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Test Number : 70-775
Test name : Perform Data Engineering on Microsoft Azure HDInsight
Vendor name : Microsoft
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Perform Data Engineering on Microsoft Azure HDInsight education

Azure facts Lake Analytics and U-SQL | 70-775 Dumps and true test Questions with VCE drill Test

Key Takeaways
  • Azure statistics Lake Analytics, together with Azure information Lake Storage, is a key component of Microsoft’s Azure statistics Lake answer. 
  • at the moment, Azure information Lake Analytics will furthermore subsist used for batch workloads only. For streaming and taste processing workloads, alternate huge facts analytics options on Azure like HDInsight or Azure Databricks should subsist used.
  • Azure facts Lake Analytics introduces a brand new massive facts query and processing language known as U-SQL.
  • U-SQL combines the ideas and constructs both of SQL and C#; the vigour of U-SQL comes from the simplicity and declarative nature of SQL with the programmatic vigour of C# including affluent varieties and expressions.
  • U-SQL operates on unstructured facts kept in info and provides a schematized view on accurate of it. It additionally offers a regularly occurring metadata catalog gadget very akin to relational databases for structured data. 
  • notwithstanding huge data and Hadoop technologies are greater than a decade ancient now, tall data and tall data analytics are greater captious than ever. whereas the preliminary edition of Hadoop become simplest able to handle batch workloads, now Hadoop ecosystem has outfit for other utilize circumstances like structured information, streaming statistics, taste processing, laptop gaining learning of workloads and graph processing.

    while Hadoop ecosystem has a bunch of tools like Hive, Impala, Pig, Storm, and Mahout to deliver the finished set of facets, more moderen facts analytics framework like Spark maintain an built-in approach to tackle various kinds of workloads.

    Azure facts Lake Analytics, or ADLA, is among the more moderen tall statistics analytics engines. ADLA is Microsoft’s entirely managed, on-demand analytics provider on Azure cloud. together with Azure facts Lake Storage and HDInsight, Azure data Lake Analytics forms the comprehensive cloud hosted statistics lake and analytics offering from Microsoft. Azure data Lake Analytics introduces a brand new massive statistics question and processing language known as U-SQL. this article gives an contour of U-SQL language and how to utilize it in functions.

    Azure records Lake

    Azure information Lake is Microsoft’s data lake providing on Azure public cloud and is made out of discrete features including records storage, processing, analytics and other complementary functions like NoSQL shop, relational database, facts warehouse and ETL equipment.

    Storage functions
  • Azure records Lake Storage or ADLS - Azure records Lake Storage is a scalable cloud storage purposely built for analytics, in accordance with open HDFS average.
  • Azure Blob Storage – well-known goal, managed remonstrate storage for Azure.
  • Analytics & Processing features
  • Azure records Lake Analytics or ADLA– utterly managed, on-demand analytics service on Azure cloud. supports new U-SQL great records processing language apart from .internet, R and Python.
  • HDInsight– HDInsight gives managed Hadoop clusters operating on Azure and is according to Hortonworks information Platform (HDP) Hadoop distro. supports Hadoop ecosystem outfit together with Spark, Hive, Map in the reduction of, HBase, Storm, and Kafka.
  • Azure Databricks– Managed serverless analytics service in line with Azure Spark. supports a Jupyter/ iPython/Zeppelin like computer experience, together with other communique elements, and supports Scala, Python, R and SQL.
  • Complementary services
  • Cosmos DB – The managed, serverless, multi modal NoSQL database provider on Azure.
  • Azure SQL Database – A managed, relational database as a service/DBaaS on Azure.
  • Azure SQL Datawarehouse – Cloud-based enterprise information Warehouse (EDW) respond running on Azure. It makes utilize of common dispensed systems and statistics warehousing concepts like hugely Parallel Processing (MPP), columnar storage, compression and so on. to subsist positive quick performance for advanced queries.
  • Azure evaluation carrier – a completely managed analytics engine on Azure; helps to construct semantic models on the cloud. It’s developed on common SQL Server analysis Server which is an on-premise analytics engine based on SQL Server. As of now, Azure evaluation service handiest supports Tabular fashions and doesn't advocate Multidimensional fashions (remember cubes?).
  • Azure records manufacturing unit – A cloud-based mostly ETL and facts integration service. It’s serverless and offers out-of-the-box connectors to 50+ cloud or on-premise programs/features like Azure Blob Storage, Cosmos DB, Azure SQL Database, on prem SQL Server/MySQL/PostgreSQL and even third birthday party services like SFDC, Dropbox and so on. it may well circulation facts between cloud capabilities, from on-premise techniques to cloud or vice versa.  
  • determine 1 under shows these a lot of cloud offerings from Microsoft on Azure cloud.

