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98-364 - Database Fundamentals - Braindumps Information

Vendor : Microsoft
Exam Code : 98-364
Exam Name : Database Fundamentals
Questions and Answers : 200 Q & A
Updated On : July 19, 2018
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98-364 Questions and Answers

98-364 Database Fundamentals

Article by Killexams Microsoft Certification Experts


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Microsoft Database Fundamentals

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SQL Server machine gaining knowledge of services – half 1: Python fundamentals | killexams.com real questions with brain dumps

With the free up of SQL Server 2017, Microsoft modified the identify of R capabilities to desktop studying services (MLS) and introduced help for Python, a commonly implemented programming language universal for its easy syntax and code readability. As with the R language, which you can use Python to radically change and analyze data in the context of a SQL Server database and then return the modified statistics to a calling software.

in this article, I explain the way to run Python scripts inside SQL Server. I beginning with the aid of introducing the sp_execute_external_script stored method and then dive into specifics about working with Python scripts. if you’re generic with operating R scripts in SQL Server, you have already got the foundation you deserve to get started with Python. you use the identical stored procedure and its parameters, basically swapping out one programming language for the other. The changes between the two tactics are basically in the languages themselves.

this text is the primary in a sequence about MLS and Python. In it, we’ll evaluation several examples that reveal the way to run Python scripts, the usage of data from the AdventureWorks2017 database. The examples are very basic and intended only to introduce you to the fundamentals of working with Python in SQL Server. In subsequent articles, we’ll dig into Python’s capabilities in additional depth, relying on this text as a basis for those to follow.

Getting began with Python scripting

To run a Python script in SQL Server, use the sp_execute_external_script stored manner. First you need to ensure that the database engine’s MLS and Python accessories are installed, and the external scripting function is enabled. For particulars on how to get all this set up, see the Microsoft document deploy Python computer researching features (In-Database).

After MLS has been put in and enabled, you could use the sp_execute_external_script saved process to run Python scripts (or R scripts, for that rely). right here syntax shows the manner’s simple facets:

sp_execute_external_script

    @language = N'language',

    @script = N'script' [,]

    [ @input_data_1 = 'SelectStatement' [,] ]

    [ @input_data_1_name =  N'InputDataVariable' [,] ]

    [ @output_data_1_name = N'OutputDataVariable' [,] ]

    [ @params =  N'@ParamName DataType [ OUT | OUTPUT ] [ ,...n ]' [,] ]

    [ @ParamName = 'value' [ OUT | OUTPUT ] [ ,...n ] ]

    [ WITH outcome sets

       (( OutputColumnName [,...n ] )) ] [;]

You’ll be trained the specifics of the syntax as you work in the course of the examples in this article. note, youngsters, that the SQL Server doc sp_execute_external_script (Transact-SQL) no longer comprises the WITH results units clause as part of the syntax because it has during the past. in its place, the document facets to the EXECUTE (Transact-SQL) subject matter for particulars on how to use this clause. I’ve blanketed it right here for completeness, but it surely’s truly part of the EXECUTE command.

To exhibit how to run the sp_execute_external_script saved process, beginning with a simple instance that retrieves earnings facts from the AdventureWorks2017 database, passes the facts right into a Python script, and then returns the records with out enhancing it:

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Use AdventureWorks2017;

GO

-- define Python script

DECLARE @pscript NVARCHAR(MAX);

SET @pscript = N'

# assign SQL Server dataset to df variable

df = InputDataSet

# return df dataset

OutputDataSet = df';

-- outline T-SQL query

DECLARE @sqlscript NVARCHAR(MAX);

SET @sqlscript = N'

  choose t.identify AS Territories, solid(h.Subtotal AS glide) AS earnings

  FROM income.SalesOrderHeader h internal join sales.SalesTerritory t

      ON h.TerritoryID = t.TerritoryID;';

-- run technique, the usage of Python script and T-SQL question

EXEC sp_execute_external_script

  @language = N'Python',

  @script = @pscript,

  @input_data_1 = @sqlscript;    

GO

The illustration can also be broken into three diverse steps:

  • Declare the @pscript variable, outline the Python script, and assign the script to the variable.
  • Declare the @sqlscript variable, define the T-SQL question, and assign the question to the variable.
  • Run the sp_execute_external_script kept manner, passing within the @pscript and @sqlscript variables. You don’t have to move the Python script and T-SQL question as variables, but it surely makes the code extra readable.
  • start with the first step. After declaring the @pscript variable, you create the Python script, which begins by means of assigning the output from the T-SQL question to the df variable:

    via default, the information back via the T-SQL query will also be accessed in the Python script by means of calling the InputDataSet variable. in this method, that you can assign the SQL Server dataset to the df variable after which work with that dataset as quintessential. As you’ll see later within the article, that you could also specify an additional name to exchange the InputDataSet variable, however for now, stick with the default.

    next, assign the df dataset to the OutputDataSet variable:

    The OutputDataSet variable is the default variable used to return the script’s data to the calling software. As with the InputDataSet variable, that you would be able to change the identify, however for now, use the default.

    during this case, the script does nothing more than return the identical facts that you’re retrieving from SQL Server, something you could have kept away from using a Python script. really, you don't even should consist of the df variable and can as an alternative quite simply assign the enter variable to the output variable:

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      OutputDataSet = InputDataSet

