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70-768 - Developing SQL Data Models - Dump Information

Vendor : Microsoft
Exam Code : 70-768
Exam Name : Developing SQL Data Models
Questions and Answers : 37 Q & A
Updated On : October 15, 2018
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70-768 Questions and Answers

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70-768 Developing SQL Data Models

Study Guide Prepared by Killexams.com Microsoft Dumps Experts


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70-768 exam Dumps Source : Developing SQL Data Models

Test Code : 70-768
Test Name : Developing SQL Data Models
Vendor Name : Microsoft
Q&A : 37 Real Questions

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Microsoft Microsoft Developing SQL Data

setting up the Database Server - degree 2 of the Stairway to SQL Server on Linux | killexams.com Real Questions and Pass4sure dumps

in this second stage we’ll installation SQL Server on our newly created Ubuntu Server. we can cover the installation of the command line tools and assess our example is working.

in case you don’t have an Ubuntu example, make certain to examine stage 1 on a way to get began.

prerequisites

before installing SQL Server 2017 on Linux, we need to make certain to satisfy just a few prerequisites, as documented by Microsoft:

memory 2 GB File system XFS or EXT4 (different file techniques, corresponding to BTRFS, are unsupported) Disk area6 GB Processor velocity 2 GHz Processor cores 2 cores Processor classification x64-compatible best

Please notice, that if you use community File gadget (NFS) far off shares in creation, there are a number of additional requirements:

  • Use NFS version 4.2 or larger. Older models of NFS do not assist required facets, such as fallocate and sparse file creation, common to modern file programs.
  • find best the /var/choose/mssql directories on the NFS mount. different files, such because the SQL Server equipment binaries, aren't supported if moved to the NFS share.
  • make certain that NFS customers use the 'nolock' choice when mounting the far off share.

    SQL Server installation

    The standard installing method is easy: as soon as the server is checked and meets the prerequisites for the installation, we add a number of configuration items for the apt installer and then use this to installation SQL Server. You might note this is distinct than getting media from Microsoft for an setting up on windows.

    We start by way of importing the GPG key. We do this through retrieving the important thing with the wget utility from the Microsoft location. We circulate this to the apt-get command. This have to be run as a superuser, so we encompass the sudo choice. The command is:

    wget -q https://programs.microsoft.com/keys/microsoft.asc | sudo apt-key add -

    The parameter –q0 (quiet) skill that we want no output from the wget command, while the add choice of the apt-key command will add the important thing contained in the file passed because the argument (during this case with the minus character) to the keys repository.

    that you would be able to see the consequences under, with an good enough to determine this completed sucessfully:

    next, let’s add the Microsoft SQL Server Ubuntu repository to the programs supply listing using the command  add-apt-repository. This command without problems appends the special repository into the /and so on/apt/sources.list file.

    sudo add-apt-repository “$(wget –q https://packages.microsoft.com/config/ubuntu/16.04/mssql-server-2017.checklist)”

    Please note that here's the link to the Cumulative Updates (CU) repository, that installs the newest SQL Server version accessible. that you could additionally choose the GDR repository (mssql-server-2017-gdr), which install crucial updates, in case you already have a SQL Server installing and want to practice  hotfixes.

    We replace the native repository by using operating the command apt-get replace. Now, are you in a position? Let’s birth with the installing. We do that by way of working

    sudo apt-get install –y mssql-server

    This command will down load the packages from the Microsoft repository (along with all the required dependencies) and unpack them in the /decide/mssql directory. The down load is set 187MB in size and will take the time, counting on your cyber web connection.

    After the SQL Server binaries and libraries had been copied to the server, remember to come to be with this message:

    To comprehensive the installation, we must configure SQL Server earlier than its first run. The mssql-conf is a Python script so you might use for the configuration of SQL Server as a substitute of the windows program, SQL Server Configuration supervisor. this is also used to perform put up-installing illustration-level configurations like changing the optimum reminiscence or parallelism settings.

    Now, as counseled, let’s run the mssql-conf script to configure SQL Server. the first question you are required to answer is which version of SQL Server you need to run. which you can select probably the most three free variations (comparison, Developer, express) or, you probably have bought a daily license, one of the crucial paid variations (web, common, commercial enterprise, Enteprise Core), according with your license.

    next, you ought to settle for the license phrases to continue.

    and at last, you are required to provide a relaxed password for the sa consumer.

