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NEW QUESTION: 1
-- Exhibit --
We have been engaged by a research hospital to help upgrade their Isilon installation. They currently have
12 previous generation Isilon nodes with 200TB of capacity and run on a 1Gbps network. They currently have 6 Illumina Hi-Seq Sequencers and an HPC cluster to process data. They would like to expand Isilon to 2PB of active data and 1PB of archive data. They use a third party data and metadata management service (IRODs) to stage data for analysis. The Isilon cluster is mainly used for analysis work with the HPC cluster.
-- Exhibit --
The customer will add two more Illumina Hi-Seq Sequencers in the next 6 months.
Once these are added, what is the total amount of data from all the sequencers they can expect to add per month?
A. 4.8 TB
B. 9.6 TB
C. 14.4 TB
D. 1.2 TB
Answer: B

NEW QUESTION: 2
Which are two major changes SyncE offers over traditional Ethernet to make it suitable for clock distribution? (Choose two.)
A. Industry standard that guarantee interoperability, since granular details, such as specific field values are specified.
B. The ESMC protocol for clock selection, distribution, management, traceability, and failover.
C. A mandated clock accuracy of 4.6ppm.
D. High-priority synchronization packets so it can continually adjust its own oscillator.
E. SyncE introduces the concept of "Boundary Clocks" and "Transparent Clocks" improving network scalability and accuracy of clock synchronization.
Answer: B,C

NEW QUESTION: 3
How often does the typical mobile user check their mobile phone?
A. 150 times per day
B. 1,000 times per day
C. 500 times per day
D. 200 times per day
Answer: A

NEW QUESTION: 4
You are building a regression model for estimating the number of calls during an event.
You need to determine whether the feature values achieve the conditions to build a Poisson regression model.
Which two conditions must the feature set contain? Each correct answer presents part of the solution.
NOTE: Each correct selection is worth one point.
A. The label data must be non-discrete.
B. The label data can be positive or negative.
C. The label data must be whole numbers.
D. The label data must be a positive value.
E. The label data must be a negative value.
Answer: C,D
Explanation:
Explanation/Reference:
Explanation:
Poisson regression is intended for use in regression models that are used to predict numeric values, typically counts. Therefore, you should use this module to create your regression model only if the values you are trying to predict fit the following conditions:
The response variable has a Poisson distribution.

Counts cannot be negative. The method will fail outright if you attempt to use it with negative labels.

A Poisson distribution is a discrete distribution; therefore, it is not meaningful to use this method with

non-whole numbers.
References:
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/poisson-regression