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NEW QUESTION: 1
Which of the following protocols work at the Network layer of the OSI model?
A. Routing Information Protocol (RIP)
B. Simple Network Management Protocol (SNMP)
C. File Transfer Protocol (FTP)
D. Internet Group Management Protocol (IGMP)
Answer: A,D
NEW QUESTION: 2
シスコエクスプレスフォワーディングのロードバランシングに関する説明として正しいものはどれですか。 (2つ選択してください)
A. 送信元IPアドレスのサブネットマスクを組み合わせて、各宛先のハッシュを作成します
B. 各ハッシュは隣接テーブルの単一のエントリに直接マップされます
C. 各ハッシュは、RIBの単一のエントリに直接マップされます
D. 送信元と宛先のIPアドレスを組み合わせて、各宛先のハッシュを作成します
E. シスコエクスプレスフォワーディングは、最大2つの宛先に負荷を分散できます。
Answer: B,D
Explanation:
Cisco IOS software basically supports two modes of CEF load balancing: On per-destination or perpacket basis.
For per destination load balancing a hash is computed out of the source and destination IP address (-> Answer 'It combines the source and destination IP addresses to create a hash for each destination' is correct). This hash points to exactly one of the adjacency entries in the adjacency table (-> Answer 'Each hash maps directly to a single entry in the adjacency table' is correct), providing that the same path is used for all packets with this source/destination address pair. If per packet load balancing is used the packets are distributed round robin over the available paths. In either case the information in the FIB and adjacency tables provide all the necessary forwarding information, just like for non-load balancing operation.
The number of paths used is limited by the number of entries the routing protocol puts in the routing table, the default in IOS is 4 entries for most IP routing protocols with the exception of BGP, where it is one entry. The maximum number that can be configured is 6 different paths -> Answer 'Cisco Express Forwarding can load-balance over a maximum of two destinations' is not correct.
Reference:
09186a00800afeb7.html
NEW QUESTION: 3
Azure loT Edgeデバイスからデータを取り込み、Azure Machine Learningでデータを前処理し、その後の処理のためにデータをAzure HDInsightに移動するソリューションを設計しています。
ソリューションに何を含めるべきですか?回答するには、回答エリアで適切なオプションを選択します。
注:それぞれの正しい選択には1ポイントの価値があります。
Answer:
Explanation:
Explanation
Box 1: Export Data
The Export data to Hive option in the Export Data module in Azure Machine Learning Studio. This option is useful when you are working with very large datasets, and want to save your machine learning experiment data to a Hadoop cluster or HDInsight distributed storage.
Box 2: Apache Hive
Apache Hive is a data warehouse system for Apache Hadoop. Hive enables data summarization, querying, and analysis of data. Hive queries are written in HiveQL, which is a query language similar to SQL.
Box 3: Azure Data Lake
Default storage for the HDFS file system of HDInsight clusters can be associated with either an Azure Storage account or an Azure Data Lake Storage.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/export-to-hive-query
https://docs.microsoft.com/en-us/azure/hdinsight/hadoop/hdinsight-use-hive