    That’s all!

    installation the SQL Server Command-line equipment

    after you have achieved the SQL Server engine installation, in case you wish to create a database or run a query you deserve to join with a tool it truly is able to working Transact-SQL commands. You may use SQL Server management Studio on home windows, SQL Server Operations Studio on home windows/Linux/Mac or command line tools like sqlcmd or bcp (each home windows and Linux). we are able to install the command line tools here.

    First, import the GPG keys from public repository with the command:

    curl https://packages.microsoft.com/keys/microsoft.asc | sudo apt-key add -

    The curl command is very comparable to the wget command and can be used to download a file the use of web protocols like FTP, HTTP or HTTPS. this is the output of the command:

    subsequent, register the Microsoft Ubuntu repository with curl:

    curl https://programs.microsoft.com/config/ubuntu/sixteen.04/prod.record | sudo tee /and so on/apt/sources.list.d/msprod.listing

    The tee command reads from normal enter and writes to ordinary output and to information. during this case we are studying the content material from the prod.checklist file and redirecting it by the use of pipe to the msprod.listing file.

    Now, replace the sources record and run the package setting up command. observe that, at the side of the equipment, you are installation additionally the unixODBC developer kit, that carries the libraries mandatory for the connection to the database.Run the command:

    sudo apt-get update

    to refresh the applications checklist. Then launch the command to download and unpack the newest edition of the tools and libs.

    sudo apt-get set up mssql-tools unixodbc-dev

    This command will replica the applications from Microsoft repository to disk and unpack it. this is a small kit, about 34MB in size.

    all over the installing, the software will ask you a few instances if you settle for the terms (one for the tools and one for the ODBC building equipment):

    as the final step, add the path, /opt/mssql-equipment/bin, to the path ambiance variable. This lets you run the equipment from a logged in session or from a script.

    in case you should run sqlcmd/bcp from the bash shell, add the direction to the route variable  in the ~/.bash_profile file:

    if you need to run these instructions additionally from a non-interactive/non-login session, you ought to alter the path within the ~/.bashrc file:

    connect with SQL Server

    We’re just about accomplished. Let’s determine if SQL Server service is running continually and take a look at to join the usage of sqlcmd, exactly in the same way we're used to join in a windows ambiance.

    To verify if the provider is working that you could run right here command:

    systemctl fame mssql-server

    Systemctl is a Linux command to assess on a technique/carrier. The output of this command includes a couple of tips concerning the working illustration of SQL Server, like the variety of projects that are operating, the memory used through SQL Server and the percent of CPU in use.

    if you must troubleshoot your SQL Server installing and don’t have the equipment installed,  which you can study the SQL Server errorlog without delay from the command line using the command:

    cat /var/opt/mssql/log/errorlog

    ok, the service is up and running. Let’s are attempting to join the use of sqlcmd and run a simple question:

    I chose to run the command with out passing the parameter –P to provide the password in clear method. you can certainly to that, for example to run this command from a script, however is terribly unsecure and will be avoided.

    Congratulations! we've our SQL Server on Linux up and running!

    in the subsequent level, we’ll discover a way to add additional disks and separate records, transaction logs and tempdb records info among them, following one of the crucial widespread most reliable practices and procure more desirable performances.

    get pleasure from!


    5 Keys to Modernizing windows development | killexams.com Real Questions and Pass4sure dumps

    down load the blueprint that may take an organization of any maturity degree the entire way up to commercial enterprise-scale continuous birth using a mixture of Automic release Automation, Automic’s 20+ years of business automation event, and the proven tools and practices the enterprise is already leveraging.

    Docker has emerged as a number one average for utility packaging and portability, garnering business-extensive guide from Microsoft, AWS, Google, pink Hat, and others. hobby in Docker on home windows is accelerating, driven partially by using Microsoft’s assist of SQL Server 2017 on Linux containers.

    Windocks become launched in 2016 as an independent port of Docker’s open-source undertaking to home windows, with SQL Server database cloning enabling start of TB databases in seconds. These capabilities have generated frequent commercial enterprise adoption and a transforming into figuring out of the keys to success for a success home windows application development modernization. For full disclosure, i am a co-founder at Windocks, and the observations outlined listed here are from our help of those dissimilar capabilities. 

