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NEW QUESTION: 1
Overview
General Overview
ADatum Corporation has offices in Miami and Montreal.
The network contains a single Active Directory forest named adatum.com. The offices connect to each other by using a WAN link that has 5-ms latency. A Datum standardizes its database platform by using SQL Server 2014 Enterprise edition.
Databases
Each office contains databases named Sales, Inventory, Customers, Products, Personnel, and Dev.
Servers and databases are managed by a team of database administrators. Currently, all of the database administrators have the same level of permissions on all of the servers and all of the databases.
The Customers database contains two tables named Customers and Classifications.
The following graphic shows the relevant portions of the tables:

The following table shows the current data in the Classifications table:

The Inventory database is updated frequently.
The database is often used for reporting.
A full backup of the database currently takes three hours to complete.
Stored Procedures
A stored procedure named USP_1 generates millions of rows of data for multiple reports. USP_1 combines data from five different tables from the Sales and Customers databases in a table named Table1.
After Table1 is created, the reporting process reads data from Table1 sequentially several times. After the process is complete, Table1 is deleted.
A stored procedure named USP_2 is used to generate a product list. The product list contains the names of products grouped by category.
USP_2 takes several minutes to run due to locks on the tables the procedure accesses. The locks are caused by USP_1 and USP_3.
A stored procedure named USP_3 is used to update prices. USP_3 is composed of several UPDATE statements called in sequence from within a transaction.
Currently, if one of the UPDATE statements fails, the stored procedure fails. A stored procedure named USP_4 calls stored procedures in the Sales, Customers, and Inventory databases.
The nested stored procedures read tables from the Sales, Customers, and Inventory databases. USP_4 uses an EXECUTE AS clause.
All nested stored procedures handle errors by using structured exception handling. A stored procedure named USP_5 calls several stored procedures in the same database. Security checks are performed each time USP_5 calls a stored procedure.
You suspect that the security checks are slowing down the performance of USP_5. All stored procedures accessed by user applications call nested stored procedures.
The nested stored procedures are never called directly.
Design Requirements
Data Recovery
You must be able to recover data from the Inventory database if a storage failure occurs. You have a Recovery Time Objective (RTO) of 5 minutes.
You must be able to recover data from the Dev database if data is lost accidentally. You have a Recovery Point Objective (RPO) of one day.
Classification Changes
You plan to change the way customers are classified. The new classifications will have four levels based on the number of orders. Classifications may be removed or added in the future. Management requests that historical data be maintained for the previous classifications. Security A group of junior database administrators must be able to manage security for the Sales database. The junior database administrators will not have any other administrative rights. A Datum wants to track which users run each stored procedure.
Storage
ADatum has limited storage. Whenever possible, all storage space should be minimized for all databases and all backups.
Error Handling
There is currently no error handling code in any stored procedure.
You plan to log errors in called stored procedures and nested stored procedures. Nested stored procedures are never called directly.
You need to recommend a solution for the error handling of USP_4.
The solution must handle errors for nested stored procedures in the code for USP_4.
What should you recommend?
A. Use the RAISERROR command in the nested stored procedures.
B. Use the @@ERROR variable in the nested stored procedures.
C. Use the RAISERROR command in USP_4.
D. Use the @@ERROR variable in USP_4.
Answer: A
Explanation:
Explanation/Reference:
Explanation:
- A stored procedure named USP_4 calls stored procedures in the Sales, Customers, and Inventory databases.
The nested stored procedures read tables from the Sales, Customers, and Inventory databases. USP_4 uses an EXECUTE AS clause.

NEW QUESTION: 2
CORRECT TEXT
Problem Scenario 44 : You have been given 4 files , with the content as given below:
spark11/file1.txt
Apache Hadoop is an open-source software framework written in Java for distributed storage and distributed processing of very large data sets on computer clusters built from commodity hardware. All the modules in Hadoop are designed with a fundamental assumption that hardware failures are common and should be automatically handled by the framework spark11/file2.txt
The core of Apache Hadoop consists of a storage part known as Hadoop Distributed File
System (HDFS) and a processing part called MapReduce. Hadoop splits files into large blocks and distributes them across nodes in a cluster. To process data, Hadoop transfers packaged code for nodes to process in parallel based on the data that needs to be processed.
spark11/file3.txt
his approach takes advantage of data locality nodes manipulating the data they have access to to allow the dataset to be processed faster and more efficiently than it would be in a more conventional supercomputer architecture that relies on a parallel file system where computation and data are distributed via high-speed networking spark11/file4.txt
Apache Storm is focused on stream processing or what some call complex event processing. Storm implements a fault tolerant method for performing a computation or pipelining multiple computations on an event as it flows into a system. One might use
Storm to transform unstructured data as it flows into a system into a desired format
(spark11Afile1.txt)
(spark11/file2.txt)
(spark11/file3.txt)
(sparkl 1/file4.txt)
Write a Spark program, which will give you the highest occurring words in each file. With their file name and highest occurring words.
Answer:
Explanation:
See the explanation for Step by Step Solution and configuration.
Explanation:
Solution :
Step 1 : Create all 4 file first using Hue in hdfs.
Step 2 : Load all file as an RDD
val file1 = sc.textFile("sparkl1/filel.txt")
val file2 = sc.textFile("spark11/file2.txt")
val file3 = sc.textFile("spark11/file3.txt")
val file4 = sc.textFile("spark11/file4.txt")
Step 3 : Now do the word count for each file and sort in reverse order of count.
val contentl = filel.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content.2 = file2.flatMap( line => line.splitf ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content3 = file3.flatMap( line > line.split)" ")).map(word => (word,1)).reduceByKey(_
+ _).map(item => item.swap).sortByKey(false).map(e=>e.swap)
val content4 = file4.flatMap( line => line.split(" ")).map(word => (word,1)).reduceByKey(_ +
_ ).map(item => item.swap).sortByKey(false).map(e=>e.swap)
Step 4 : Split the data and create RDD of all Employee objects.
val filelword = sc.makeRDD(Array(file1.name+"->"+content1(0)._1+"-"+content1(0)._2)) val file2word = sc.makeRDD(Array(file2.name+"->"+content2(0)._1+"-"+content2(0)._2)) val file3word = sc.makeRDD(Array(file3.name+"->"+content3(0)._1+"-"+content3(0)._2)) val file4word = sc.makeRDD(Array(file4.name+M->"+content4(0)._1+"-"+content4(0)._2))
Step 5: Union all the RDDS
val unionRDDs = filelword.union(file2word).union(file3word).union(file4word)
Step 6 : Save the results in a text file as below.
unionRDDs.repartition(1).saveAsTextFile("spark11/union.txt")

NEW QUESTION: 3
Which factor is inherent in all business activities and as a result must be routinely addressed in internal and external audits?
A. Change
B. Risk
C. Cost
D. Resources
Answer: B