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So what else do you need most, So this challenge terrifies many people.

NEW QUESTION: 1
Given:
3.class Employee {
4.String name; double baseSalary;
5.Employee(String name, double baseSalary) {
6.this.name = name;
7.this.baseSalary = baseSalary;
8.}
9.}
10.public class SalesPerson extends Employee {
11.double commission;
12.public SalesPerson(String name, double baseSalary, double commission) {
13.// insert code here
14.}
15.}
Which two code fragments, inserted independently at line 13, will compile? (Choose two.)
A. this.commission = commission;
super(name, baseSalary);
B. this.commission = commission;
C. super(name, baseSalary);
this.commission = commission;
D. this.commission = commission;
super();
E. super(name, baseSalary, commission);
F. super(name, baseSalary);
G. super();
this.commission = commission;
Answer: C,F

NEW QUESTION: 2
You use Power BI Desktop to create a visualization for a Microsoft SQL Server data source.
You need to ensure that you can use R visualization.
Which two actions should you perform? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. Install SQL Server R Services on the server that runs SQL Server.
B. Download and install RStudio Server on the computer that has Power BI Desktop installed.
C. Download and install Microsoft R on the computer that has Power BI Desktop installed.
D. Enable R Scripting on the computer that has Power BI Desktop installed.
E. Download and install Microsoft R Server.
Answer: C
Explanation:
Explanation
References: https://docs.microsoft.com/en-us/power-bi/desktop-r-visuals

NEW QUESTION: 3

A. trimming
B. padding
C. salting
D. hashing
Answer: C,D
Explanation:
Explanation
There are two strategies that you can use to avoid hotspotting:
* Hashing keys
To spread write and insert activity across the cluster, you can randomize sequentially generated keys by hashing the keys, inverting the byte order. Note that these strategies come with trade-offs. Hashing keys, for example, makes table scans for key subranges inefficient, since the subrange is spread across the cluster.
* Salting keys
Instead of hashing the key, you can salt the key by prepending a few bytes of the hash of the key to the actual key.
Note. Salted Apache HBase tables with pre-split is a proven effective HBase solution to provide uniform workload distribution across RegionServers and prevent hot spots during bulk writes. In this design, a row key is made with a logical key plus salt at the beginning. One way of generating salt is by calculating n (number of regions) modulo on the hash code of the logical row key (date, etc).
Reference:
https://blog.cloudera.com/blog/2015/06/how-to-scan-salted-apache-hbase-tables-with-region-specific-key-ranges
http://maprdocs.mapr.com/51/MapR-DB/designing_row_keys_for_mapr_db_binary_tables.html