    [Click on the image to enlarge it]figure 1: features in Azure statistics Lake providing

    The huge information and statistics lake-primarily based application structure on Azure cloud platform is proven beneath in design 2.

    [Click on the image to enlarge it]

    figure 2: common massive statistics/statistics lake/ETL/analytics architecture on Azure

    U-SQL Introduction

    U-SQL is the huge data question and processing language for Azure facts Lake Analytics. It’s a new language created by Microsoft primarily for Azure facts Lake Analytics. U-SQL combines SQL-like declarative language with the programmatic vigour of C#, together with C# prosperous kinds and expressions. U-SQL offers the generic massive facts processing concepts comparable to "schema on read", "lazy assessment", custom processors and reducers. information engineers who maintain prior to now used languages like Pig, Hive and Spark would find similarity with those. developers with C# and SQL learning would learn U-SQL handy to gain learning of and commence with.

     determine 3: How U-SQL relates to C# and SQL

    though U-SQL uses many ideas and key terms from SQL language, it’s no longer ANSI SQL compliant. It adds unstructured file coping with capabilities using key phrases like EXTRACT and OUTPUT.presently, ADLA and U-SQL may furthermore subsist used for batch processing handiest. It doesn’t provide tide analytics or event processing potential.

    U-SQL concepts and Scripts
  • U-SQL question and processing common sense is written in information with ".usql" extension called U-SQL scripts. visual Studio IDE or Azure portal may well subsist used for authoring these scripts. A U-SQL project in visible Studio carries discrete scripts, code at the back of info and connected reference assemblies.
  • determine 4 beneath suggests a screenshot of a U-SQL venture in visual Studio IDE.

     determine 4: A U-SQL undertaking in visible Studio

  • U-SQL scripts result the common Extract/Retrieve, seriously change and cargo/Output sample (ETL) used with the aid of different massive data languages like Pig or Spark. it may extract statistics from text information (both unstructured text info and semi structured information like JSON or XML) and tables.
  • U-SQL imposes a schema whereas retrieving unstructured records from info – this helps in performing SQL-like operations of retrieved records.
  • Rowset is the simple statistics structure of U-SQL. It’s used throughout for extracting records from enter file/table, and performing transformation, as well as for writing to output vacation spot. Rowsets are unordered which helps Azure statistics Analytics Engine to parallelize the processing the utilize of diverse processing nodes.
  • U-SQL scripts can utilize types, operators and expressions from C#.
  • U-SQL scripts utilize SQL constructs like choose, the place, subsist a fraction of and other information definition (DDL) and facts manipulation language (DML). complete key words should subsist written in higher case only.
  • supports manage movement constructs like IF ELSE, however don't wait on whereas or For loop.
  • [Click on the image to enlarge it]

    determine 5: statistics circulation in U-SQL script

    what is required for U-SQL local development

    Microsoft gives an emulator-like setup for trying U-SQL and Azure records Lake on local desktop or desktop. For this, three accessories are required:

