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NEW QUESTION: 1
Which SQL statement should be used to select the minimum and maximum salaries (SALARY), by job code (JOB), from a table EMPLOYEE?
A. SELECT job, MIN(salary), MAX(salary)FROM employeeORDER BY job;
B. SELECT JOB, MIN(salary), MAX(salary)FROM employeeORDER BY job, MIN(salary), MAX(salary);
C. SELECT job, MIN(salary), MAX(salary)FROM employeeGROUP BY job;
D. SELECT job, MIN(salary), MAX(salary)FROM employeeGROUP BY job, MIN(salary), MAX(salary);
Answer: C
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
Where is data compression enabled on a VMAX All Flash array?
A. Data Pool
B. Disk Group
C. Thin devices
D. Storage Group
Answer: D
Explanation:
Reference https://www.emc.com/collateral/white-papers/h15393-vmax-all-flash-adaptive-compression-engine.pdf (Page 4)
NEW QUESTION: 3
What happens when you attempt to compile and run the following code?
#include <iostream>
using namespace std;
#define FUN(arg) if(arg) cout<<"Test";
int main()
{
int i=1;
FUN(i<3);
return 0;
}
A. It prints: T0
B. It prints: T
C. It prints: 0
D. It prints: Test
Answer: D
NEW QUESTION: 4
You have an Azure Stream Analytics query. The query returns a result set that contains 10,000 distinct values for a column named clusterID.
You monitor the Stream Analytics job and discover high latency.
You need to reduce the latency.
Which two actions should you perform? Each correct answer presents a complete solution.
注：それぞれの正しい選択には1ポイントの価値があります。
A. Scale out the query by using PARTITION BY.
B. Increase the number of streaming units.
C. Convert the query to a reference query.
D. Add a temporal analytic function.
E. Add a pass-through query.
Answer: A,B
Explanation:
C: Scaling a Stream Analytics job takes advantage of partitions in the input or output. Partitioning lets you divide data into subsets based on a partition key. A process that consumes the data (such as a Streaming Analytics job) can consume and write different partitions in parallel, which increases throughput.
E: Streaming Units (SUs) represents the computing resources that are allocated to execute a Stream Analytics job. The higher the number of SUs, the more CPU and memory resources are allocated for your job. This capacity lets you focus on the query logic and abstracts the need to manage the hardware to run your Stream Analytics job in a timely manner.
References:
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-parallelization
https://docs.microsoft.com/en-us/azure/stream-analytics/stream-analytics-streaming-unit-consumption
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