DP-900 Korean 無料問題集「Microsoft Azure Data Fundamentals (DP-900 Korean Version)」

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解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: No
The API determines the type of account to create. Azure Cosmos DB provides five APIs: Core (SQL) and MongoDB for document data, Gremlin for graph data, Azure Table, and Cassandra.
Currently, you must create a separate account for each API.
Box 2: Yes
Azure Cosmos DB uses partitioning to scale individual containers in a database to meet the performance needs of your application. In partitioning, the items in a container are divided into distinct subsets called logical partitions. Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Box 3: No
Logical partitions are formed based on the value of a partition key that is associated with each item in a container.
Reference:
https://docs.microsoft.com/en-us/azure/cosmos-db/partitioning-overview

Explanation:
Box 1: Yes
Batch processing refers to processing of high volume of data in batch within a specific time span.
It processes large volume of data all at once. Batch processing is used when data size is known and finite. It takes little longer time to processes data.
Box 2: Yes
Stream processing refers to processing of continuous stream of data immediately as it is produced. It analyzes streaming data in real time. Stream processing is used when the data size is unknown and infinite and continuous.
Box 3: No
Reference:
https://www.geeksforgeeks.org/difference-between-batch-processing-and-stream-processing/

Explanation:
Box 1: Yes
Big data solutions often use long-running batch jobs to filter, aggregate, and otherwise prepare the data for analysis. Usually these jobs involve reading source files from scalable storage (like HDFS, Azure Data Lake Store, and Azure Storage), processing them, and writing the output to new files in scalable storage.
Box 2: Yes
Batch processing can have output to SQL Database, Hive, HBase, Spark SQL.
Box 3: Yes
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/big-data/batch-processing

Explanation:
Box 1: SELECT
Box 2: GROUP BY
Example:
When used with a GROUP BY clause, each aggregate function produces a single value covering each group, instead of a single value covering the whole table. The following example produces summary values for each sales territory in the AdventureWorks2012 database. The summary lists the average bonus received by the sales people in each territory, and the sum of year-to-date sales for each territory.
SELECT TerritoryID, AVG(Bonus)as 'Average bonus', SUM(SalesYTD) as 'YTD sales' FROM Sales.SalesPerson GROUP BY TerritoryID; Reference:
https://docs.microsoft.com/en-us/sql/t-sql/functions/avg-transact-sql

Explanation:
SocialMedia is an Array object.
https://docs.microsoft.com/en-us/answers/questions/200268/adf-reading-array-of-objects-in-data-flow.html
https://docs.microsoft.com/en-us/azure/data-factory/format-json

解説: (JPNTest メンバーにのみ表示されます)

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: Diagnostic
Diagnostic Analytics: At this stage you can begin to answer some of those why questions.
Historical data can begin to be measured against other data to answer the question of why something happened in the past. This is the process of gathering and interpreting different data sets to identify anomalies, detect patters, and determine relationships.
Box 2: Prescriptive
Prescriptive analytics is a combination of data, mathematical models, and various business rules to infer actions to influence future desired outcomes.
Incorrect Answer:
Predictive analytics, broadly speaking, is a category of business intelligence that uses descriptive and predictive variables from the past to analyze and identify the likelihood of an unknown future outcome Box 3: Cognitive Reference:
https://demand-planning.com/2020/01/20/the-differences-between-descriptive-diagnostic-predictive-cognitive-analytics/
https://azure.microsoft.com/en-us/blog/answering-whats-happening-whys-happening-and-what-will-happen-with-iot-analytics/

Explanation:
Azure Hybrid Benefit allows you to use SQL Server licenses with Software Assurance or qualifying subscription licenses to pay a reduced base rate* for these products and services for SQL Server on Azure:
vCPU-based service tiers of Azure SQL Database (excluding serverless).
Azure SQL Managed Instance.
SQL Server in Azure Virtual Machines.
SQL Server Integration Services.
https://azure.microsoft.com/en-us/pricing/hybrid-benefit/faq/
https://azure.microsoft.com/en-us/pricing/hybrid-benefit/#why-azure-hybrid-benefit

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: Azure Data factory
Relevant Azure service for the three ETL phases are Azure Data Factory and SQL Server Integration Services (SSIS).
Box 2: Azure Synapse Analytics
You can copy and transform data in Azure Synapse Analytics by using Azure Data Factory Note: Azure Synapse Analytics connector is supported for the following activities:
Copy activity with supported source/sink matrix table
Mapping data flow
Lookup activity
GetMetadata activity
Reference:
https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl
https://docs.microsoft.com/en-us/azure/data-factory/connector-azure-sql-data-warehouse