  • visual Studio 2017 or 2019
  • Azure SDK (edition 2.7.1 or greater) which comes up with the client facet SDKs to interact with Azure cloud capabilities and required for storage, compute and so forth.
  • Azure records Lake and tide Analytics tools for visual Studio (edition 2.four), which is a plugin for local U-SQL and Azure records Lake construction. when you deploy this, imperative Azure records Lake Analytics (and other) challenge templates can subsist introduced in visual Studio as proven below. pick U-SQL mission to start.
  • [Click on the image to enlarge it]

    determine 6: New assignment template screenshot

    First U-SQL script

    For the primary U-SQL script, we'd utilize a dataset that constitutes of the rating of restaurants in Bangalore, India. The uncooked statistics are in CSV information and maintain privilege here columns:

  • rest_id - pleasing identification of the restaurant
  • identify - identify of the restaurant
  • tackle - address of the restaurant
  • online_order - whether on-line ordering is available in the restaurant or now not
  • book_table - even if table booking options can subsist establish or now not
  • rate - common score of the restaurant out of 5
  • votes - complete number of scores for the restaurant
  • cell - phone number of the restaurant
  • region - regional during which the restaurant is found
  • rest_type - category of restaurant (e.g. informal eating, short Bites, delivery, Bakery, Dessert Parlor and so on.)
  • favorite_dish_id - identification of probably the most favourite dish of the restaurant
  • The below table indicates the sample facts.

    [Click on the image to enlarge it]figure 7: Restaurant ratings table with pattern information

    The below script reads restaurant ratings records from a CSV file and writes the equal facts to a TSV file. It doesn’t utilize a transformation step yet.

    // Script - RestaurantScript.usql // information is extracted from enter file (CSV) and kept in employees rowset variable @restaurant_ratings = EXTRACT rest_id int, name string, address string, online_order bool, book_order bool, expense double, votes int, cellphone string, region string, rest_type string, favorite_dish_id int FROM "/Samples/records/restaurants_ratings.csv" the usage of Extractors.Csv(); // No Transformation – extracted information is loaded as is to output file (TSV) OUTPUT @restaurant_ratings TO "/output/restaurants_out.tsv" the utilize of Outputters.Tsv();

    The scripts would write the complete restaurant rowsets to output file in a tab separated format.

    observe that C# datatypes are used here (e.g. string and never char/varchar as typically used in SQL). no longer only will they utilize the datatypes of C#, but expressions and the entire goodness of an eloquent programming language.

    U-SQL script with radically change Step // Script - RestaurantScript.usql // Variables for input and output file name and paths DECLARE @inputFile = "/Samples/information/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // statistics is extracted from input file (CSV) and saved in employees rowset variable @restaurant_ratings = EXTRACT rest_id int, name string, tackle string, online_order bool, book_order bool, rate double, votes int, phone string, site string, rest_type string, favorite_dish_id int FROM @inputFile using Extractors.Csv(skipFirstNRows:1); // skip first row which comprise headers // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = select identify.ToUpper() AS name, // changing the names to uppercase fee AS rating, online_order AS OnlineOrder, cell AS telephone, site AS place, rest_type AS class, favorite_dish_id AS FavoriteDishId FROM @restaurants_rating where cost > 4 && online_order == proper; // Load modified statistics to output dossierOUTPUT @bestOnlineRestaurants TO @outputFile using Outputters.Tsv(outputHeader:true); // Write column names/headers to output file extend U-SQL expression the usage of customized code

    U-SQL supports customized expressions written in C# code. The C# code resides in code behind information. word in the beneath diagram each and every .usql file has an linked .usql.cs file the site custom C# code resides. determine eight: U-SQL mission with assorted script and code behind information