  • Docker is foundational for modernizing home windows software development and significant to Microsoft’s approach for a shared windows and Linux building platform. Containers are additionally emphasized for SQL Server 2017 on Linux, and for Kubernetes orchestration, which many view because the future for commercial enterprise computing.
  • Full stack assist with SQL Server is a requirement and a challenge.   front-conclusion purposes make use of Git with continuous Integration trying out it truly is achieved in seconds.  SQL Server environments, then again, require hours or days to provision and count on shared environments.   The introduction of SQL Server database cloning simplifies the cozy birth of TB class databases in seconds, with isolated containers.  using this combination SQL Server can now be integrated into DevOps and continual Integration procedures, with SQL Server containers or generic circumstances. 
  • protection is a true-tier consideration and is executed with immutable, auditable facts images.   SQL database pictures and clones must also support enterprise encryption, encrypted credentials, and person/neighborhood position-based mostly entry.
  • Multi-cloud and on-premise assist enhance supplier independence and protection.  a hallmark of a contemporary design includes assist for scaling right down to run on a consumer’s desktop.
  • Extensibility is performed with commonplace APIs mixed with native home windows safety and infrastructure.   SQL Server environments provisioned during the Docker API are with ease built-in with purple Hat OpenShift, or Pivotal Cloud Foundry and PKS, Kubernetes, and different platforms.   Extensibility contains latest storage methods, windows authentication, and SQL Server encryption.
  • The outlook for Docker-based mostly modernization of home windows full stack construction has evolved enormously in the past two years, to consist of SQL Server database cloning and records delivery to each containers and cases. relaxed, immutable facts and container photographs can be sourced from storage arrays and/or SQL Server backups, with TB database clones delivered in seconds. Many companies are using this mixture on AWS and Azure, complimenting AWS RDS and SQL Azure. Others are operating on-premise. 

    home windows agencies have in no way been enhanced positioned to modernize full-stack home windows construction with SQL Server containers and database cloning. 

    down load the ‘functional Blueprint to continual start’ to learn how Automic unencumber Automation can aid you start or continue your enterprise’s digital transformation.

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    70-768 Developing SQL Data Models

    Study Guide Prepared by Killexams.com Microsoft Dumps Experts


    Killexams.com 70-768 Dumps and Real Questions

    100% Real Questions - Exam Pass Guarantee with High Marks - Just Memorize the Answers



    70-768 exam Dumps Source : Developing SQL Data Models

    Test Code : 70-768
    Test Name : Developing SQL Data Models
    Vendor Name : Microsoft
    Q&A : 37 Real Questions

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    Developing SQL Data Models

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    Building a Regression Model Using Oracle Data Mining | killexams.com real questions and Pass4sure dumps

    In this article, I will do regression analysis with Oracle data mining. 

    Data science and machine learning are very popular today. But these subjects require extensive knowledge and application expertise. We can solve these problems with various products and software that have been developed by various companies. In Oracle, methods and algorithms for solving these problems are presented to users with the DBMS_DATA_MINING package.

    With the DBMS_DATA_MINING package, we can create models such as clustering, classification, regression, anomaly detection, feature extraction, and association. We can interpret efficiency with the models we create. The results we obtain from these models can be put into our business scenario.

    The DBMS_DATA_MINING package does not come up by default on the Oracle database. For this reason, it's necessary to install this package first. You can set up your database with Oracle data mining by following this link.

    With the installation of the Oracle data mining package, three new dictionary tables are created:

    SELECT * FROM ALL_MINING_MODELS; SELECT * FROM ALL_MINING_MODEL_SETTINGS; SELECT * FROM ALL_MINING_MODEL_ATTRIBUTES;

    The ALL_MINING_MODELS table contains information about all the models.

    The ALL_MINING_MODELS_SETTINGS and ALL_MINING_MODELS_ATTRIBUTES tables contain parameters and specific details about these models.

    Now, let's prepare an easily understood data set to do market basket analysis.

    We need a dataset to do the example. We will do this example through the BOSTON_HOUSING dataset we downloaded from kaggle.

    Let's first examine the BOSTON_HOUSING dataset.

    Column Name Description Data Type crim per capita crime rate by town. Number zn proportion of residential land zoned for lots over 25,000 sq.ft. Number indus proportion of non-retail business acres per town. Number chas Charles River dummy variable (= 1 if tract bounds river; 0 otherwise). Number nox nitrogen oxides concentration (parts per 10 million). Number rm average number of rooms per dwelling. Number age proportion of owner-occupied units built prior to 1940. Number dis weighted mean of distances to five Boston employment centres. Number rad index of accessibility to radial highways. Number tax full-value property-tax rate per $10,000. Number ptratio pupil-teacher ratio by town. Number black 1000(Bk – 0.63)^2 where Bk is the proportion of blacks by town. Number lstat lower status of the population (percent). Number medv median value of owner-occupied homes in $1000s.

    Number

    Now that we have reviewed the details with our dataset, let's load the BOSTON_HOUSING that we downloaded to our Oracle database.

