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NEW QUESTION: 1
A cost-conscious customer needs a server upgrade to host a Citrix application virtualization solution. The solution must meet the following requirements:
- Support 1,200 simultaneous users
- Scale out easily to accommodate 20 percent future growth
- Provide optimum performance for applications such as Microsoft Office Suite, Skype for Business, and Adobe Photoshop Which solution should the architect recommend?
A. an HPE Integrity Superdome X System with BL920s Gen8 blades
B. an HPE Integrity Superdome X System with BL920s Gen9 blades
C. an HPE Moonshot Systems with m710 cartridges
D. an HPE Moonshot System with m350 cartridges
Answer: C
NEW QUESTION: 2
As shown in the figure, the R0 and RTB G0/0/1 interfaces are configured with IPV6 addresses of different network prefixes, and the network administrator is on two routers.
Both run the OSPFv3 protocol and configure the OSPFv3 Router ID for each router. The correct description is (). (many selected)
A. RTA can ping RTB
B. RTA and RTB cannot establish OSPFv3 neighbor relationships.
C. RTA cannot ping RTB
D. RTA and RTB can establish OSPFv3 neighbor relationships.
Answer: A,D
NEW QUESTION: 3
Which platform is run directly using a hypervisor?
A. containers
B. applications
C. virtual machines
D. bare metal systems
Answer: C
NEW QUESTION: 4
You are designing a data warehouse with two fact tables. The first table contains sales per month and the second table contains orders per day.
Referential integrity must be enforced declaratively.
You need to design a solution that can join a single time dimension to both fact tables.
What should you do?
A. Create a surrogate key for the time dimension.
B. Join the two fact tables.
C. Create a time dimension that can join to both fact tables at their respective granularity.
D. Create a view on the sales table.
Answer: A
Explanation:
With dimensionally modeled star schemas or snowflake schemas, decision support queries follow a typical pattern: the query selects several measures of interest from the fact table, joins the fact rows with one or several dimensions along the surrogate keys, places filter predicates on the business columns of the dimension tables, groups by one or several business columns, and aggregates the measures retrieved from the fact table over a period of time. The following demonstrates this pattern, which is also sometimes referred to as a star join query: select ProductAlternateKey, CalendarYear,sum(SalesAmount) from FactInternetSales Fact join DimTime on Fact.OrderDateKey = TimeKey join DimProduct on DimProduct.ProductKey = Fact.ProductKey where CalendarYear between 2003 and 2004 and ProductAlternateKey like 'BK%' group by ProductAlternateKey,CalendarYear
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