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NEW QUESTION: 1
高レベルの変更とプロジェクトへの広範な利害関係者の関与がある環境に最も適したプロジェクトライフサイクルモデルは次のとおりです。
A. 反復
B. 再帰
C. 適応
D. 予測
Answer: C

NEW QUESTION: 2
Note: This question is part of a series of questions that use the same or similar answer choices. An answer choice may be correct for more than one question in the series. Each question is independent of the other questions in this series. Information and details provided in a question apply only to that question.
You have a Microsoft SQL Server Analysis Services (SSAS) instance that is configured to use multidimensional mode. You create the following cube:

Users need to be able to analyze sales by product and color.
You need to create the dimension.
Which relationship type should you use between the InternetSales table and the new dimension?
A. regular
B. fact
C. no relationship
D. many-to-many
E. referenced
F. data mining
Answer: E
Explanation:
Explanation/Reference:
Explanation:
A reference dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined indirectly to the fact table through a key in another dimension table, as shown in the following illustration.

A reference dimension relationship represents the relationship between dimension tables and a fact table in a snowflake schema design. When dimension tables are connected in a snowflake schema, you can define a single dimension using columns from multiple tables, or you can define separate dimensions based on the separate dimension tables and then define a link between them using the reference dimension relationship setting. The following figure shows one fact table named InternetSales, and two dimension tables called Customer and Geography, in a snowflake schema.

You can create two dimensions related to the InternetSales measure group: a dimension based on the Customer table, and a dimension based on the Geography table. You can then relate the Geography dimension to the InternetSales measure group using a reference dimension relationship using the Customer dimension.
Incorrect Answers:
B: A regular dimension relationship between a cube dimension and a measure group exists when the key column for the dimension is joined directly to the fact table.
C: Fact dimensions, frequently referred to as degenerate dimensions, are standard dimensions that are constructed from attribute columns in fact tables instead of from attribute columns in dimension tables.
E: Many to Many Dimension Relationships.
In most dimensions, each fact joins to one and only one dimension member, and a single dimension member can be associated with multiple facts. In relational database terminology, this is referred to as a one-to-many relationship. However, it is frequently useful to join a single fact to multiple dimension members. For example, a bank customer might have multiple accounts (checking, saving, credit card, and investment accounts), and an account can also have joint or multiple owners. The Customer dimension constructed from such relationships would then have multiple members that relate to a single account transaction.

References: https://docs.microsoft.com/en-us/sql/analysis-services/multidimensional-models-olap-logical- cube-objects/dimension-relationships

NEW QUESTION: 3
What is the purpose of the & character at the end of a command line?
A. It causes the command to be run in the background of the current shell.
B. It puts the command under the job control facilities of the current shell.
C. It causes the command to be run as a child of the initial login shell of the user.
D. It causes standard error to be unbuffered.
Answer: A

NEW QUESTION: 4
Your company is migrating their 30-node Apache Hadoop cluster to the cloud. They want to re-use
Hadoop jobs they have already created and minimize the management of the cluster as much as possible.
They also want to be able to persist data beyond the life of the cluster. What should you do?
A. Create a Google Cloud Dataflow job to process the data.
B. Create a Cloud Dataproc cluster that uses the Google Cloud Storage connector.
C. Create a Hadoop cluster on Google Compute Engine that uses persistent disks.
D. Create a Google Cloud Dataproc cluster that uses persistent disks for HDFS.
E. Create a Hadoop cluster on Google Compute Engine that uses Local SSD disks.
Answer: A