    // Code at the back of C# file - RestaurantScript.usql.cs namespace UsqlApp1 public static category Helpers public static string FormatRestaurantName(string name, string area, string restaurantType) recrudesce name + " (" + restaurantType + ") - " + location; // celebrate that U-SQL doesn't yet aid new C# 7.0 string interpolation // recrudesce $"identify ( restaurantType ) - place"; // Script - RestaurantScript.usql // Variables for enter and output file identify and paths DECLARE @inputFile = "/Samples/information/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // statistics is extracted from input file (CSV) and saved in personnel rowset variable @restaurant_ratings = EXTRACT rest_id int, name string, handle string, online_order bool, book_order bool, expense double, votes int, cell string, region string, rest_type string, favorite_dish_id int FROM @inputFile using Extractors.Csv(skipFirstNRows:1); // pass first row which comprise headers // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = pick USQLApp1.Helpers.FormatRestaurantName(identify, region, rest_type) AS name, rate AS score, online_order AS OnlineOrder, cellphone AS telephone, favorite_dish AS FavoriteDish FROM @restaurant_ratings the site fee > 4 && online_order == genuine; // Load converted information to output dossierOUTPUT @bestOnlineRestaurants TO @outputFile the utilize of Outputters.Tsv(outputHeader:genuine); // Write column names/headers to output file U-SQL script performing joins

    U-SQL supports joins between two diverse datasets. It gives inner subsist a fraction of, Outer subsist a fraction of, cross join, and many others.In here code snippet, they role internal join between a eating places dataset and dish elements dataset.

    // Script - RestaurantScript.usql DECLARE @inputFile = "/Samples/statistics/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // records is extracted from input file (CSV) and saved in personnel rowset variable @restaurant_ratings = // Code not proven for brevity. exact identical code as above example // Transformation Step: Columns are renamed and no of rows are filtered @bestOnlineRestaurants = // Code not shown for brevity. exact equal code as above illustration

    Now, they would want statistics about dishes and their constituents. notwithstanding this information typically would subsist present in an exterior source, they might utilize an in-memory rowset privilege here.

    // Declare an in-memory rowset for dish constituents containing dish identity, identify of dish and // materials. @dish_ingredients = choose * FROM (VALUES (1, "Biryani", "Rice, Indian spices, vegetables, Meat, Egg, Yoghurt, Dried Fruits"), (2, "Masala Dosa", "rice, husked black gram, mustard seeds, fenugreek seeds, salt, vegetable oil, potatoes, onion, eco-friendly chillies, curry leaves, turmeric"), (3, "Cake", " sugar, butter, egg, cocoa, creme, salt") ) AS D(DishId, Dish, constituents); // role an inner subsist fraction of between @bestOnlineRestaurants and @dish_ingredients rowset @rs_innerJn = opt for, r.rating, i.Dish, i.additivesFROM @bestOnlineRestaurants AS r internal subsist fraction of @dish_ingredients AS i ON r.FavoriteDishId == i.DishId; // Write to output dossierOUTPUT @rs_innerJn TO @outputFile the utilize of Outputters.Tsv(outputHeader:authentic);

    This returns the eating places with greater ratings, along with the ingredient details of its favourite dish, which is retrieved via joining the restaurant particulars rowset with dish ingredient rowset through inner subsist a fraction of.

    [Click on the image to enlarge it]

    figure 9: U-SQL chore with dissimilar script and code in the back of data

    U-SQL script the usage of developed-in services

    U-SQL offers a host of constructed-in features together with aggregate capabilities, analytical capabilities, ranking capabilities etc. beneath are few samples.

    category of feature     example mixture functionsAVG, SUM, count, STDEV (general Deviation), MIN, MAX and so forth. Analytical purposesFIRST_VALUE, LAST_VALUE, LAG, LEAD, PERCENTILE_CONT etc. ranking applicationsRANK, DENSE_RANK, NTILE, ROW_NUMBER and so forth.

    within the below script, they are the usage of constructed-in combination features like MIN, MAX, AVG and STDEV on for restaurant.