    I’ve covered the df variable here to support display the way to start working with SQL Server statistics within a Python script. greater commonly than no longer, you’ll are looking to manipulate and analyze the SQL Server data. Assigning the supply facts to a separate variable lets you adjust the supply records, whereas nonetheless preserving the customary dataset. It’s as much as you to come to a decision the highest quality technique to move information from one object to the next, in keeping with your certain situations.

    next, outline the T-SQL question and assign it to the @sqlscript variable. in this case, the question is just an easy be part of, however you could make it as complicated as it has to be. One merchandise price noting, youngsters, is that the code converts the earnings column to the float facts category. here's because Python helps a restrained number of forms when compared to SQL Server, and the Python engine can implicitly convert only one of the crucial SQL Server varieties.

    as an example, the Territories column is configured with the NVARCHAR data class. When the Python script runs, the Python engine implicitly converts the column to the Python str classification. besides the fact that children, the earnings column is configured with the funds classification. The Python engine can't implicitly convert this classification, so that you must explicitly convert it to one that the engine can tackle. during this case, you’re converting the revenue column to go with the flow, which the Python engine can then convert to the Python float64 type. (For extra advice on the SQL Server kinds that Python supports, see the Microsoft document Python Libraries and statistics forms.)

    After defining the Python script and T-SQL query, which you can call the sp_execute_external_script stored technique, passing in the fundamental parameter values. For the @language parameter, specify Python. if you have been working with an R script, you possibly can move in R because the cost. currently, these are the simplest two values that the parameter helps.

    For the @script parameter, specify the @pscript variable, and for the @input_data_1 parameter, specify the @sqlscript variable. The @input_data_1 parameter is non-compulsory. You wouldn't have to consist of SQL Server information in the Python script, but if you’re going to be operating Python in the SQL Server ambiance, it looks doubtless you’ll are looking to use SQL Server facts.

    With the parameters in vicinity, which you can name the kept system. in case you do, the Python engine runs the Python script, the use of the SQL Server facts, and returns the dataset to the calling application. right here figure suggests the first chunk of rows back by using the script.

    %20screenshots1/stPython1_fig01.png

    As you could see, the Python script returns a primary outcomes set with the information organized into columns and rows, similar to the supply information. You could have seen, however, that the again records includes no column names. For that, you deserve to specially assign names to the back statistics.

    Assigning names to Python script objects

    When the use of the sp_execute_external_script kept procedure to run Python scripts, you could specify names for the default variables assigned to the input and output datasets. that you would be able to additionally specify column names for the outputted dataset. right here procedure name assigns the identify RawData to the input variable, the identify SumData to the output variable, and the names Territories and income to the output columns:

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    DECLARE @pscript NVARCHAR(MAX);

    SET @pscript = N'

    # assign SQL Server dataset to df

    df = RawData

    # return df dataset

    SumData = df';

    DECLARE @sqlscript NVARCHAR(MAX);

    SET @sqlscript = N'

      select t.name AS Territories, cast(h.Subtotal AS waft) AS revenue

      FROM revenue.SalesOrderHeader h inner join earnings.SalesTerritory t

          ON h.TerritoryID = t.TerritoryID;';

    EXEC sp_execute_external_script

      @language = N'Python',

      @script = @pscript,

      @input_data_1 = @sqlscript,

      @input_data_1_name = N'RawData', --enter variable identify

      @output_data_1_name = N'SumData' --output variable name

      WITH outcome sets(

        (Territories NVARCHAR(50), sales funds)); --output column names

    GO

    To rename the enter variable, you ought to consist of the @input_data_1_name parameter when calling the sp_execute_external_script stored manner, assigning the brand new name to that parameter. You ought to then use the name in the Python script when referencing the enter dataset:

    To rename the output variable, you ought to include the @output_data_1_name parameter when calling the saved system, assigning the new name to that parameter. You need to then use that name inside the Python script when referencing the output dataset:

    The next step is to assign names to the columns. To try this, include the WITH effect units clause when calling the saved system, specifying the column names and their statistics types. The checklist of column names have to be enclosed in double parentheses with the names separated through commas. SQL Server will then assign those names to the output dataset within the order they’re exact, as proven in the following consequences.

    %20screenshots1/stPython1_fig02.png

    When assigning column names, keep in mind that the database engine ought to be capable of implicitly convert the returned records to that category. for example, in the WITH result units clause above, you assign the Territories name to the first column, specifying the NVARCHAR(50) information class. if you had as a substitute precise CHAR(5), most of the column values would were truncated. in case you had precise INT for earnings, the database engine would have back an error.

    with reference to the income column, SQL Server effectively converts the Python float64 class to the SQL Server money classification with out changing the statistics. you could get away with this since the values are already confined to four decimal areas should you import the statistics into the Python script. If there had been a enhanced number of decimal locations in the facts lower back with the aid of the Python script, the values would have been rounded once they had been transformed. regularly this usually are not a problem, depending on the level of precision that’s required, but it surely aspects to the fact that, every time facts is implicitly transformed, you should be privy to its knowledge have an effect on.

    When it involves the output dataset, it’s as much as you no matter if to specify column names. You’ll doubtless wish to decide on a case-through-case foundation, counting on your software wants.

    The equal goes for renaming the input and output variables. There should be would becould very well be instances when renaming them can also be beneficial for clarity, specifically in case you’re importing extra datasets. commonly, besides the fact that children, it’s just a remember of alternative. individually, I pick not to rename the input and output variables if there’s no overriding intent to accomplish that. I locate it less difficult to evaluation code that makes use of the default names.

