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NEW QUESTION: 1
An IBM Content Manager solution designer is designing an item type for a consumer credit check company. The item type will contain data related to consumer credit checks, company policy states that all credit check data must be automatically deleted after 720 days for security purposes.
Which of the following configuration options would allow Content Manager to automatically delete credit check data at the desired time without the help of a custom application?
A. Create an item type for credit check data, enable system-controlled retention, use a retention policy that is event driven with a 720 day retention period.
B. Create an item type for credit check data and set it to store all document parts to a resource manager which uses a 720 day migration policy.
C. Create an item type for credit check data and set it to store all document parts to a resource manager which stores to e Tivoli Storage Manager server with retention protection enabled.
D. Create an item type for credit check data, enable system-controlled expiration, and use a retention policy with a fixed time option of 720 days.
Answer: D
NEW QUESTION: 2
CORRECT TEXT
Problem Scenario 80 : You have been given MySQL DB with following details.
user=retail_dba
password=cloudera
database=retail_db
table=retail_db.products
jdbc URL = jdbc:mysql://quickstart:3306/retail_db
Columns of products table : (product_id | product_category_id | product_name | product_description | product_price | product_image )
Please accomplish following activities.
1. Copy "retaildb.products" table to hdfs in a directory p93_products
2. Now sort the products data sorted by product price per category, use productcategoryid colunm to group by category
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=products --target-dir=p93
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 : Step 2 : Read the data from one of the partition, created using above command, hadoop fs -cat p93_products/part-m-00000
Step 3 : Load this directory as RDD using Spark and Python (Open pyspark terminal and do following}. productsRDD = sc.textFile(Mp93_products")
Step 4 : Filter empty prices, if exists
#filter out empty prices lines
Nonempty_lines = productsRDD.filter(lambda x: len(x.split(",")[4]) > 0)
Step 5 : Create data set like (categroyld, (id,name,price)
mappedRDD = nonempty_lines.map(lambda line: (line.split(",")[1], (line.split(",")[0], line.split(",")[2], float(line.split(",")[4])))) tor line in mappedRDD.collect(): print(line)
Step 6 : Now groupBy the all records based on categoryld, which a key on mappedRDD it will produce output like (categoryld, iterable of all lines for a key/categoryld) groupByCategroyld = mappedRDD.groupByKey() for line in groupByCategroyld.collect():
print(line)
step 7 : Now sort the data in each category based on price in ascending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price on which it needs to be sorted.
groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2])).take(5)
Step 8 : Now sort the data in each category based on price in descending order.
# sorted is a function to sort an iterable, we can also specify, what would be the Key on which we want to sort in this case we have price which it needs to be sorted.
on groupByCategroyld.map(lambda tuple: sorted(tuple[1], key=lambda tupleValue:
tupleValue[2] , reverse=True)).take(5)
NEW QUESTION: 3
PCIeについて正しいのはどの2つですか？ （2つ選択してください。）
A. PCIe 4.0規格は、PCIe 5.0と比較して転送速度を2倍にします。
B. 4つのレーンで構成されるリンクは、x4リンクと呼ばれます。
C. PCIe標準はバスベースのシステムであり、すべてのデバイスが同じ双方向の32ビットまたは
64ビットのパラレル信号パス。
D. PCI Express x16アダプターをx4スロットにインストールできます。
E. PCleはネイティブのホットスワップ機能をサポートしています。
Answer: A,B