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NEW QUESTION: 1
Amazon ElastiCacheでは、単一のキャッシュノードの障害は、アプリケーションの可用性とバックエンドデータベースの負荷に影響を与える可能性がありますが、ElastiCacheは障害の発生したキャッシュノードの代替をプロビジョニングし、再配置されます。
この潜在的な可用性の影響を軽減するソリューションは次のうちどれですか？
A. 高容量ノードの数を減らします。
B. メモリと計算容量を、それぞれ容量の小さい多数のキャッシュノードに分散します。
C. メモリと計算容量を、それぞれ容量の小さいキャッシュノードの数を減らして分散します。
D. それぞれが大容量のキャッシュノードを多数含めます。
Answer: B
Explanation:
Explanation
In Amazon ElastiCache, the number of cache nodes in the cluster is a key factor in the availability of your cluster running Memcached. The failure of a single cache node can have an impact on the availability of your application and the load on your back-end database while ElastiCache provisions a replacement for the failed cache node and it get repopulated.
You can reduce this potential availability impact by spreading your memory and compute capacity over a larger number of cache nodes, each with smaller capacity, rather than using a fewer number of high capacity nodes.
http://docs.aws.amazon.com/AmazonElastiCache/latest/UserGuide/CacheNode.Memcached.html
NEW QUESTION: 2
The Cisco Nexus 2248 FEX has been added recently to the network via a vPC to the Nexus 5000 switches; however, connectivity is not yet operational. What is the problem with the configuration?
A. The maximum number of links for pinning purposes should be increased to 2.
B. Ethernet1/18 must be configured without a port channel.
C. Both Nexus 5000 switches must associate to the same FEX number
D. Each Nexus 5000 should have its own FEX to support a straight-through topology.
Answer: B
NEW QUESTION: 3
Which four pieces of information are stored for each prefix in the LFIB? (Choose four.)
A. next-hop IP address
B. outgoing label
C. incoming interface
D. Layer 2 header rewrite information
E. outgoing interface
F. local label
Answer: A,B,E,F
Explanation:
Forwarding Labeled Packets LSR forwards the packet based on: Top Label value of the received packet Corresponding entry in LFIB (LABEL <=> INTERFACE) #show mpls forwarding-table - will show: local label outgoing label prefix (network) outgoing interface next-hop
LSR expects packet to come with "top" label being "Local" (from show mpls forwarding-table).
If Outgoing label is "Aggregate", then that means that this is a summary route and more specific
lookup is performed.
If LSR cannot find label/interface mapping in LFIB, then it drops the packet.
There are several "RESERVED" labels numbered from 0 to 15:
0 - explicit NULL - is used to preserver QoS info through EXP bits. It copies 'ip prec' or DiffServ.
1 - Router alert label - forces LSR to software switch the packet.
3 - Implicit NULL - this label is used for "connected" or "summary" routes. This way LSR signals its
neighbors to execute "POP label" operation on "connected" routes. It is called PHP, Penultimate
Hop Popping, and is used to make sure that LSR does not perform 2 lookups (label + ip).
14 - OEM alert label - is used for monitoring purpose.
In Cisco IOS, the default range is 16 through 100,000, but can be expanded by using "mpls label
range" command.
NEW QUESTION: 4
You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python.
Which format should you use to store this data in HDFS?
A. JSON
B. SequenceFiles
C. CSV
D. Avro
E. HTML
F. XML
Answer: B
Explanation:
Explanation/Reference:
Using Hadoop Sequence Files
So what should we do in order to deal with huge amount of images? Use hadoop sequence files! Those are map files that inherently can be read by map reduce applications - there is an input format especially for sequence files - and are splitable by map reduce, so we can have one huge file that will be the input of many map tasks. By using those sequence files we are letting hadoop use its advantages. It can split the work into chunks so the processing is parallel, but the chunks are big enough that the process stays efficient.
Since the sequence file are map file the desired format will be that the key will be text and hold the HDFS filename and the value will be BytesWritable and will contain the image content of the file.
Reference: Hadoop binary files processing introduced by image duplicates finder