    First, create the Oracle table in which we will load the data set (train.csv) that we downloaded.

    CREATE TABLE BOSTON_HOUSING ( ID NUMBER, CRIM NUMBER, ZN NUMBER, INDUS NUMBER, CHAS NUMBER, NOX NUMBER, RM NUMBER, AGE NUMBER, DIS NUMBER, RAD NUMBER, TAX NUMBER, PTRATIO NUMBER, BLACK NUMBER, LSTAT NUMBER, MEDV NUMBER );

    Now that we have created our table, we will load the dataset we downloaded as CSV into the table; we have multiple methods to do this:

  • Using Oracle External Table

  • Using Oracle SQL Loader

  • Using SQL-PL/SQL editors (Oracle SQL Developer, Toad, PL/SQL Developer, etc.)

  • I will load the dataset with the editor that I am using. I use Toad as an editor. With Toad, you can perform the data loading process by following the steps below.

    Database > Import > Import Table Data

    You may not be using this editor because Toad is paid. This feature is available to other editors, so you can easily do this with other editors. For example, with Oracle SQL Developer for free, you can load data as follows using Oracle SQL Developer.

    SELECT * FROM BOSTON_HOUSING;

    We have completed the dataset installation process. 

    When we observe the data, we see the details according to the various characteristics of the houses. Each row contains information on the specific characteristics of the house. Our basic parameters for regression analysis are as presented in this table. In this table, we predict the result of regression analysis. The MEDV column is the target variable that we will use in this analysis.

    Before starting the regression analysis, I will give some information about the algorithm used to better understand the subject.

    The DBMS_DATA_MINING package is implemented by the Generalized Linear Model algorithm. To use this algorithm, we need to define some parameters. These parameters are as follows:

    Now let's create the table where we will read the algorithm settings.

    CREATE TABLE SETTINGS_GLM AS SELECT * FROM TABLE (DBMS_DATA_MINING.GET_DEFAULT_SETTINGS) WHERE SETTING_NAME LIKE '%GLM%'; BEGIN INSERT INTO SETTINGS_GLM VALUES (DBMS_DATA_MINING.ALGO_NAME, 'ALGO_GENERALIZED_LINEAR_MODEL'); INSERT INTO SETTINGS_GLM VALUES (DBMS_DATA_MINING.PREP_AUTO, 'ON'); INSERT INTO SETTINGS_GLM VALUES ( DBMS_DATA_MINING.GLMS_RIDGE_REGRESSION, 'GLMS_RIDGE_REG_DISABLE'); INSERT INTO SETTINGS_GLM VALUES ( DBMS_DATA_MINING.ODMS_MISSING_VALUE_TREATMENT, 'ODMS_MISSING_VALUE_MEAN_MODE'); COMMIT; END;

    Yes, we created the table to read the algorithm parameters. Now we can go on creating our model.

    CREATE OR REPLACE VIEW VW_BOSTON_HOUSING AS SELECT * FROM BOSTON_HOUSING; BEGIN DBMS_DATA_MINING.CREATE_MODEL( model_name => 'MD_GLM_MODEL', mining_function => DBMS_DATA_MINING.REGRESSION, data_table_name => 'VW_BOSTON_HOUSING', case_id_column_name => 'ID', target_column_name => 'MEDV', settings_table_name => 'SETTINGS_GLM'); END;

    Our model has occurred, now look at the details of our model from the dictionary.

    SELECT MODEL_NAME, ALGORITHM, COMMENTS, CREATION_DATE, MINING_FUNCTION, MODEL_SIZE FROM ALL_MINING_MODELS WHERE MODEL_NAME = 'MD_GLM_MODEL';

    SELECT SETTING_NAME, SETTING_VALUE FROM ALL_MINING_MODEL_SETTINGS WHERE MODEL_NAME = 'MD_GLM_MODEL';

    The Anova table of the Regression model we have created can be accessed as follows.

    SELECT * FROM TABLE (DBMS_DATA_MINING.GET_MODEL_DETAILS_GLM ('MD_GLM_MODEL'));

    Let's see how we can test with our model now. First of all, we create the table that we need to load the test.csv data that we have downloaded from kaggle and we import the data in it.

    CREATE TABLE BOSTON_HOUSING_TEST ( ID NUMBER, CRIM NUMBER, ZN NUMBER, INDUS NUMBER, CHAS NUMBER, NOX NUMBER, RM NUMBER, AGE NUMBER, DIS NUMBER, RAD NUMBER, TAX NUMBER, PTRATIO NUMBER, BLACK NUMBER, LSTAT NUMBER, MEDV NUMBER );

    Database –> Import –> Import Table Data (test.csv)

    SELECT * FROM BOSTON_HOUSING_TEST;

    Now run the model that we created with the test data and look at the results.