    Manipulating the Python data body

    in case you’ve worked with the R language in SQL Server, you’re little question common with the concept of the facts body, a two-dimensional information structure comparable to a SQL Server table. information frames make it more straightforward to control and analyze sets of records within your scripts. Python records frames function a whole lot the equal way as they do within the R language and are fundamental to working with the information within the context of SQL Server.

    The InputDataSet and OutputDataSet variables each shop statistics as facts frames. here's genuine despite the fact that you rename the variables. The variables are both DataFrame objects, in response to the DataFrame classification. The SQL Server information imported right into a Python script is converted to a DataFrame object, and the information back with the aid of a Python script is handed to the output variable as a DataFrame object. And a whole lot of what you do with that statistics in between often contains DataFrame objects.

    a simple strategy to ascertain that you’re working with records frames is to regulate the primary example to come back best the statistics classification of the df variable:

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    DECLARE @pscript NVARCHAR(MAX);

    SET @pscript = N'

    # assign SQL Server dataset to df

    df = InputDataSet

    # retrieve type of df object

    print(class(df))';

    DECLARE @sqlscript NVARCHAR(MAX);

    SET @sqlscript = N'

      opt for t.identify AS Territories, solid(h.Subtotal AS float) AS revenue

      FROM sales.SalesOrderHeader h internal be part of income.SalesTerritory t

        ON h.TerritoryID = t.TerritoryID;';

    EXEC sp_execute_external_script

      @language = N'Python',

      @script = @pscript,

      @input_data_1 = @sqlscript;    

    GO

    To get the information type, you call the class feature, specifying the df variable, after which name the print characteristic to return the statistics category to the application. once I ran this script in SQL Server management Studio, I received right here message:

    STDOUT message(s) from exterior script:

    <class 'pandas.core.frame.DataFrame'>

    As you could see, the df variable is a DataFrame object, which it inherited from the InputDataSet variable. word, although, that the namespace is really pandas.core.frame.DataFrame. this is because the DataFrame class is a part of the Pandas library which is blanketed within the MLS Python installing. We’ll be protecting the Pandas library in additional aspect in subsequent articles. except then, that you would be able to be trained extra about Pandas at https://pandas.pydata.org/.

    Now try to do anything with the facts you’re importing from SQL Server. You’ll beginning by using aggregating the earnings values to assess the full quantity of earnings per territory:

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    DECLARE @pscript NVARCHAR(MAX);

    SET @pscript = N'

    # calculate revenue totals for each and every territory

    df1 = InputDataSet

    df2 = df1.groupby("Territories", as_index=False).sum()

    # return aggregated dataset

    OutputDataSet = df2';

    DECLARE @sqlscript NVARCHAR(MAX);

    SET @sqlscript = N'

      choose t.name AS Territories, forged(h.Subtotal AS drift) AS sales

      FROM revenue.SalesOrderHeader h inner be part of income.SalesTerritory t

        ON h.TerritoryID = t.TerritoryID;';

    EXEC sp_execute_external_script

      @language = N'Python',

      @script = @pscript,

      @input_data_1 = @sqlscript

      WITH outcome units(

        (Territories NVARCHAR(50), TotalSales funds));    

    GO

    To aggregate the statistics within the df1 records frame, name the item’s groupby feature which is a member of the Pandas DataFrame category:

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      df2 = df1.groupby("Territories", as_index=False).sum()

    When calling the groupby characteristic, specify the df1 variable, adopted by way of a duration after which the feature identify. besides the fact that children the feature can take a number of arguments, this illustration comprises only two. the primary is the Territories column which shows that the data may be grouped according to values in that column. The 2nd argument is the as_index alternative which is set to False. This tells the Python engine to come the Territories values, guaranteeing that the interesting Territories names are blanketed in the outcomes together with the aggregated revenue statistics.

    After specifying the groupby arguments, tag on the sum feature to point out that the revenue values should still be brought collectively for each and every neighborhood. which you could use any of the supported aggregate services, akin to suggest, max, min, first, or remaining.

    since the revenue column is the most effective column in the dataset other than Territories, you don't should do anything with the characteristic itself. Had the dataset contained additional columns that you didn't are looking to include, you may have had to specify the sales column when calling the groupby function.

    next, assign the aggregated results to the df2 variable and then assign that variable to the OutputDataSet variable, giving the following effects.

    %20screenshots1/stPython1_fig04.png

    The subsequent step is to add a calculated column to the df2 records body that quotes a territory’s performance in response to the aggregated sales values. To do so, modify the facts frame by using defining the range of each predefined price:

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    DECLARE @pscript NVARCHAR(MAX);

    SET @pscript = N'

    # calculate earnings totals for each and every territory

    df1 = InputDataSet

    df2 = df1.groupby("Territories", as_index=False).sum()

    # add performance column

    df2["Performance"] = "ideal"

    df2["Performance"][df2["Sales"] < 7000000] = "negative"

    df2["Performance"][df2["Sales"] > 10000000] = "astonishing"

    # return aggregated dataset

    OutputDataSet = df2';

    DECLARE @sqlscript NVARCHAR(MAX);

    SET @sqlscript = N'

      choose t.name AS Territories, solid(h.Subtotal AS float) AS sales

      FROM sales.SalesOrderHeader h internal be part of sales.SalesTerritory t

        ON h.TerritoryID = t.TerritoryID;';

    EXEC sp_execute_external_script

      @language = N'Python',

      @script = @pscript,

      @input_data_1 = @sqlscript

      WITH outcome sets(

        (Territories NVARCHAR(50), TotalSales funds, efficiency NVARCHAR(20)));    

