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NEW QUESTION: 1
You are designing a customer dimension for a multidimensional OLAP (MOLAP) database. The dimension table will contain millions of rows of data.
Customer dimension attributes will come from the following two sources:
* An enterprise resource planning (ERP) system that contains millions of rows of data. Attributes from this source will be updated daily.
* A marketing database that contains thousands of rows of data representing customers' geographic information. Attributes from this source will be updated monthly.
Most report queries against the cube use attributes from both sources. Customers in the cube dimension will frequently be filtered by the geographic attributes.
You need to design the dimension table and cube dimension to ensure that report queries perform well, and minimize the time required to reprocess cube attributes.
What should you do? (More than one answer choice may achieve the goal. Select the BEST answer.)
A. Create one dimension table that combines data from both sources. Create a cube dimension for each source.
B. Create a dimension table for each source, and a cube dimension for each source.
C. Create one dimension table that combines data from both sources. Create one cube dimension that references the dimension table.
D. Create a dimension table for each source. Create one cube dimension that joins the two dimension tables together.
Answer: B

NEW QUESTION: 2
What does the Maximum Tolerable Downtime (MTD) determine?
A. The estimated period of time a business critical database can remain down before customers are affected.
B. The estimated period of time a business can remain interrupted beyond which it risks never recovering
C. The fixed length of time a company can endure a disaster without any Disaster Recovery (DR) planning
D. The fixed length of time in a DR process before redundant systems are engaged
Answer: B

NEW QUESTION: 3
Which of the following job types are supported by Cloud Dataproc (select 3 answers)?
A. Hive
B. Spark
C. YARN
D. Pig
Answer: A,B,D
Explanation:
Cloud Dataproc provides out-of-the box and end-to-end support for many of the most popular job types, including Spark, Spark SQL, PySpark, MapReduce, Hive, and Pig jobs.
Reference:
https://cloud.google.com/dataproc/docs/resources/faq#what_type_of_jobs_can_i_run