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NEW QUESTION: 1
A customer is looking to purchase a database management system to handle mixed workloads on distributed platforms. Their main goal is the reduction of operational costs. Which IBM database offering best meets their needs?
A. DB2
B. Informix
C. solidDB
D. IMS
Answer: A
Explanation:
Reference:http://www.ibm.com/developerworks/data/library/dmmag/DMMag_2011_Issue4/Newsb ytes/(first two paragraphs)

NEW QUESTION: 2

A. Option D
B. Option B
C. Option C
D. Option A
Answer: C

NEW QUESTION: 3
あなたのデータベースがARCHIVELOGモードで実行され、自動UNDO管理が有効になっています。フラッシュバック・データベースを有効にする前に、どの2つのタスクを実行する必要がありますか。
A. UNDO保存保証を有効にします。
B. フラッシュバックすることができるデータベースにDB_FLASHBACK_RETENTION_TARGETパラメータが（分）時点に設定されていることを確認します。
C. 最小限のサプリメンタル・ロギングを有効にします。
D. 高速リカバリ領域を有効にします。
E. ごみ箱を有効にします。
Answer: B,D
Explanation:
Explanation
Section: About Flashback Database
Flashback Database uses its own logging mechanism, creating flashback logs and storing them in the fast recovery area. You can only use Flashback Database if flashback logs are available. To take advantage of this feature, you must set up your database in advance to create flashback logs.
References: https://docs.oracle.com/database/121/BRADV/flashdb.htm#BRADV582

NEW QUESTION: 4
What four functions do scheduling algorithms perform on hadoop cluster?
A. Allow multiple users to share clusters in a predictable policy-guided manner
B. Ensure data locality by ordering map tasks so that they run on data local maps slots
C. Reduce the total amount of computation necessary to complete a job.
D. Run jobs at periodic times of the day
E. support the implementation of service-level agreements for multiple cluster users
F. Reduce the job latencies in environment with multiple jobs of different sizes
G. allow short jobs to complete even when large, long jobs (consuming a lot of resources are running)
Answer: C,D,F,G
Explanation:
Hadoop schedulers Since the pluggable scheduler was implemented, several scheduler algorithms have been developed for it. / FIFO scheduler The original scheduling algorithm that was integrated within the JobTracker was called FIFO. In FIFO scheduling, a JobTracker pulled jobs from a work queue, oldest job first. This schedule had no concept of the priority or size of the job, but the approach was simple to implement and efficient. / Fair scheduler The core idea behind the fair share scheduler was to assign resources to jobs such that on average over time, each job gets an equal share of the available resources. The result is that jobs that require less time are able to access the CPU and finish intermixed with the execution of jobs that require more time to execute. This behavior allows for some interactivity among Hadoop jobs and permits greater responsiveness of the Hadoop cluster to the variety of job types submitted. The fair scheduler was developed by Facebook.
/ Capacity scheduler The capacity scheduler shares some of the principles of the fair scheduler but has distinct differences, too. First, capacity scheduling was defined for large clusters, which may have multiple, independent consumers and target applications. For this reason, capacity scheduling provides greater control as well as the ability to provide a minimum capacity guarantee and share excess capacity among users. The capacity scheduler was developed by Yahoo!. In capacity scheduling, instead of pools, several queues are created, each with a configurable number of map and reduce slots. Each queue is also assigned a guaranteed capacity (where the overall capacity of the cluster is the sum of each queue's capacity).
* The introduction of the pluggable scheduler was yet another evolution in cluster computing with Hadoop. The pluggable scheduler permits the use (and development) of schedulers optimized for the particular workload and application. The new schedulers have also made it possible to create multi-user data warehouses with Hadoop, given the ability to share the overall Hadoop infrastructure with multiple users and organizations.