    GO

    delivery by using defining a new column named efficiency and assigning the default price acceptable to the column:

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      df2["Performance"] = "perfect"

    so as to add the column, you want only specify the df2 variable, followed by way of brackets that include the identify of the new column in charges. then you definately use an equal operator (=) to set the column’s default value to suited.

    subsequent, regulate the column definition to encompass a 2nd possible cost, the usage of a conditional expression to outline a data range for when to observe that value:

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      df2["Performance"][df2["Sales"] < 7000000] = "bad"

    once more, beginning by specifying the df2 variable and the name of the column in brackets. next, outline the conditional expression, enclosed in a second set of brackets. The expression states that if the sales price is under 7 million, the efficiency value should be set to negative. then you definately repeat this technique to add a third cost, best this time the conditional expression suggests that, if the income value exceeds 10 million, the efficiency price should be set to fabulous:

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      df2["Performance"][df2["Sales"] > 10000000] = "superb"

    That’s all there's to including a calculated column to the statistics frame. The Python script now returns the results shown in here figure.

    %20screenshots1/stPython1_fig05.png

    Of route, you could have carried out the equal outcomes with T-SQL on my own, but even such a simple example helps display one of the approaches that you can manipulate records to achieve quite a lot of consequences. In follow, your Python scripts is normally lots extra advanced when you are to operate meaningful analytics.

    working a Python script within a stored method

    commonly in the event you’re working with Python, you’ll want to name the sp_execute_external_script stored method from within a consumer-defined kept manner that may then be evoked from a calling utility. To accomplish that, easily embed the T-SQL and Python code and sp_execute_external_script manner name in the system definition, as shown in right here example:

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    DROP manner IF EXISTS dbo.GetSalesTotals;

    GO

    CREATE technique dbo.GetSalesTotals

    AS

      SET NOCOUNT ON;

      DECLARE @pscript NVARCHAR(MAX);

      SET @pscript = N'

    # calculate earnings totals for each territory

    df1 = InputDataSet

    df2 = df1.groupby("Territories", as_index=False).sum()

    # add efficiency column

    df2["Performance"] = "desirable"

    df2["Performance"][df2["Sales"] < 7000000] = "poor"

    df2["Performance"][df2["Sales"] > 10000000] = "miraculous"

    # return aggregated dataset

    OutputDataSet = df2';

      DECLARE @sqlscript NVARCHAR(MAX);

      SET @sqlscript = N'

        choose t.identify AS Territories, solid(h.Subtotal AS waft) AS earnings

        FROM income.SalesOrderHeader h internal be part of sales.SalesTerritory t

          ON h.TerritoryID = t.TerritoryID;';

      EXEC sp_execute_external_script

        @language = N'Python',

        @script = @pscript,

        @input_data_1 = @sqlscript

        WITH outcomes units(

          (Territories NVARCHAR(50), TotalSales cash, efficiency NVARCHAR(20)));  

    GO

    There’s no magic here, just a basic definition that creates the GetSalesTotals process which calls the sp_execute_external_script method and passes in the Python script and T-SQL question as we’ve viewed in previous examples. you could then use an EXECUTE remark to run the procedure:

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      EXEC dbo.GetSalesTotals;

    The procedure will return the equal outcomes because the old instance, simplest now you’re able to persist the logic. that you can also outline a stored method to include input parameters whose values can then be consumed through the Python script:

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    DROP technique IF EXISTS dbo.GetSalesTotals;

    GO

    CREATE process dbo.GetSalesTotals

      (@low INT, @high INT) --add manner parameters

    AS

      SET NOCOUNT ON;

      DECLARE @pscript NVARCHAR(MAX);

      SET @pscript = N'

    # calculate revenue totals for each territory

    df1 = InputDataSet

    df2 = df1.groupby("Territories", as_index=False).sum()

    # add performance column

    df2["Performance "] = "acceptable"

    df2["Performance "][df2["Sales"] < LowSales] = "terrible"

    df2["Performance "][df2["Sales"] > HighSales] = "brilliant"

    # return aggregated dataset

    OutputDataSet = df2';

      DECLARE @sqlscript NVARCHAR(MAX);

      SET @sqlscript = N'

        opt for t.name AS Territories, solid(h.Subtotal AS glide) AS earnings

        FROM sales.SalesOrderHeader h internal be a part of income.SalesTerritory t

          ON h.TerritoryID = t.TerritoryID;';

      EXEC sp_execute_external_script

        @language = N'Python',

        @script = @pscript,

        @input_data_1 = @sqlscript,

        @params = N'@LowSales waft, @HighSales drift', --outline Python variables

        @LowSales = @low, --assign process parameter to Python variable

        @HighSales = @excessive --assign process parameter to Python variable

        WITH effect sets(

          (Territories NVARCHAR(50), TotalSales funds, rankings NVARCHAR(20)));  

    GO

    This time, when defining the kept process, encompass two input parameters, @low and @high, each described with the INT statistics type. the first parameter will set the low range for the calculated column, and the 2d parameter will set the excessive latitude.

    you then deserve to tie these parameters to the Python script. To achieve this, consist of three further parameters when calling the sp_execute_external_script kept system. the primary is the @params parameter, which specifies the @LowSales and @HighSales parameters. These are used to flow values into the Python script. each parameters are defined with the go with the flow statistics classification.

