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NEW QUESTION: 1
On the SteelCentral Controller for SteelHead Mobile, which report would you use to examine data reduction over time?
A. Endpoint History
B. Endpoint Report
C. Desktop Traffic
D. Desktop Bandwidth
Answer: D
Explanation:
The Desktop Bandwidth report answers the following questions:
* How much bandwidth optimization has occurred on the SteelHead Mobile clients as a result of data optimization?
* What was the average and peak reduction of data sent by SteelHead Mobile clients?
* What was the overall increase in the amount of data that can be transmitted as a result of data optimization?
Incorrect:
not C: The Endpoint History report answers the following questions:
How many endpoints connected, over time?
When were the most endpoints connected?
How many licenses were added in a given period?
References: SteelCentral Controller for SteelHead, Mobile User's Guide, Version 4.8, April 2016, page 166
https://support.riverbed.com/bin/support/download?did=ponkltmr1vg9mv23ok2fmtgkj4
NEW QUESTION: 2
You administer Windows 10 Enterprise client computers in your company network.
You change settings on a reference computer by using the Windows Firewall with Advanced Security tool. You want to apply the same settings to other computers.
You need to save the Windows Firewall with Advanced Security configuration settings from the reference computer. You also need to be able to import the configuration settings into a Group Policy object later.
What should you do?
A. Run the netsh firewall export c:\settings.xmlcommand.
B. Run the netsh advfirewall export c:\settings.wfwcommand.
C. Open Local Group Policy Editor, select the Windows Firewall with Advanced Security node, and then select the Export Policy action.
D. Run the netsh advfirewall export c:\settings.xmlcommand
Answer: B
NEW QUESTION: 3
Examine the structure of the PROMOS table:
You want to display the list of promo names with the message 'Same Day' for promos that started and ended on the same day.
Which query gives the correct output?
A. SELECT promo_name, NVL(TRUNC(promo_end_date - promo_start_date), 'Same
Day') FROM promos;
B. SELECT promo_name, NVL2(TO_CHAR(TRUNC(promo_end_date-promo_start_date)),
NULL,'Same Day')
FROM promos;
C. SELECT promo_name, DECODE((NULLIF(promo_start_date, promo_end_date)),
NULL,'Same day') FROM promos;
D. SELECT promo_name, NVL(NULLIF(promo_start_date, promo_end_date), 'Same Day')
FROM promos;
Answer: C
Explanation:
The NULLIF Function The NULLIF function tests two terms for equality. If they are equal the function returns a null, else it returns the first of the two terms tested. The NULLIF function takes two mandatory parameters of any data type. The syntax is NULLIF(ifunequal, comparison_term), where the parameters ifunequal and comparison_term are compared. If they are identical, then NULL is returned. If they differ, the ifunequal parameter is returned ANSWER A - date and String incompatibl;a datatypes for NVL function The Date TRUNC Function The date TRUNC function performs a truncation operation on a date value based on a specified date precision format. The date TRUNC function takes one mandatory and one optional parameter. Its syntax is TRUNC(source date, [date precision format]). The source date parameter represents any value that can be implicitly converted into a date item. The date precision format parameter specifies the degree of truncation and is optional. If it is absent, the default degree of truncation is day. This means that any time component
NEW QUESTION: 4
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
A. The wide model is used for memorization, while the deep model is used for generalization.
B. A good use for the wide and deep model is a recommender system.
C. The wide model is used for generalization, while the deep model is used for memorization.
D. A good use for the wide and deep model is a small-scale linear regression problem.
Answer: A,B
Explanation:
Explanation
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html
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