    // Declare variables for input and output files DECLARE @inputFile = "/Samples/records/restaurants_raw_data.csv"; DECLARE @outputFile = "/output/restaurants_aggr.csv"; @restaurant_ratings = EXTRACT rest_id int, name string, tackle string, online_order bool, book_order bool, expense double, votes int, cell string, site string, rest_type string, favorite_dish_id int FROM @inputFile the utilize of Extractors.Csv(skipFirstNRows:1); @output = choose rest_type AS RestaurantType, MIN(expense) AS MinRating, MAX(price) AS MaxRating, AVG(rate) AS AvgRating, STDEV(price) AS StdDevRating FROM @restaurants_ratings neighborhood with the aid of rest_type; // Write to output dossierOUTPUT @output TO @outputFile the utilize of Outputters.Csv(outputHeader:real); U-SQL catalog

    to date, they now maintain focused on unstructured and semi-structured statistics being study from files and written to info. while one among U-SQL’s strengths is to operate on unstructured facts kept in info and supply a schematized view on excellent of unstructured statistics, it may well control structured statistics. It provides a commonplace metadata catalog device like Hive. below is a listing of primary objects supported with the aid of U-SQL:

  • Database: U-SQL helps databases similar to different huge information programs like Hive.
  • Database Schema: Database schemas neighborhood linked objects existing beneath a Database, precisely like relational databases.
  • Tables and Indexes: Tables are containers to hang structured records. Tables can hold columns of different statistics kinds. desk facts is kept in data. Tables supply extra merits above simply schematized views over unstructured data like indexing, partitioning desk facts into diverse buckets with every bucket backed up by pass of a file.
  • Views: U-SQL views are of two varieties – (i) views that are in keeping with a U-SQL table and (ii) views that point to a file and utilize EXTRACT to bag the facts.
  • capabilities: helps both scalar and desk valued capabilities.
  • approaches:  processes are comparable to functions however they don’t recrudesce any cost.
  • Assemblies: U-SQL helps storing .web assemblies which extends U-SQL scripts with custom expression.
  • Now, let’s drawl in their restaurant ranking illustration, they might like to further dissect eating places with low ratings. To enact so, we'd want to stream complete the restaurants with lower than a four ranking to a U-SQL table for additional analysis.

    U-SQL database, tables and indexes

    in the under example. they are able to create a U-SQL database which should subsist created inside the database with a schema and index key. They aren't creating a schema principally here, so the desk can subsist created below the default schema ‘dbo’ (bear in mind SQL Server?) inner the database.

    The beneath code illustration indicates a pass to create this table.

    // Script - RestaurantScript.usql DECLARE @inputFile = "/Samples/data/restaurants_ratings.csv"; DECLARE @outputFile = "/output/restaurants_out.tsv"; // data is extracted from enter file (CSV) and saved in employees rowset variable @restaurant_ratings = // Code now not proven for brevity. exact identical code as above instance // Transformation Step: Filter most effectual those restaurants with score below 4 @lowRatedRestaurants = opt for rest_id AS RestaurantId, identify AS identify, cost AS score, online_order AS OnlineOrder, cellphone AS mobilephone, region AS vicinity, rest_type AS class, favorite_dish_id AS FavoriteDishId FROM @restaurants_ratings where expense < 4; // Insert Low rated restaurant details to U-SQL Catalog // Create the database if it does not exist alreadyCREATE DATABASE IF now not EXISTS RestaurantsDW; USE RestaurantsDW; // Drop the desk if it exists DROP desk IF EXISTS dbo.LowRatedRestaurants; // Create the desk by using specifying the column schema and index CREATE desk dbo.LowRatedRestaurants( RestaurantId int, identify string, INDEX idx CLUSTERED (name DESC) allotted with the aid of HASH(name), score double, OnlineOrder bool, cellphone string, vicinity string, class string, FavoriteDishId int ); // Insert the rowset information to the U-SQL desk created just before INSERT INTO dbo.LowRatedRestaurants opt for * FROM @lowRatedRestaurants; U-SQL views

    U-SQL views are similar to database views– they don't physically shop the statistics and supply a view over data stored in table or info. Views may well subsist based on table or in keeping with an extraction over information.

    The illustration script below shows how to create a view that’s in line with an extraction.

