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NEW QUESTION: 1
Which of the following are OSPF message types? (Select 4 answers)
A. Link-State Request
B. Hello
C. Database Description
D. Link-State Advertisement
E. Link-State Update
Answer: A,B,C,E

NEW QUESTION: 2
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
A. Apply a Pearson correlation coefficient.
B. Apply a linear discriminant analysis.
C. Apply an analysis of variance (ANOVA).
D. Apply a Spearman correlation coefficient.
Answer: B
Explanation:
The linear discriminant analysis method works only on continuous variables, not categorical or ordinal variables.
Linear discriminant analysis is similar to analysis of variance (ANOVA) in that it works by comparing the means of the variables.
Scenario:
Data scientists must build notebooks in a local environment using automatic feature engineering and model building in machine learning pipelines.
Experiments for local crowd sentiment models must combine local penalty detection data.
All shared features for local models are continuous variables.
Incorrect Answers:
B: The Pearson correlation coefficient, sometimes called Pearson's R test, is a statistical value that measures the linear relationship between two variables. By examining the coefficient values, you can infer something about the strength of the relationship between the two variables, and whether they are positively correlated or negatively correlated.
C: Spearman's correlation coefficient is designed for use with non-parametric and non-normally distributed data. Spearman's coefficient is a nonparametric measure of statistical dependence between two variables, and is sometimes denoted by the Greek letter rho. The Spearman's coefficient expresses the degree to which two variables are monotonically related. It is also called Spearman rank correlation, because it can be used with ordinal variables.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/fisher-linear-discriminant-analysis
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/compute-linear-correlation

NEW QUESTION: 3
View the Exhibit and examine the structure of the PROMOTIONS, SALES, and CUSTOMER tables.

You need to generate a report showing the promo name along with the customer name for all products that were sold during their promo campaign and before 30th October 2007.
You issue the following query:

Which statement is true regarding the above query?
A. It executes successfully but does not give the required result.
B. It produces an error because equijoin and nonequijoin conditions cannot be used in the same SELECT statement.
C. It executes successfully and gives the required result.
D. It produces an error because the join order of the tables is incorrect.
Answer: A

NEW QUESTION: 4
Given an HttpServletRequestrequest and HttpResponseresponse, which sets a cookie
"username" with the value "joe" in a servlet?
A. request.setCookie("username", "joe")
B. response.addCookie("username", "joe")
C. response.addHeader(new Cookie("username", "joe"))
D. request.addCookie("username", "joe")
E. response.addCookie(new Cookie("username", "joe"))
F. request.addCookie(new Cookie("username", "joe"))
G. request.addHeader(new Cookie("username", "joe"))
Answer: E