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NEW QUESTION: 1
You need to implement a feature engineering strategy for the crowd sentiment local models.
What should you do?
A. Apply a linear discriminant analysis.
B. Apply a Spearman correlation coefficient.
C. Apply a Pearson correlation coefficient.
D. Apply an analysis of variance (ANOVA).
Answer: A
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: 2
View the Exhibit.


Refer to the exhibit. An architect is planning an HPE Moonshot System with m710 cartridges to support a customer application virtualization solution. The customer indicated that the maximum acceptable response time for the solution is three seconds. The architect has implemented a POC and tested a single cartridge with different numbers of user sessions.
The architect monitored resource utilization throughout the tests. The results of the test with the point at which the response time exceeded three seconds are shown, marked as the "VSI max." What should the architect conclude from this test?
A. None of the monitored resources shows signs of being a bottleneck, so the architect should consider other factors such as the HPE Moonshot switch module.
B. The network adapter becomes a bottleneck before other resources, so the architect should propose m710p cartridges, which provide 10GbE ports.
C. The memory becomes a bottleneck before other resources, so the architect should propose m400 cartridges, which provide a higher memory capacity.
D. CPU becomes a limiting factor around 50 sessions, so the architect should propose at least one cartridge per 45 or 50 users.
Answer: B

NEW QUESTION: 3
For which type of user is Nutanix community edition (CE) designed?
A. A user interested in test driving features on their own test hardware and infrastructure
B. A user who needs a backup environment while upgrading to the latest version of Acropolis
C. A user interested in benchmarking their datacenter
D. A user who wants to test the performance capabilities of Nutanix
Answer: A

NEW QUESTION: 4
POV要件を満たすには、テレメトリメッセージの形式を推奨する必要があります。
何を勧めますか？
A. XML
B. Avro
C. JSON
Answer: C
Explanation:
Scenario: POV Requirements
Ensure that all message content during this phase is human readable to simplify debugging.
Avro uses a binary format, so it is not human readable.
The more lightweight JSON (Javascript object notation) has become a popular alternative to XML for various reasons. A couple obvious ones are:
Less verbose- XML uses more words than necessary
JSON is faster- Parsing XML software is slow and cumbersome.
Reference:
https://blog.cloud-elements.com/json-better-xml