    You should then assign the @low and @high parameters to the @LowSales and @HighSales parameters, respectively:

    @LowSales = @low,

    @HighSales = @excessive

    that you could then reference the @LowSales and @HighSales parameters internal the Python script using the names LowSales and HighSales:

    df2["Ratings"][df2["Sales"] < LowSales] = "poor"

    df2["Ratings"][df2["Sales"] > HighSales] = "marvelous"

    Now when you name the stored system, you can circulate in the low and high values used to outline the degrees within the efficiency column:

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      EXEC dbo.GetSalesTotals @low = 7000000, @high = 10000000;

    during this case, you’re specifying the equal values that you used in the outdated instance, so you will receive that equal effects. besides the fact that children, that you may also specify distinctive values:

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      EXEC dbo.GetSalesTotals @low = 5000000, @excessive = 20000000;

    The stored procedure now returns the effects proven in right here determine which indicate that only 1 territory is performing poorly and only one above regular.

    %20screenshots1/stPython1_fig06.png

    despite the fact here is a fairly trivial instance, it demonstrates the right way to dynamically interact with the Python script by passing in values when calling stored processes in order to improved handle the outcomes.

    attending to recognize Python and desktop getting to know features

    certainly, we’ve barely skimmed the floor of all we are able to do with Python in SQL Server. Python is a flexible and wide language that provides a wide range of options for manipulating and examining facts. best of all, being in a position to run Python within SQL Server makes it possible to investigate a database’s records directly within the context of that database.

    As we development via this collection, we’ll dig extra into the Python language and the a variety of techniques we can use it to control statistics and operate analytics. in the meantime, this article should provide you with a good foundation for getting started with Python in SQL Server. We’ll be building on this groundwork going ahead. I additionally inspire you to dig into Python your self and play with it in the context of SQL Server. The more desirable you keep in mind that language, the more effective a device you’ll have for working with SQL Server information.


    improving the consumer event and app performance with cloud-primarily based equipment | killexams.com real questions with brain dumps

    This documentation is archived and isn't being maintained.

    company Case analyze

    April 2016

    Microsoft IT supports a portfolio of marketing, companion, and income applications for Microsoft, apart from many different functions. Our companions promote on behalf of, or in partnership with, Microsoft. They use many tools and materials, including companion portals, enrollment equipment, and incentives to do enterprise with Microsoft and to support their shoppers. to make use of these equipment and components, companions need to have quite a lot of identities for each consumer of managed provider accounts, companion lively directory, and Microsoft Azure lively listing as a result of the siloed nature of the applications.

    during the past, our initiatives had longer liberate cycles as a result of we used a standard waterfall construction system. We delivered options through an software-centric model that concentrated less on services. This process sometimes duplicated business method and statistics capabilities, increased charges, and affected client satisfaction.

    We all started a journey 18 months in the past to transform our corporation and techniques to be extra agile. A key part of this alternate included relocating application development to the cloud.

    a new initiative

    We made an important effort to alternate the organizational structure and subculture of our engineering team. This transformation changed into very consumer focused and was applied the use of engineering fundamentals for each and every sprint group. determine 1 indicates how our shift in engineering procedures and tools results in an improved consumer adventure. The IT show off technical case analyze, moving to contemporary engineering has extra information about our organizational, cultural, and building process changes.

    Graphic highlighting modern engineering: People and culture maturiy > Engineering principles, tools, and processes > Services and features > Customer experince

    figure 1. modern engineering in action

    One component of our up to date engineering effort to increase agility changed into the initiative, Lead with the cloud. We applied the initiative in line with business value with the aim of velocity and agility. It grew to be a priority for us to speed up this transformation.

    considering that the initiative began, we've come a protracted approach in our transformation, and now there are zero tasks the use of the waterfall development method. We center of attention on constructing new functions or capabilities the use of the PaaS mannequin, with a number of exceptions. Any older, on-premises functions are constructed using the IaaS mannequin. We use the out-of-box capabilities of Azure to benefit enormous price each in terms of can charge mark downs and reduced time to free up.

    Azure energetic directory

    To simplify the event for partners with dissimilar debts throughout diverse equipment, we carried out Azure energetic listing (Azure advert). during the past, for example, Microsoft managed and created person bills for the partner Incentives portal. When a associate employee left the company, the accomplice didn't have the capability to disable the employee account. With Azure ad out-of-the-field, we gave user management control again to the associate, which additionally helped us put into effect the only sign-on journey for our partners. partners now have their own Azure advert tenant to manage user bills, which allows a seamless journey for gaining access to different Microsoft companion portals and functions. This simplified process provides savings and reduced chance for Microsoft. The IT show off company Case look at, enforcing single signal-on for Microsoft partners, has more counsel concerning the merits of single signal-on.

    Now, to onboard a associate, we create an account (usually the administrator role) in Azure advert. Then we send an email invitation, which comprises a link to the associate Incentives portal and a password. The companion administrator follows the hyperlink to log in with the user name and password, and confirms their new Azure advert account. For compliance and protection, the companion administrator is caused to request a PIN. This two-component authentication ensures that the correct person is being onboarded. A PIN is emailed and is then entered via the associate administrator to profit access to the portal. as soon as the administrator has the Azure ad account, they're capable of access different Azure ad partner elements without signing in once more. The partner administrator can then control the business’s consumer accounts in Azure advert. If the associate employee leaves the company, the accomplice Azure advert administrator readily deletes the user account.

