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

Explanation:
The job roles define differentiated tasks and responsibilities. In some organizations, the same person might perform multiple roles; so in their role as database administrator they might provision a transactional database, and then in their role as a data engineer they might create a pipeline to transfer data from the database to a data warehouse for analysis.
https://learn.microsoft.com/en-us/training/modules/explore-roles-responsibilities-world-of-data/2- explore-job-roles

Explanation:
Box 1: Yes
Like IaaS, PaaS includes infrastructure - servers, storage, and networking - but also middleware, development tools, business intelligence (BI) services, database management systems, and more. PaaS is designed to support the complete web application lifecycle: building, testing, deploying, managing, and updating.
PaaS allows you to avoid the expense and complexity of buying and managing software licenses, the underlying application infrastructure and middleware, container orchestrators such as Kubernetes, or the development tools and other resources Box 2: No In PaaS offerings, the underlying OS is managed by Azure, and users do not have control over OS updates or versions. This is handled by the cloud provider to ensure consistent maintenance and security.
Box 3: No
There really is no way to pause / stop billing for your Azure SQL Database. Microsoft's official answer "Yes, you can export your database. Delete the Azure SQL database and that will pause billing. Then when you need it you can create a new database and import your previously exported DB." Reference:
https://azure.microsoft.com/en-us/overview/what-is-paas
https://feedback.azure.com/forums/217321-sql-database/suggestions/6931152-please-add- ability-to-temporarily-turn-off-on-sql

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Cloud features such as scalability, high-availability and multi-tenant capability are included, reducing the amount of coding that developers must do.
Single databases in Azure SQL Database support manual dynamic scalability, but not autoscale.
https://azure.microsoft.com/en-gb/overview/what-is-paas/
https://docs.microsoft.com/en-us/azure/azure-sql/database/scale-resources
https://docs.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview

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Explanation:
Box 1: Yes
Stream processing has access to the most recent data received or data within a rolling time window. Stream processing operates on data in near real-time, allowing for analysis and processing of data as it is received or within a defined time window.
Box 2: No
Batch processing is not required to occur immediately and can have higher latency. Batch processing typically operates on larger volumes of data and is often performed at regular intervals or in scheduled batches, which can have latency in the order of minutes, hours, or even days.
Box 3: Yes
Stream processing is commonly used for simple response functions, aggregates, or calculations such as rolling averages. It enables real-time data analysis and enables quick calculations and aggregations on streaming data as it arrives.
https://docs.microsoft.com/en-us/learn/modules/explore-fundamentals-stream-processing/2- batch-stream
https://www.precisely.com/blog/big-data/big-data-101-batch-stream-processing

Explanation:
Box 1: Batch
The batch processing model requires a set of data that is collected over time while the stream processing model requires data to be fed into an analytics tool, often in micro-batches, and in real-time. The batch Processing model handles a large batch of data while the Stream processing model handles individual records or micro-batches of few records.
In Batch Processing, it processes over all or most of the data but in Stream Processing, it processes over data on a rolling window or most recent record.
Box 2: Batch
Box 3: Streaming
Reference:
https://k21academy.com/microsoft-azure/dp-200/batch-processing-vs-stream-processing

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解説: (JPNTest メンバーにのみ表示されます)

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Explanation:
The following two applications are best suited to batch processing:
- Employee payroll processing and generating payroll checks
- Setting inventory stocking levels based on seasonal sales volume
Data for batch processing is collected over time, often from different data sources, and it is processed as a dataset that includes a range of rows or all rows in the dataset. Batch processing is designed to handle processing of large datasets. There is typically a long latency between data collection and data processing.
The following two applications are best suited to stream processing:
- Reporting the number of users and bandwidth user for an online game
- Identifying detected manufacturing errors to automatically reject failing parts Stream processing is designed for real-time or near real-time data processing, often as a data load process with minimal processing. Data must be able to stream out as quickly as it streams in for processing. Data is either processed as it is generated or in micro-batches of a very few rows with latency of no more than a few milliseconds.