy, in particular its focus on collaboration and structure an end-to-end solution. Its faculty to integrate with open source and proprietary products was a bonus, as was its scalability. But Domino’s focus on expert data scientists leaves national data scientists wanting, according to Gartner, and it likewise lacks some data prep, automation, and augmentation capabilities.
Anaconda remained in the Niche Players category. Key force of the Anaconda product is its gain into the open source Python community, which continues to churn out data science innovation. Its capability to scale open source Python is likewise a plus. But the expertise needed to successfully wield the Anaconda platform is a caution, per Gartner, and the complexity of the Python “jungle” is likewise a concern. Reliance on the open source community likewise puts customers at a drawback when they need something specific (Gartner uses the sample of model operationalization), and the overall smooth of coherence is a downside.
Datawatch is a newcomer to this Magic Quadrant by passage of its January 2018 acquisition of Angoss, which has more than 20 years of savor in the field. Gartner praised the coherence and ease of spend of the Datawatch products, and marked the text analytics and optimization engine components as above average. Customer uphold was likewise a plus. A lack of data preparation capabilities dragged Datawatch’s score down, while the overall vision of the product and uncertainties raised by the acquisition were likewise mentioned.
What Gartner Sees In Analytic Hubs
Winners and Losers from Gartner’s Data Science and ML Platform Report
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CPP-Institue [2 Certification Exam(s) ]
CPP-Institute [1 Certification Exam(s) ]
CSP [1 Certification Exam(s) ]
CWNA [1 Certification Exam(s) ]
CWNP [13 Certification Exam(s) ]
Dassault [2 Certification Exam(s) ]
DELL [9 Certification Exam(s) ]
DMI [1 Certification Exam(s) ]
DRI [1 Certification Exam(s) ]
ECCouncil [21 Certification Exam(s) ]
ECDL [1 Certification Exam(s) ]
EMC [129 Certification Exam(s) ]
Enterasys [13 Certification Exam(s) ]
Ericsson [5 Certification Exam(s) ]
ESPA [1 Certification Exam(s) ]
Esri [2 Certification Exam(s) ]
ExamExpress [15 Certification Exam(s) ]
Exin [40 Certification Exam(s) ]
ExtremeNetworks [3 Certification Exam(s) ]
F5-Networks [20 Certification Exam(s) ]
FCTC [2 Certification Exam(s) ]
Filemaker [9 Certification Exam(s) ]
Financial [36 Certification Exam(s) ]
Food [4 Certification Exam(s) ]
Fortinet [13 Certification Exam(s) ]
Foundry [6 Certification Exam(s) ]
FSMTB [1 Certification Exam(s) ]
Fujitsu [2 Certification Exam(s) ]
GAQM [9 Certification Exam(s) ]
Genesys [4 Certification Exam(s) ]
GIAC [15 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 [30 Certification Exam(s) ]
Hortonworks [4 Certification Exam(s) ]
Hospitality [2 Certification Exam(s) ]
HP [750 Certification Exam(s) ]
HR [4 Certification Exam(s) ]
HRCI [1 Certification Exam(s) ]
Huawei [21 Certification Exam(s) ]
Hyperion [10 Certification Exam(s) ]
IAAP [1 Certification Exam(s) ]
IAHCSMM [1 Certification Exam(s) ]
IBM [1532 Certification Exam(s) ]
IBQH [1 Certification Exam(s) ]
ICAI [1 Certification Exam(s) ]
ICDL [6 Certification Exam(s) ]
IEEE [1 Certification Exam(s) ]
IELTS [1 Certification Exam(s) ]
IFPUG [1 Certification Exam(s) ]
IIA [3 Certification Exam(s) ]
IIBA [2 Certification Exam(s) ]
IISFA [1 Certification Exam(s) ]
Intel [2 Certification Exam(s) ]
IQN [1 Certification Exam(s) ]
IRS [1 Certification Exam(s) ]
ISA [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) ]
ITEC [1 Certification Exam(s) ]
Juniper [64 Certification Exam(s) ]
LEED [1 Certification Exam(s) ]
Legato [5 Certification Exam(s) ]
Liferay [1 Certification Exam(s) ]
Logical-Operations [1 Certification Exam(s) ]
Lotus [66 Certification Exam(s) ]
LPI [24 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 [69 Certification Exam(s) ]
Microsoft [374 Certification Exam(s) ]
Mile2 [3 Certification Exam(s) ]
Military [1 Certification Exam(s) ]
Misc [1 Certification Exam(s) ]
Motorola [7 Certification Exam(s) ]
mySQL [4 Certification Exam(s) ]
NBSTSA [1 Certification Exam(s) ]
NCEES [2 Certification Exam(s) ]
NCIDQ [1 Certification Exam(s) ]
NCLEX [2 Certification Exam(s) ]
Network-General [12 Certification Exam(s) ]
NetworkAppliance [39 Certification Exam(s) ]
NI [1 Certification Exam(s) ]
NIELIT [1 Certification Exam(s) ]
Nokia [6 Certification Exam(s) ]
Nortel [130 Certification Exam(s) ]
Novell [37 Certification Exam(s) ]
OMG [10 Certification Exam(s) ]
Oracle [279 Certification Exam(s) ]
P&C [2 Certification Exam(s) ]
Palo-Alto [4 Certification Exam(s) ]
PARCC [1 Certification Exam(s) ]
PayPal [1 Certification Exam(s) ]
Pegasystems [12 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) ]
PsychCorp [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 [15 Certification Exam(s) ]
Sair [8 Certification Exam(s) ]
Salesforce [5 Certification Exam(s) ]
SANS [1 Certification Exam(s) ]
SAP [98 Certification Exam(s) ]
SASInstitute [15 Certification Exam(s) ]
SAT [1 Certification Exam(s) ]
SCO [10 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 [7 Certification Exam(s) ]
SOA [15 Certification Exam(s) ]
Social-Work-Board [4 Certification Exam(s) ]
SpringSource [1 Certification Exam(s) ]
SUN [63 Certification Exam(s) ]
SUSE [1 Certification Exam(s) ]
Sybase [17 Certification Exam(s) ]
Symantec [134 Certification Exam(s) ]
Teacher-Certification [4 Certification Exam(s) ]
The-Open-Group [8 Certification Exam(s) ]
TIA [3 Certification Exam(s) ]
Tibco [18 Certification Exam(s) ]
Trainers [3 Certification Exam(s) ]
Trend [1 Certification Exam(s) ]
TruSecure [1 Certification Exam(s) ]
USMLE [1 Certification Exam(s) ]
VCE [6 Certification Exam(s) ]
Veeam [2 Certification Exam(s) ]
Veritas [33 Certification Exam(s) ]
Vmware [58 Certification Exam(s) ]
Wonderlic [2 Certification Exam(s) ]
Worldatwork [2 Certification Exam(s) ]
XML-Master [3 Certification Exam(s) ]
Zend [6 Certification Exam(s) ]