DP-203日本語 無料問題集「Microsoft Data Engineering on Microsoft Azure (DP-203日本語版)」

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Explanation:

Report1: CSV
CSV: The destination writes records as delimited data.
Report2: AVRO
AVRO supports timestamps.
Not Parquet, TSV: Not options for Azure Data Lake Storage Gen2.
Reference:
https://streamsets.com/documentation/datacollector/latest/help/datacollector/UserGuide/Destinations/ADLS- G2-D.html

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Explanation:

Data storage: Azure Data Lake Store
A key mechanism that allows Azure Data Lake Storage Gen2 to provide file system performance at object storage scale and prices is the addition of a hierarchical namespace. This allows the collection of objects/files within an account to be organized into a hierarchy of directories and nested subdirectories in the same way that the file system on your computer is organized. With the hierarchical namespace enabled, a storage account becomes capable of providing the scalability and cost-effectiveness of object storage, with file system semantics that are familiar to analytics engines and frameworks.
Batch processing: HD Insight Spark
Aparch Spark is an open-source, parallel-processing framework that supports in-memory processing to boost the performance of big-data analysis applications.
HDInsight is a managed Hadoop service. Use it deploy and manage Hadoop clusters in Azure. For batch processing, you can use Spark, Hive, Hive LLAP, MapReduce.
Languages: R, Python, Java, Scala, SQL
Analytic data store: SQL Data Warehouse
SQL Data Warehouse is a cloud-based Enterprise Data Warehouse (EDW) that uses Massively Parallel Processing (MPP).
SQL Data Warehouse stores data into relational tables with columnar storage.
References:
https://docs.microsoft.com/en-us/azure/storage/blobs/data-lake-storage-namespace
https://docs.microsoft.com/en-us/azure/architecture/data-guide/technology-choices/batch-processing
https://docs.microsoft.com/en-us/azure/sql-data-warehouse/sql-data-warehouse-overview-what-is

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Explanation:
Box 1: A security policy for sale
Here are the steps to create a security policy for Sales:
Create a user-defined function that returns the name of the current user:
CREATE FUNCTION dbo.GetCurrentUser()
RETURNS NVARCHAR(128)
AS
BEGIN
RETURN SUSER_SNAME();
END;
Create a security predicate function that filters the Sales table based on the current user:
CREATE FUNCTION dbo.SalesPredicate(@salesperson NVARCHAR(128))
RETURNS TABLE
WITH SCHEMABINDING
AS
RETURN SELECT 1 AS access_result
WHERE @salesperson = SalespersonName;
Create a security policy on the Sales table that uses the SalesPredicate function to filter the data:
CREATE SECURITY POLICY SalesFilter
ADD FILTER PREDICATE dbo.SalesPredicate(dbo.GetCurrentUser()) ON dbo.Sales WITH (STATE = ON); By creating a security policy for the Sales table, you ensure that each salesperson can only access their own sales data. The security policy uses a user-defined function to get the name of the current user and a security predicate function to filter the Sales table based on the current user.
Box 2: table-value function
to restrict row access by using row-level security, you need to create a table-valued function that returns a table of values that represent the rows that a user can access. You then use this function in a security policy that applies a predicate on the table.

Explanation:

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Explanation:

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