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

Explanation:
A treemap chart divides the chart area into rectangles that represent the different levels and relative sizes of the data hierarchy.
Reference:
https://docs.microsoft.com/en-us/sql/reporting-services/report-design/tree-map-and-sunburst- charts-in-reporting-services?view=sql-server-ver15

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: Yes
You can reduce transfer sensitive data with transformations on data source.
Box 2: No
ELT use the compute resource from destination system.
Box 3: Yes
ELT minimizes time with transformations are going realizaded on target data destination systems.
https://docs.microsoft.com/en-us/azure/architecture/data-guide/relational-data/etl

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Batch data can be run weekly, monthly, or even yearly.

Explanation:
Box 1: No
A pipeline is a logical grouping of activities that together perform a task.
Box 2: Yes
You can construct pipeline hierarchies with data factory.
Box 3: Yes
A pipeline is a logical grouping of activities that together perform a task.
Reference:
https://mrpaulandrew.com/2019/09/25/azure-data-factory-pipeline-hierarchies-generation-control/

Explanation:
Box 1: Yes
Azure Databricks can consume data from SQL Databases using JDBC and from SQL Databases using the Apache Spark connector.
The Apache Spark connector for Azure SQL Database and SQL Server enables these databases to act as input data sources and output data sinks for Apache Spark jobs.
Box 2: Yes
You can stream data into Azure Databricks using Event Hubs.
Box 3: Yes
You can run Spark jobs with data stored in Azure Cosmos DB using the Cosmos DB Spark connector. Cosmos can be used for batch and stream processing, and as a serving layer for low latency access.
You can use the connector with Azure Databricks or Azure HDInsight, which provide managed Spark clusters on Azure.
Reference:
https://docs.microsoft.com/en-us/azure/databricks/data/data-sources/sql-databases-azure
https://docs.microsoft.com/en-us/azure/databricks/scenarios/databricks-stream-from-eventhubs

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: No
Microsoft handles all patching and updating of the SQL and operating system code. You don't have to manage the underlying infrastructure.
Box 2: Yes
SQL Database is a fully managed service that has built-in high availability, backups, and other common maintenance operations.
Box 3: No
Reference:
https://docs.microsoft.com/en-us/azure/azure-sql/database/sql-database-paas-overview

Explanation:
Box 1: Azure Synapse Analytics
Output data to parquet format.
Box 2: Azure Data Lake Storage
Azure Data Lake Storage (ADLA) now natively supports Parquet files. ADLA adds a public preview of the native extractor and outputter for the popular Parquet file format Box 3: Azure SQL Database Persist a tabular representation of data that is stored in parquet format.
Reference:
https://docs.microsoft.com/en-us/azure/data-factory/supported-file-formats-and-compression- codecs

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
https://docs.microsoft.com/en-us/azure/cosmos-db/create-graph-gremlin-console

解説: (JPNTest メンバーにのみ表示されます)

Explanation:
Box 1: No
You can duplicate a dashboard. The duplicate ends up in the same Power BI workspace. There is no current functionality that allows you to move reports from one workspace to another.
Box 2: No
Box 3: Yes
Reference:
https://docs.microsoft.com/en-us/power-bi/connect-data/service-datasets-across-workspaces
https://docs.microsoft.com/en-us/power-bi/consumer/end-user-dashboards
https://powerbi.microsoft.com/en-us/excel-and-power-bi/

Explanation:
Box Ingest: Azure Data Factory
You can build a data ingestion pipeline with Azure Data Factory (ADF).
Box Preprocess & model: Azure Synapse Analytics
Use Azure Synapse Analytics to preprocess data and deploy machine learning models.
Reference:
https://docs.microsoft.com/en-us/azure/machine-learning/how-to-data-ingest-adf
https://docs.microsoft.com/en-us/azure/machine-learning/team-data-science-process/sqldw- walkthrough

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