    USE DATABASE RestaurantsDW; // Delete the View if it already exists DROP VIEW IF EXISTS RestaurantsView; // Create the View in line with an extraction CREATE VIEW RestaurantsView AS EXTRACT rest_id int, name string, handle string, online_order bool, book_order bool, cost double, votes int, telephone string, area string, rest_type string, favorite_dish_id int FROM "/Samples/records/restaurants_raw_data.csv" the usage of Extractors.Csv(skipFirstNRows:1); // bypass first row which hold headers

    To rush the view here code is used:

    @influence = opt for * FROM RestaurantsDW.dbo.RestaurantsView; OUTPUT @outcomes TO "/output/Restaurants_View.csv" the usage of Outputters.Csv(); U-SQL table valued capabilities (TVF)

    U-SQL helps each scalar feature and desk valued feature (TVF). features assume zero to many arguments and recrudesce either a lone scalar expense or a desk, which is a dataset made from columns and rows.

    The under code snippet shows first how to create a TVF and then how to invoke it. It takes a lone parameter and returns a table.

    CREATE DATABASE IF not EXISTS RestaurantsDW; USE DATABASE RestaurantsDW; DROP feature IF EXISTS tvf_SearchRestaurants; // Create the table Valued feature that accepts Restaurant classification as string // and returns a table that includes matched restaurant details. CREATE feature tvf_SearchRestaurants(@RestaurantType string) RETURNS @searchRestaurants table(rest_id int, name string, address string, online_order bool, book_order bool, rate double, votes int, mobile string, site string, rest_type string, favorite_dish_id int) AS start @allRestaurants = EXTRACT rest_id int, identify string, address string, online_order bool, book_order bool, expense double, votes int, mobilephone string, region string, rest_type string, favorite_dish_id int FROM "/Samples/information/restaurants_raw_data.csv" the utilize of Extractors.Csv(skipFirstNRows:1); // pass first row which comprise headers @searchRestaurants = select * FROM @allRestaurants the site rest_type == @RestaurantType; RETURN; end;

    Now let’s invoke the table valued characteristic they simply created and tide ‘Bakery’ as parameter– it might recrudesce the entire eating places that are of class Bakery.

    OUTPUT RestaurantsDW.dbo.tvf_SearchRestaurants("Bakery") TO "/output/BakeryRestaurants.csv" the utilize of Outputters.Csv(); Case examine

    here case study highlights using Azure facts Lake Analytics and U-SQL language in a multiyear, enormous, strategic digital transformation software. The consumer, a tall assurance important, over the 12 months bought assorted insurance organizations and brokers, and in consequence used dissimilar consumer engagement systems for interacting with clients over e mail, textual content/SMS, internet/mobile chat and calls (both inbound and outbound). on account of the fractured approach, it grew to subsist very complicated for the customer to dissect consumer interaction information.

    while the consumer launched into a taste to build an omni channel platform and an built-in contact seat for client carrier over a lot of channels (e mail, textual content, chat bot, contact middle voice calls), their immediate tactical alternative become to research data from quite a lot of sources for email, textual content/SMS, chat and design contact with logs.

    An Azure facts Lake-based mostly respond was developed to respond the immediate need of examining statistics from discrete systems, in different codecs. facts from numerous source systems maintain been moved to Azure data Lake shop and had been then analyzed the usage of Azure statistics Lake analysis and U-SQL.

  • Ingest – in the ingest section, unstructured and structured facts from two discrete sources (e mail/text/Chat statistics as well as muster Log) are moved to Azure using Azure records manufacturing unit ETL carrier.
  • shop – uncooked information is saved on Azure statistics Lake Storage/ADLS as flat information.
  • Analyze – various kinds of evaluation including filtering, joins, aggregation, windowing and many others. are carried out in U-SQL.
  • model and Serve – Analyzed facts is stored in structured tables for later consumption from energy BI/customized experiences by means of person.
  • [Click on the image to enlarge it]

    determine 10: Azure information Analytics Pipeline


    Azure facts Lake Storage and Analytics maintain emerged as a powerful option for performing massive data and analytics workloads in parallel with Azure HDInsight and Azure Databricks. notwithstanding it’s nevertheless in its early days and lacks streaming and taste processing capabilities, its power lies within the new U-SQL language which combines the simplicity and ubiquity of SQL with Mirosoft’s flagship, the potent C# language. also, Microsoft’s construction tools like visual Studio and local dev/test capability design it a magnificent competitor in tall data & analytics space.

