CWT-101 Practice Questions

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NEW QUESTION: 1
The network manager has requested a 300-workstation expansion of the network. The workstations are to be installed in a single broadcast domain, but each workstation must have its own collision domain. The expansion is to be as cost-effective as possible while still meeting the requirements.
Which three items will adequately fulfill the request? (Choose three).
A. two IP subnets with a mask of 255.255.255.0
B. one IP subnet with a mask of 255.255.254.0
C. seven 48-port switches
D. one router interface
E. seven 48-port hubs
F. seven router interfaces
Answer: B,C,D
Explanation:
Explanation/Reference:
of 255.255.254.0 can absorb 510 hosts being 23 bits mask and also 7*48 port switches can handle this much hosts and router interface is required to be minimum to avoid unnecessary wastage hence the answers.

NEW QUESTION: 2
Welche Art von digitaler Disruptionstechnologie ist der Schlüssel, um Erkenntnisse von den Datenunternehmen zu erhalten?
A. Künstliche Intelligenz
B. Augmented Reality
C. DevOps
D. Zusammensetzbare Infrastruktur
Answer: A

NEW QUESTION: 3
You are designing a solution for a company. The solution will use model training for objective classification.
You need to design the solution.
What should you recommend?
A. interactive Spark queries
B. an Azure Cognitive Services application
C. a Spark Streaming job
D. Power BI models
E. a Spark application that uses Spark MLib.
Answer: E
Explanation:
Spark in SQL Server big data cluster enables AI and machine learning.
You can use Apache Spark MLlib to create a machine learning application to do simple predictive analysis on an open dataset.
MLlib is a core Spark library that provides many utilities useful for machine learning tasks, including utilities that are suitable for:
Classification
Regression
Clustering
Topic modeling
Singular value decomposition (SVD) and principal component analysis (PCA) Hypothesis testing and calculating sample statistics References:
https://docs.microsoft.com/en-us/azure/hdinsight/spark/apache-spark-machine-learning-mllib-ipython