    Partners Incentives Experience login screenshot

    Partner Incentives portal signed in screenshot

    determine 2. companion Incentives portal sign on journey with Azure advert

    Azure building

    We used the many development functions in Azure to obtain our desires. here are just a few examples.

    visible Studio crew ServicesProject migration

    We migrated all of our projects from on-premises Microsoft team foundation features to visual Studio team functions on Azure. All new code is now created in visual Studio group functions. visual Studio team services offers a collection of fundamental templates, and our engineering group uses the agile and SCRUM templates. With these templates, we are able to deploy a task in a couple of minutes, with every little thing we need for a assignment. Templates help uniform approaches throughout our groups, making it less complicated for engineers to circulate between tasks. furthermore, Azure liberate manager comprises a library of scripts, requiring less customized code for promotion a construct via examine and deployment.

    Engineering fundamentals

    We use visible Studio crew functions to book our group contributors and ensure a consistent adherence to the brand new methods. by way of embedding the engineering fundamentals the place our engineers do their work, dash teams have a visual reminder of necessities and exit standards. This helps velocity up development via defining practices like enabling rapid provisioning of building and check environments on demand, unattended deployment, and automated testing and building of comfortable and compliant capabilities.

    performance checking out

    Our greatest problem all the way through performance trying out is the want for geographically distributed, construction-like environments and load-test agent machines, to simulate actual situations. The enormous numbers of machines required for on-premises stress checking out had been expensive and intricate to control. With load checking out options provided by means of Azure, these issues disappear. Now, executing quickly, true-time performance in a sensible way is feasible at a fraction of the cost. it's handy to configure the atmosphere and we simplest pay for consumption. Our engineers can also use ExpressRoute to behavior performance trying out in the cloud, where they have got a dependency on domain-joined infrastructure.

    This Microsoft IT demo suggests the benefits of efficiency checking out in a cloud-based ambiance the use of Microsoft visible Studio.

    Azure load testing dashboard screenshot

    figure 3. Azure load checking out dashboard

    Transition to virtual machines

    historically, in the natural waterfall model, our engineering team would beginning up many environments and grasp on to them. For each and every dash team, there changed into an average of six non-production environments. within the new era of contemporary engineering, we obligatory to in the reduction of the footprint of non-construction environments to below two. This shift compelled us to make use of dynamic atmosphere provisioning. for example, we are able to kick off a build in a single field and then decommission it in an automated manner, doing away with distinctive on-premises servers that are idle many of the time. we are starting to enforce this procedure all over our initiatives. Our engineering teams additionally moved from clunky physical computers to Azure the usage of virtual machines to achieve computer as a carrier.

    Monitoring performance and controlling deployment

    before our engineering transformation, engineers had to write custom error logging and instrumentations of their personal database. any individual trying to debug an argument would need to understand where to look. Azure software Insights provides a repository for each new software for performance and error monitoring. Straight out of the field, without customization, the dashboard gives everything we should monitor performance. Figures four and 5 show the wealthy tips provided, from server particulars to exceptions.

    Screenshot of Azure Application Insights dashboard

    figure four. Azure application Insights dashboard

    Screenshot of Azure Application Insights exception details

    determine 5. Azure utility Insights exception particulars

    BenefitsEngineering productivity

    once our engineers made the cultural shift to building within the cloud, they no longer spend time holding and managing environments. they could now spend more time focusing on innovation. productivity was stronger as a result of:

  • The on-premises performance trying out footprint turned into completely eliminated.

  • visual Studio group capabilities adoption enabled agile development.

  • With only a few code alterations, out-of-field App Insights, gave a prosperous set of records shooting instrumentation and debugging capabilities.

  • Servers are up so far always.

  • The lifestyle shifted for engineers to work in a digital ambiance.

  • stronger consumer journey

    the single signal-on for companions with Azure ad gives them access to the diversity of tools and elements, which makes it less complicated to do company with Microsoft.

  • Azure advert provided a single signal-on for companions.

  • Operations costs for Microsoft had been decreased as a result of we no longer need a crew of americans to manage person debts.

  • consumer administration hazards have been reduced and we gave control lower back to partners.

  • training realized

    alongside the way, we discovered some constructive classes.

  • Adopting the Azure frame of mind, which involved cultural and behavioral changes, takes time.

  • cutting back static environments through leveraging Azure dynamic atmosphere provisioning.

  • expanding automation and deployment checking out.

  • Verifying that facts safety requirements are met by the cloud environment.

  • For more informationMicrosoft IT exhibit

    Microsoft.com/itshowcase

    © 2016 Microsoft agency. All rights reserved. Microsoft and windows are either registered emblems or emblems of Microsoft organization within the united states and/or other international locations. The names of genuine companies and products mentioned herein could be the trademarks of their respective house owners. This document is for informational purposes simplest. Microsoft MAKES NO WARRANTIES, express OR IMPLIED, during this abstract.


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    98-364 Certification Brain Dumps Source : Database Fundamentals

    Test Code : 98-364
    Test Name : Database Fundamentals
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    Q&A : 200 Real Test Questions/Answers


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    The latest: Time magazine says Trump's bought it incorrect | killexams.com real questions with brain dumps

    PALM seashore, Fla. (AP) — The newest on President Donald Trump (all times native):

    9 p.m.

    Time magazine is disputing President Donald Trump's account of how he rejected the journal's request for an interview and picture periods forward of its "grownup of the 12 months" subject.

    In a Friday night tweet, Trump says the magazine recommended him he changed into "doubtless" going to be granted the title for the 2d yr in a row. He tweets: "I mentioned likely isn't any good and took a move."

    Later that night, Time tweeted that Trump "is incorrect about how we opt for person of the yr." The magazine says it would not touch upon its alternative except publication.