    in regards to the author

    Aniruddha Chakrabarti has 19 years of adventure spread throughout method, consulting, product structure and IT capabilities. He has journey across capabilities including respond structure, presales, technology architecture, start management and application administration. As AVP of digital in Mphasis, Chakrabarti is liable for presales, answer, RFP/RFI and know-how structure of gigantic digital deals and courses. earlier than joining Mphasis he has performed quite a few leadership and architecture-concentrated roles at Accenture, Microsoft, target, Misys and Cognizant. His focus areas encompass cloud, massive data & analytics, AI/ML, NLP, IoT, allotted programs, microservices and DevOps.

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    List of Certification test Dumps

    3COM [8 Certification Exam(s) ]
    AccessData [1 Certification Exam(s) ]
    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) ]
    Admission-Tests [15 Certification Exam(s) ]
    ADOBE [93 Certification Exam(s) ]
    AFP [1 Certification Exam(s) ]
    AICPA [2 Certification Exam(s) ]
    AIIM [1 Certification Exam(s) ]
    Alcatel-Lucent [14 Certification Exam(s) ]
    Alfresco [1 Certification Exam(s) ]
    Altiris [3 Certification Exam(s) ]
    Amazon [7 Certification Exam(s) ]
    American-College [2 Certification Exam(s) ]
    Android [4 Certification Exam(s) ]
    APA [1 Certification Exam(s) ]
    APC [2 Certification Exam(s) ]
    APICS [2 Certification Exam(s) ]
    Apple [71 Certification Exam(s) ]
    AppSense [1 Certification Exam(s) ]
    APTUSC [1 Certification Exam(s) ]
    Arizona-Education [1 Certification Exam(s) ]
    ARM [1 Certification Exam(s) ]
    Aruba [8 Certification Exam(s) ]
    ASIS [2 Certification Exam(s) ]
    ASQ [3 Certification Exam(s) ]
    ASTQB [11 Certification Exam(s) ]
    Autodesk [2 Certification Exam(s) ]
    Avaya [107 Certification Exam(s) ]
    AXELOS [1 Certification Exam(s) ]
    Axis [1 Certification Exam(s) ]
    Banking [1 Certification Exam(s) ]
    BEA [6 Certification Exam(s) ]
    BICSI [2 Certification Exam(s) ]
    BlackBerry [17 Certification Exam(s) ]
    BlueCoat [2 Certification Exam(s) ]
    Brocade [4 Certification Exam(s) ]
    Business-Objects [11 Certification Exam(s) ]
    Business-Tests [4 Certification Exam(s) ]
    CA-Technologies [20 Certification Exam(s) ]
    Certification-Board [10 Certification Exam(s) ]
    Certiport [3 Certification Exam(s) ]
    CheckPoint [45 Certification Exam(s) ]
    CIDQ [1 Certification Exam(s) ]
    CIPS [4 Certification Exam(s) ]
    Cisco [326 Certification Exam(s) ]
    Citrix [48 Certification Exam(s) ]
    CIW [18 Certification Exam(s) ]
    Cloudera [10 Certification Exam(s) ]
    Cognos [19 Certification Exam(s) ]
    College-Board [2 Certification Exam(s) ]
    CompTIA [80 Certification Exam(s) ]
    ComputerAssociates [6 Certification Exam(s) ]
    Consultant [2 Certification Exam(s) ]
    Counselor [4 Certification Exam(s) ]
    CPP-Institute [4 Certification Exam(s) ]
    CSP [1 Certification Exam(s) ]
    CWNA [1 Certification Exam(s) ]
    CWNP [14 Certification Exam(s) ]
    CyberArk [2 Certification Exam(s) ]
    Dassault [2 Certification Exam(s) ]
    DELL [13 Certification Exam(s) ]
    DMI [1 Certification Exam(s) ]
    DRI [1 Certification Exam(s) ]
