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NEW QUESTION: 1
What is the default size of the /var/log partition?
A. 500MB by default or more if configured by an administrator at setup.
B. As defined by parameters set in the node initialization wizard.
C. 500MB or 2GB depending on the version of OneFS.
D. As large as the cluster size minus the production data.
Answer: C

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
You are migrating an on-premises application to Azure. One component of the application is a legacy Windows native executable that performs image processing.
The image processing application must run every hour. During times that the image processing application is not running, it should not be consuming any Azure compute resources.
You need to ensure that the image processing application runs correctly every hour.
Solution: Create an Azure Function to runs the image processing application every hour.
Does the solution meet the goal?
A. Yes
B. No
Answer: A