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NEW QUESTION: 1
What are three supported methods of importing data into TIBCO MDM? (Choose three.)
A. Flat Files
B. JMS
C. Java API
D. Web Service
E. Database Access
Answer: A,B,D

NEW QUESTION: 2
A company has a SaaS solutions that will uses Azure SQL Database with elastic pools. The solution will have a dedicated database for each customer organization Customer organizations have peak usage at different periods during the year.
Which two factors affect your costs when sizing the Azure SQL Database elastic pools? Each correct answer presents a complete solution.
NOTE: Each correct selection is worth one point.
A. maximum data size
B. number of databases
C. number of read operations
D. eDTUs consumption
E. number of transactions
Answer: A,D
Explanation:
Explanation
A: With the vCore purchase model, in the General Purpose tier, you are charged for Premium blob storage that you provision for your database or elastic pool. Storage can be configured between 5 GB and 4 TB with
1 GB increments. Storage is priced at GB/month.
C: In the DTU purchase model, elastic pools are available in basic, standard and premium service tiers. Each tier is distinguished primarily by its overall performance, which is measured in elastic Database Transaction Units (eDTUs).
References:
https://azure.microsoft.com/en-in/pricing/details/sql-database/elastic/

NEW QUESTION: 3
You has a cluster running with the Fail Scheduler enabled.
There are currently no jobs running on the cluster you submit a job A, so that only job A is running on the cluster. A while later, you submit job B.
Now job A and Job B are running on the cluster al the same time. How will the Fair' Scheduler handle these two Jobs?
A. When job A gets submitted, it consumes all the task slot
B. When job B gets submitted, Job A has to finish first, before job it can get scheduled.
C. When job A gets submitted, it doesn't consume all the task slot
D. When job B gets submitted, it will get assigned tasks, while job A continues to run with fewertasks.
Answer: D
Explanation:
Fair scheduling is a method of assigning resources to jobs such that all jobs get, on average, an equal share of resources over time. When there is a single job running, that job uses the entire cluster. When other jobs are submitted, tasks slots that free up are assigned to the new jobs, so that each job gets roughly the same amount of CPU time. Unlike the default Hadoop scheduler, which forms a queue of jobs, this lets short jobs finish in reasonable time while not starving long jobs. It is also a reasonable way to share a cluster between a number of users. Finally, fair sharing can also work with job priorities - the priorities are used as weights to determine the fraction of total compute time that each job should get.
Reference: Hadoop,Fair Scheduler Guide