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NEW QUESTION: 1
You have a Hyper-V host named Server1 that runs Windows Server 2016. Server1 has two network adaptors named NK1 and NIC2. Server2 has two virtual switches named vSwitch1 and vSwitch2. N1C1 connects to vSwitch1. NIC2 connects to vSwitch2 Server1 hosts a virtual machine named VM1. VM1 has two network adapters named vmNIC1 and vmNIC1.
VmNIC1 connects to vSwitch1. VmNIC2 connects to vSwitch2.
You need to create a NIC team on VM1.
What should you run on VM1? To answer, select the appropriate options in the answer area.
NOTE: Each correct selection is worth one point.

Answer:
Explanation:

Explanation

Name NICTEAM1 TeamMembers vmNIC1, vmNIC2 -TeamingMode $var1 -LoadBalancingAlgorithm $var2

NEW QUESTION: 2
You observe that the number of spilled records from map tasks for exceeds the number of map output records. You child heap size is 1 GB and your io.sort.mb value is set to 100MB. How would you tune your io.sort.mb value to achieve maximum memory to disk I/O ratio?
A. Tune io.sort.mb value until you observe that the number of spilled records equals (or is as close to equals) the number of map output records.
B. Decrease the io.sort.mb value below 100M
C. Increase the IO.sort.mb as high you can, as close to 1GB as possible.
D. For 1GB child heap size an io.sort.mb of 128MB will always maximum memory to disk I/O.
Answer: A
Explanation:
here are a few tradeoffs to consider.
1.the number of seeks being done when merging files. If you increase the merge factor too high, then the seek cost on disk will exceed the savings from doing a parallel merge (note that OS cache might mitigate this somewhat).
2.Increasing the sort factor decreases the amount of data in each partition. I believe the number is io.sort.mb / io.sort.factor for each partition of sorted data. I believe the general rule of thumb is to have io.sort.mb = 10 * io.sort.factor (this is based on the seek latency of the disk on the transfer speed, I believe. I'm sure this could be tuned better if it was your bottleneck. If you keep these in line with each other, then the seek overhead from merging should be minimized
3.you increase io.sort.mb, then you increase memory pressure on the cluster, leaving less memory available for job tasks. Memory usage for sorting is mapper tasks * io.sort.mb -- so you could find yourself causing extra GCs if this is too high
Essentially,
If you find yourself swapping heavily, then there's a good chance you have set the sort factor too high.
If the ratio between io.sort.mb and io.sort.factor isn't correct, then you may need to change io.sort.mb (if you have the memory) or lower the sort factor.
If you find that you are spending more time in your mappers than in your reducers, then you may want to increase the number of map tasks and decrease the sort factor (assuming there is memory pressure).
Reference: How could I tell if my hadoop config parameter io.sort.factor is too small or too big?
http://stackoverflow.com/questions/8642566/how-could-i-tell-if-my-hadoop-config-parameter-iosort-factor-is-too-small-or-to

NEW QUESTION: 3
タイプCURRのフィールドを透過テーブルに追加します。他に何をしなければなりませんか？
A. タイプCUKYのフィールドへの参照を作成します
B. 定義済みタイプとして新しいフィールドCUKYを作成します
C. タイプCUKYのキーフィールドを追加します
D. タイプCUKYのフィールドを含むチェックテーブルを追加します
Answer: A