    ECCouncil [23 Certification Exam(s) ]
    ECDL [1 Certification Exam(s) ]
    EMC [134 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 [41 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 [16 Certification Exam(s) ]
    Foundry [6 Certification Exam(s) ]
    FSMTB [1 Certification Exam(s) ]
    Fujitsu [2 Certification Exam(s) ]
    GAQM [10 Certification Exam(s) ]
    Genesys [4 Certification Exam(s) ]
    GIAC [15 Certification Exam(s) ]
    Google [6 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 [5 Certification Exam(s) ]
    Hospitality [2 Certification Exam(s) ]
    HP [764 Certification Exam(s) ]
    HR [4 Certification Exam(s) ]
    HRCI [1 Certification Exam(s) ]
    Huawei [33 Certification Exam(s) ]
    Hyperion [10 Certification Exam(s) ]
    IAAP [1 Certification Exam(s) ]
    IAHCSMM [1 Certification Exam(s) ]
    IBM [1547 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 [9 Certification Exam(s) ]
    ITEC [1 Certification Exam(s) ]
    Juniper [67 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 [9 Certification Exam(s) ]
    McData [3 Certification Exam(s) ]
    Medical [68 Certification Exam(s) ]
    Microsoft [402 Certification Exam(s) ]
    Mile2 [3 Certification Exam(s) ]
    Military [1 Certification Exam(s) ]
    Misc [3 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 [3 Certification Exam(s) ]
    Network-General [12 Certification Exam(s) ]
    NetworkAppliance [42 Certification Exam(s) ]
    NetworkAppliances [1 Certification Exam(s) ]
    NI [1 Certification Exam(s) ]
    NIELIT [1 Certification Exam(s) ]
    Nokia [7 Certification Exam(s) ]
    Nortel [130 Certification Exam(s) ]
    Novell [37 Certification Exam(s) ]
    OMG [10 Certification Exam(s) ]
    Oracle [315 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) ]
    PCI-Security [1 Certification Exam(s) ]
    Pegasystems [17 Certification Exam(s) ]
    PEOPLECERT [4 Certification Exam(s) ]
    PMI [16 Certification Exam(s) ]
    Polycom [2 Certification Exam(s) ]
    PostgreSQL-CE [1 Certification Exam(s) ]
    Prince2 [7 Certification Exam(s) ]
    PRMIA [1 Certification Exam(s) ]
    PsychCorp [1 Certification Exam(s) ]
    PTCB [2 Certification Exam(s) ]
    QAI [1 Certification Exam(s) ]
    QlikView [2 Certification Exam(s) ]
    Quality-Assurance [7 Certification Exam(s) ]
    RACC [1 Certification Exam(s) ]
    Real Estate [1 Certification Exam(s) ]
    Real-Estate [1 Certification Exam(s) ]
    RedHat [8 Certification Exam(s) ]
    RES [5 Certification Exam(s) ]
    Riverbed [9 Certification Exam(s) ]
    RSA [16 Certification Exam(s) ]
    Sair [8 Certification Exam(s) ]
    Salesforce [7 Certification Exam(s) ]
    SANS [1 Certification Exam(s) ]
    SAP [98 Certification Exam(s) ]
    SASInstitute [15 Certification Exam(s) ]
    SAT [2 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 [137 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 [7 Certification Exam(s) ]
    Veeam [2 Certification Exam(s) ]
    Veritas [33 Certification Exam(s) ]
    Vmware [71 Certification Exam(s) ]
    Wonderlic [2 Certification Exam(s) ]
    Worldatwork [2 Certification Exam(s) ]
    XML-Master [3 Certification Exam(s) ]
    Zend [6 Certification Exam(s) ]

    References : : :
    Calameo : Certification test dumps

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