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NEW QUESTION: 1
あなたの会社は最近、既存のオフィスから通りを下って新しいビルを購入しました。 以下の各作業について、すべてのワイヤレスアクセスポイント（AP）に適したアンテナタイプと暗号化オブジェクトを選択します。
1. セキュリティを最大化しながら、BESTが信号伝播を制御する2つの建物間に暗号化されたワイヤレス接続を作成します。
2. ビル1内のアクセスポイント（AP）によって生成されたワイヤレス信号を制限して、ビル外でのブロードキャストを最小限に抑えます。
3. ビル1内のワイヤレス通信可能範囲を最大化します。
4. ビル1の内部ワイヤレスネットワークは訪問者専用であり、接続するために訪問者のデバイスに特別な設定を要求してはいけません。

説明：シミュレーションが完了したら、[完了]ボタンをクリックして送信します。
Answer:
Explanation:

NEW QUESTION: 2
You are hired as the new head of operations for a SaaS company. Your CTO has asked you to make
debugging any part of your entire operation simpler and as fast as possible. She complains that she has
no idea what is going on in the complex, service-oriented architecture, because the developers just log to
disk, and it's very hard to find errors in logs on so many services. How can you best meet this requirement
and satisfy your CTO?
A. Begin using CloudWatch Logs on every service. Stream all Log Groups into S3 objects. Use AWS
EMR cluster jobs to perform ad-hoc MapReduce analysis and write new queries when needed.
B. Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration
on the <code>PutBucket</code> event and publish events to AWS Kinesis. Use Apache Spark on AWS
EMR to perform at-scale stream processing queries on the log chunks and flag issues.
C. Copy all log files into AWS S3 using a cron job on each instance. Use an S3 Notification Configuration
on the <code>PutBucket</code> event and publish events to AWS Lambda. Use the Lambda to analyze
logs as soon as they come in and flag issues.
D. Begin using CloudWatch Logs on every service. Stream all Log Groups into an AWS Elasticsearch
Service Domain running Kibana 4 and perform log analysis on a search cluster.
Answer: D
Explanation:
The Elasticsearch and Kibana 4 combination is called the ELK Stack, and is designed specifically for
real-time, ad-hoc log analysis and aggregation. All other answers introduce extra delay or require
pre-defined queries.
Amazon Elasticsearch Service is a managed service that makes it easy to deploy, operate, and scale
Elasticsearch in the AWS Cloud. Elasticsearch is a popular open-source search and analytics engine for
use cases such as log analytics, real-time application monitoring, and click stream analytics.
Reference: https://aws.amazon.com/elasticsearch-service/

NEW QUESTION: 3
You are designing an application for shopping list management.
When users attempt to delete a list, you need to warn them and have them explicitly confirm the deletion.Which command surface should you use?
A. notification
B. charms bar
C. flyout menu
D. message dialog
Answer: D

NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 25 : You have been given below comma separated employee information. That needs to be added in /home/cloudera/flumetest/in.txt file (to do tail source) sex,name,city
1 ,alok,mumbai
1 ,jatin,chennai
1 ,yogesh,kolkata
2 ,ragini,delhi
2 ,jyotsana,pune
1,valmiki,banglore
Create a flume conf file using fastest non-durable channel, which write data in hive warehouse directory, in two separate tables called flumemaleemployee1 and flumefemaleemployee1
(Create hive table as well for given data}. Please use tail source with
/home/cloudera/flumetest/in.txt file.
Flumemaleemployee1 : will contain only male employees data flumefemaleemployee1 :
Will contain only woman employees data
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create hive table for flumemaleemployeel and .'
CREATE TABLE flumemaleemployeel
(
sex_type int, name string, city string )
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
CREATE TABLE flumefemaleemployeel
(
sex_type int, name string, city string
)
ROW FORMAT DELIMITED FIELDS TERMINATED BY ',';
Step 2 : Create below directory and file mkdir /home/cloudera/flumetest/ cd
/home/cloudera/flumetest/
Step 3 : Create flume configuration file, with below configuration for source, sink and channel and save it in flume5.conf.
agent.sources = tailsrc
agent.channels = mem1 mem2
agent.sinks = stdl std2
agent.sources.tailsrc.type = exec
agent.sources.tailsrc.command = tail -F /home/cloudera/flumetest/in.txt agent.sources.tailsrc.batchSize = 1 agent.sources.tailsrc.interceptors = i1 agent.sources.tailsrc.interceptors.i1.type = regex_extractor agent.sources.tailsrc.interceptors.il.regex = A(\\d} agent.sources.tailsrc.
interceptors. M.serializers = t1 agent.sources.tailsrc. interceptors, i1.serializers.t1. name = type agent.sources.tailsrc.selector.type = multiplexing agent.sources.tailsrc.selector.header = type agent.sources.tailsrc.selector.mapping.1 = memi agent.sources.tailsrc.selector.mapping.2 = mem2 agent.sinks.std1.type = hdfs
agent.sinks.stdl.channel = mem1
agent.sinks.stdl.batchSize = 1
agent.sinks.std1.hdfs.path = /user/hive/warehouse/flumemaleemployeei
agent.sinks.stdl.rolllnterval = 0
agent.sinks.stdl.hdfs.tileType = Data Stream
agent.sinks.std2.type = hdfs
agent.sinks.std2.channel = mem2
agent.sinks.std2.batchSize = 1
agent.sinks.std2.hdfs.path = /user/hi ve/warehouse/fIumefemaleemployee1 agent.sinks.std2.rolllnterval = 0 agent.sinks.std2.hdfs.tileType = Data Stream agent.channels.mem1.type = memory agent.channels.meml.capacity = 100
agent.channels.mem2.type = memory agent.channels.mem2.capacity = 100
agent.sources.tailsrc.channels = mem1 mem2
Step 4 : Run below command which will use this configuration file and append data in hdfs.
Start flume service:
flume-ng agent -conf /home/cloudera/flumeconf -conf-file
/home/cloudera/fIumeconf/flume5.conf --name agent
Step 5 : Open another terminal create a file at /home/cloudera/flumetest/in.txt.
Step 6 : Enter below data in file and save it.
l.alok.mumbai
1 jatin.chennai
1 ,yogesh,kolkata
2 ,ragini,delhi
2 ,jyotsana,pune
1,valmiki,banglore
Step 7 : Open hue and check the data is available in hive table or not.
Step 8 : Stop flume service by pressing ctrl+c