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NEW QUESTION: 1
デバイスセンサーはどのようにしてRADIUSサーバーに情報を送信しますか?
A. コレクター
B. アナライザー
C. 承認
D. 会計
Answer: D

NEW QUESTION: 2
회계사는 자신에게 작은 VPC 네트워크를 설계하라는 요구를하고 비즈니스의 성격 상 네트워크의 작업 부하가 적고 동적 데이터에 액세스하는 빈도가 낮은 곳이 필요합니다. 회계사이기 때문에 저비용 또한 중요한 요소입니다. 요구 사항에 가장 적합한 EBS 볼륨 유형은 무엇입니까?
A. 모두 동일하고 비용이 같습니다.
B. 자기
C. 범용 (SSD)
D. 자기 또는 프로비저닝 된 IOPS (SSD)
Answer: B
Explanation:
설명:
작업 부하의 요구 사항을 충족시킬 수 있도록 3 가지 EBS 볼륨 유형 중에서 선택할 수 있습니다 : 일반 목적 (SSD), 프로비저닝 된 IOPS (SSD) 및 자기 일반용 (SSD)은 새로운 SSD 지원 범용 EBS 볼륨 유형으로, 고객을위한 기본 선택 항목으로 권장합니다. 일반 용도 (SSD) 볼륨은 중소 규모 데이터베이스, 개발 및 테스트 환경 및 부팅 볼륨을 포함하여 광범위한 작업 부하에 적합합니다. 프로비저닝 된 IOPS (SSD) 볼륨은 일관되고 낮은 지연 성능으로 스토리지를 제공하며 대용량 관계형 또는 NoSQL 데이터베이스와 같은 I / O 집약적 인 애플리케이션을 위해 설계되었습니다. 자기 볼륨은 모든 EBS 볼륨 유형의 기가 바이트 당 최저 비용을 제공합니다. 자기 볼륨은 데이터 액세스 빈도가 낮은 작업 부하와 가장 낮은 스토리지 비용이 중요한 응용 프로그램에 이상적입니다.
참조 : https://aws.amazon.com/ec2/faqs/

NEW QUESTION: 3
A company has 10 access point licenses available on their backup Cisco WLC and their primary Cisco WLC is at full capacity, 5 access points are set to high failover priority and 7 access points are set to critical failover priority. During a failure, not all critical access points failed over to the backup Cisco WLC. Which configuration is the cause of this issue?
A. The high priority access point is oversubscribed.
B. The critical priority access point count is oversubscribed.
C. network ap-priority is set to disable.
D. network ap-priority is set to enable.
Answer: A
Explanation:
https://www.ciscolive.com/c/dam/r/ciscolive/emea/docs/2016/pdf/BRKCOL-2275.pdf

NEW QUESTION: 4
CORRECT TEXT
Problem Scenario 76 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.orders
table=retail_db.order_items
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of order table : (orderid , order_date , ordercustomerid, order_status}
.....
Please accomplish following activities.
1 . Copy "retail_db.orders" table to hdfs in a directory p91_orders.
2 . Once data is copied to hdfs, using pyspark calculate the number of order for each status.
3 . Use all the following methods to calculate the number of order for each status. (You need to know all these functions and its behavior for real exam)
- countByKey()
-groupByKey()
- reduceByKey()
-aggregateByKey()
- combineByKey()
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Import Single table
sqoop import --connect jdbc:mysql://quickstart:3306/retail_db --username=retail dba - password=cloudera -table=orders --target-dir=p91_orders
Note : Please check you dont have space between before or after '=' sign. Sqoop uses the
MapReduce framework to copy data from RDBMS to hdfs
Step 2 : Read the data from one of the partition, created using above command, hadoop fs
-cat p91_orders/part-m-00000
Step 3: countByKey #Number of orders by status allOrders = sc.textFile("p91_orders")
#Generate key and value pairs (key is order status and vale as an empty string keyValue = aIIOrders.map(lambda line: (line.split(",")[3], ""))
#Using countByKey, aggregate data based on status as a key
output=keyValue.countByKey()Jtems()
for line in output: print(line)
Step 4 : groupByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split)",")[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.groupByKey().map(lambda kv: (kv[0], sum(kv[1]}}}
tor line in output.collect(): print(line}
Step 5 : reduceByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(","}[3], 1))
#Using countByKey, aggregate data based on status as a key output=
keyValue.reduceByKey(lambda a, b: a + b)
tor line in output.collect(): print(line}
Step 6: aggregateByKey
#Generate key and value pairs (key is order status and vale as an one keyValue = allOrders.map(lambda line: (line.split(",")[3], line}} output=keyValue.aggregateByKey(0, lambda a, b: a+1, lambda a, b: a+b} for line in output.collect(): print(line}
Step 7 : combineByKey
#Generate key and value pairs (key is order status and vale as an one
keyValue = allOrders.map(lambda line: (line.split(",")[3], line))
output=keyValue.combineByKey(lambda value: 1, lambda ace, value: acc+1, lambda ace, value: acc+value) tor line in output.collect(): print(line)
#Watch Spark Professional Training provided by www.ABCTECH.com to understand more on each above functions. (These are very important functions for real exam)