    Trump frequently brags about his cover appearances within the iconic journal. He has falsely claimed to cling the record of cowl appearances.

    The magazine will unveil its adult of the yr on Dec. 6.

    ———

    6 p.m.

    President Donald Trump says he became down an interview and photograph shoot for Time magazine's "adult of the 12 months" problem.

    In a Friday night tweet, Trump says the journal recommended him he turned into "probably" going to be granted the title for the 2nd 12 months in a row. He tweets: "I pointed out doubtless isn't any good and took a move."

    Trump often brags about his cowl appearances within the iconic journal. He has falsely claimed to cling the listing of cowl appearances, and changed into printed prior this year to have displayed fake considerations of Time at several of his private golf golf equipment.

    A spokesperson for Time didn't instantly reply to a request for remark Friday evening.

    The magazine will unveil its adult of the year on Dec. 6.

    ———

    5 p.m.

    President Donald Trump is offering his condolences to Egyptian President Abdel-Fattah el-Sissi following "the heinous assault on worshippers in Egypt's North Sinai province" Friday.

    Trump spoke with el-Sissi through mobilephone from his membership in Palm beach, Florida.

    The White apartment says that Trump condemned the assault within the call "and reiterated that the us will continue to stand with Egypt within the face of terrorism."

    The White residence provides that the "foreign neighborhood can not tolerate barbaric terrorist companies and need to fortify its efforts to defeat terrorism and extremism in all its kinds."

    Trump past on Twitter had used the assault as an opportunity to reaffirm his support for a wall on the U.S. border with Mexico and to avert individuals from a few Muslim-majority countries from entering the U.S.

    ———

    4:25 p.m.

    President Donald Trump is denouncing the lethal mosque attack in Egypt and maintaining that the world have to crush terrorists by means of defense force means. he's also pushing his shuttle ban, which is tied up in courts.

    at least 235 americans had been killed when Islamic militants attacked a crowded mosque during prayers in the Sinai Peninsula.

    Trump tweeted: "want the WALL, need the BAN!" He delivered: "God bless the americans of Egypt."

    At his Florida hotel for an extended Thanksgiving weekend, Trump spoke with the aid of cell with Turkish President Recep Tayyip Erdogan about bringing peace to the core East.

    A Turkish professional says Trump agreed the U.S. would no longer arm Syrian Kurdish combatants — a claim no longer established by means of the White condo.

    ———

    2 p.m.

    President Donald Trump says he could be calling his Egyptian counterpart "in a short while" to talk about Friday's "tragic terrorist assault, with so a good deal loss of life."

    as a minimum 235 individuals were killed Friday when militants attacked a crowded mosque right through prayers in the Sinai Peninsula, setting off explosives and spraying worshippers with gunfire.

    Trump says in a tweet after departing considered one of his golf clubs in Florida that, "We need to get tougher AND SMARTER than ever before, and we can."

    He provides: "want the WALL, want the BAN! God bless the individuals of Egypt."

    Trump looks to be regarding his guarantees to build a border wall along the southern U.S. border with Mexico and his efforts to bar people from certain Muslim-majority international locations from coming to the U.S.

    ———

    11:10 a.m.

    President Donald Trump is denouncing what he is calling the "Horrible and cowardly terrorist assault on blameless and defenseless worshipers (sic) in Egypt."

    Militants attacked a crowded mosque right through Friday prayers in the Sinai Peninsula, settling off explosives, spraying worshippers with gunfire and killing at least 200 people within the deadliest ever assault on Egyptian civilians through Islamic extremists.

    Trump says on Twitter, "the realm cannot tolerate terrorism" and that "we ought to defeat them militarily and discredit the extremist ideology that types the foundation of their existence!"

    Trump's tweet got here as he become taking part in golf at one in every of his Florida courses with Tiger Woods and Dustin Johnson.

    ———

    10:30 a.m.

    Turkish foreign Minister Mevlut Cavusoglu says U.S. President Donald Trump has instructed his Turkish counterpart that the united states will now not provide hands to Syrian Kurdish combatants.

    Cavusoglu stated Trump relayed his resolution right through a telephone conversation between the Turkish and U.S. leaders on Friday. Cavusoglu changed into current in Turkish President Recep Tayyip Erdogan's office throughout the cellphone name.

    Turkey considers the Kurdish Syrian opponents, widely used through the initials YPG, to be terrorists as a result of their affiliation to outlawed Kurdish rebels in Turkey. A U.S. choice to arm the combatants soured members of the family between both NATO allies.

    Cavusoglu mentioned: "Mr. Trump obviously pointed out that he had given clear directions and that the YPG may not accept fingers and that this nonsense should still have ended a long time in the past."

    ———

    9:forty a.m.

    Turkey's president says he mentioned the Syrian crisis and different regional concerns with in a mobilephone name Friday with President Donald Trump.

    President Recep Tayyip Erdogan's office stated the two leaders also discussed ties between Turkey and the U.S. all the way through their telephone dialog on Friday.

    Their discussion comes days after Erdogan attended a trilateral meeting with the Russian and Iranian leaders in Sochi, Russia, to advertise a peaceful contract in Syria.

    family members between NATO allies Turkey and the united states have soured over a couple of considerations, including U.S. guide to Syrian Kurdish warring parties which Ankara considers to be terrorists because of their links to outlawed Kurdish rebels in Turkey.

    past, Trump tweeted that he'd be talking with Erdogan "about bringing peace to the mess that I inherited in the middle East."

    ———

    7:30 a.m.