    SELECT T.*, PREDICTION (MD_GLM_MODEL USING *) MODEL_PREDICT_RESPONSE FROM BOSTON_HOUSING_TEST T;


    Principled Technologies Tests OLTP and Data Analytics Performance of Dell EMC Ready Solutions for Microsoft SQL | killexams.com real questions and Pass4sure dumps

    Principled Technologies (PT) measured two types of database performance on Dell EMC Ready Solutions for Microsoft SQL, a preconfigured server-and-storage solution.

    Durham, NC, October 08, 2018 --(PR.com)-- Database applications drive many essential business functions, including online transaction processing (OLTP) for e-commerce and online analytical processing (OLAP) to generate insights for decision-making. Dell EMC Ready Solutions for Microsoft SQL is a preconfigured solution that companies can use to meet these needs without taking the time and effort to assemble, configure, and test hardware and software components.

    Principled Technologies set up Dell EMC Ready Solutions for Microsoft SQL that included four Dell EMC PowerEdge R740xd servers in a Microsoft Storage Spaces Direct (S2D) cluster environment running SQL Server 2017. They first ran an OLTP workload and then a workload processing OLAP data warehouse queries.

    According to the report, the Dell EMC Ready Solutions for Microsoft SQL “achieved levels of performance that could serve a company with growing database requirements well - either by delivering a strong user experience or by providing valuable data analysis.”

    To learn about the findings of this study, including the number of OLTP and OLAP database VMs that Dell EMC Ready Solutions for Microsoft SQL supported, read the report at http://facts.pt/0zt00k, the summary at http://facts.pt/zh81i5, and the infographic at http://facts.pt/7owedc.

    About Principled Technologies, Inc.Principled Technologies, Inc. is the leading provider of technology marketing and learning & development services.

    Principled Technologies, Inc. is located in Durham, North Carolina, USA. For more information, please visit www.principledtechnologies.com.

    Jasmine WhitePrincipled Technologies, Inc.1007 Slater Road, Suite #300Durham, NC 27703press@principledtechnologies.com


    Oracle Releases Autonomous NoSQL Database | killexams.com real questions and Pass4sure dumps

    Oct 3, 2018

    Oracle is releasing the Oracle Autonomous NoSQL Database, the newest addition to the Oracle Autonomous Database portfolio, designed for workloads needing fast and predictable responses to simple operations.

    The self-driving database service enables developers to easily deploy massive-scale applications, including UI personalization, shopping carts, online fraud detection, gaming, and advertising.

    The fully managed Oracle Autonomous NoSQL Database handles NoSQL applications that require low latency, data model flexibility, and elastic scaling.

    Using simple APIs, developers can focus on application development without having to worry about managing servers, storage expansion, cluster deployments, software installation, or backup.

    Developers can simply specify the throughput and capacity that they wish to provision, and resources are allocated and scaled accordingly to meet dynamic workload requirements.

    “We continue to leverage our revolutionary autonomous capabilities to transform the database market," said Andrew Mendelsohn, executive vice president, Oracle Database. "Our latest self-driving database cloud service, Oracle Autonomous NoSQL Database, provides extreme reliability and performance at very low costs to achieve a highly flexible application development framework.”

    Oracle Autonomous Database offers exceptional performance while helping reduce risk and costs for enterprises.

    Running on Oracle Cloud Infrastructure, the autonomous database portfolio is self-driving, self-securing, and self-repairing to automate key management processes including patching, tuning and upgrading to keep critical infrastructure automatically running.

    The platform offers features such as:

  • Modern: A developer-oriented solution, Oracle Autonomous NoSQL Database is designed for flexibility. The database supports key value APIs including simple declarative SQL API and command line interfaces along with flexible data models for data representation including relational and ad-hoc JSON.
  • Open: The service provides a non-proprietary SQL language, delivering innovative interoperability between standard relational and standard JSON data models. Users also have deployment options to run the same application in the cloud or on-premises with no platform lock-in.
  • Easy: With an available SDK and support for popular languages including Python, Node.JS and Java, Oracle offers a no hassle application development solution to easily connect to Oracle Autonomous NoSQL Database.
  • For more information about this news, visit www.oracle.com.



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    Microsoft 70-768 Exam (Developing SQL Data Models) Detailed Information



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