    President Donald Trump says he'll be speakme with Turkish President Recep Tayyip Erdogan on Friday "about bringing peace to the mess that I inherited in the core East."

    Trump railed all through his crusade about the ongoing conflicts in Afghanistan and Iraq, but has boasted about progress in recent months beneath his watch.

    Trump says in a tweet that he'll "get it all finished, but what a mistake, in lives and bucks to be there within the first area!"

    it be unclear precisely which international locations he's referring to, however Trump has cited $6 trillion in the past to investigate U.S. spending on conflicts in the middle East.

    reality checkers have discovered that quantity to be only partially accurate considering it falls on the high conclusion of analysts' estimates and contains future clinical care and disability benefits and nation-building prices.

    ———

    7:17 a.m.

    President Donald Trump can be taking part in golf with Tiger Woods at Trump's course in Jupiter, Florida, Friday.

    Trump says in a tweet that premiere golfer Dustin Johnson also will take half.

    Woods' 79 PGA Tour victories and 14 primary titles each rank No. 2 all-time. He has no longer competed professionally seeing that February on account of a again injury and isn't anticipated to come back this yr. His last win turned into in August 2013

    Trump says after golf, he'll return to his deepest Mar-a-Lago club "for talks on bringing even more jobs and agencies lower back to america!"

    ———

    6:50 a.m.

    he is not letting it go.

    President Donald Trump is continuing to rail towards soccer gamers who kneel right through the countrywide Anthem to protest racism and police brutality.

    Trump asks his followers in a Black Friday tweet: "can you agree with that the disrespect for our country, our Flag, our Anthem continues with out penalty to the gamers."

    he is accusing NFL commissioner Roger Goodell of having "misplaced manage" of what he called a "hemorrhaging league" the place "players are the boss!"

    Trump's tweet become based on one from his social media chief, Dan Scavino.

    Scavino had shared a Breitbart information story about big apple Giants player Olivier Vernon taking the knee right through the anthem on Thanksgiving forward of a game against the Redskins.

    The web site is run by means of Trump's former chief strategist.


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    LATAM shut-One provider raises US$600m in LatAm fundamental | killexams.com real questions with brain dumps

    * Enersis Americas prints bond

    * BCP preps sale of US$300m three-12 months be aware

    * PDVSA boss slams record of push for bolivar bond payments

    through Mike Gambale and Paul Kilby

    ny, Oct 20 (IFR) - below is a recap of basic issuance endeavor in the Latam basic market on Thursday:

    number of offers priced: 1

    complete issuance volume: US$600m

    ENERSIS AMERICAS SA

    Enersis Americas SA (ENRSIS), Baa3/BBB/BBB, announced a US$600m (no grow) SEC registered 10-yr senior unsecured notes deal. active bookrunners are BBVA, Citigroup, JPMorgan, Morgan Stanley and Santander.

    UOP: Debt compensation, repurchase of shares in connection with the Chilean merger withdrawal rights exercised by Enersis Americas shareholders and GCP. contract: T+3 (Oct 25, 2016).

    IPT: T+hi 200s area

    rate information: T+250bp area (+/- 5bp)

    LAUNCH: US$600m at T+245bp

    PRICED: US$600m 4.0% cpn 10yr (10/25/2026). At 98.364, yld 4.202%. T+245bp. MWC+40bp. 1st pay: four/25/2017.

    e-book: US$four.8bn

    PIPELINE:

    Banco de Credito del Peru (BCP), Baa2/BBB+, announced a US$300m 3-yr (10/25/19) senior unsecured word offering via financial institution of america Merrill Lynch, Credicorp and JP Morgan. BCP held investor calls on Thursday morning at 10:30am. Pricing is expected to take vicinity on Friday. UOP: GCP. Settle: 10/25/sixteen. IPTs are set at T+170bp enviornment.

    Trinidad era limitless (TGU) started roadshows on Wednesday to market a possible benchmark US greenback bond.

    The borrower should be in los angeles on Friday, earlier than heading to London on October 24, Boston on October 25 and manhattan on October 26. expected ratings for the 144A/RegS senior unsecured be aware are BBB/BBB-. TGU is the biggest energy organization in Trinidad & Tobago

    The Argentine Province of Santa Fe has employed banks to arrange investor meetings within the US and Europe forward of a potential US$250m bond sale. meetings will take vicinity in big apple on Friday, wrapping up in Boston on October 24. A 144A/Reg S situation is anticipated to follow. JP Morgan is the world coordinator on the abilities sale, joined by using Citigroup and HSBC as bookrunners.

    LATAM airlines community S.A. ("LATAM", NYSE: LFL), a Chile-primarily based airline neighborhood, mandated Citi and JP Morgan as world coordinators and joint bookrunners and BNPP, BofAML, Deutsche bank, and Santander as joint bookrunners to organize a collection of mounted earnings investor conferences in Chile, Europe, and the U.S.. A USD-denominated benchmark 144A/Reg S 7-yr providing is expected to observe, subject to market situations. Roadshows wrapped up on Wednesday.

    (Reporting through Mike Gambale; enhancing by using Paul Kilby)



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    Microsoft 98-364 Exam (Database Fundamentals) Detailed Information

    98-364 - Database Fundamentals


    98-364 Test Objectives


    Understanding core database concepts (20–25%)
    Preparation resources
    Create database objects (20–25%)
    Preparation resources
    Manipulate data (25–30%)
    Preparation resources
    Understand data storage (15–20%)
    Preparation resources
    Administer a database (10–15%)
    Preparation resources


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