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NEW QUESTION: 1
Which functions are iBMC's specification of remote management ? (Multiple Choice)
A. redfish API
B. Virtual KVM
C. SOL(Serial over LAN)
D. Virtual Media
Answer: B,C,D

NEW QUESTION: 2
Was ist der Hauptgrund, um sicherzustellen, dass die angemessenen Aufbewahrungsfristen für auf elektronischen Medien gespeicherte Daten eingehalten werden?
A. Zum Erstellen eines Inventars der auf der Festplatte gespeicherten Datenbestände zur Sicherung und Wiederherstellung
B. Reduzierung des CO2-Fußabdrucks durch Eliminierung von Papier
C. Zum Freigeben von Informationen, die nicht ordnungsgemäß klassifiziert wurden
D. Um das Risiko von Verlust, unbefugtem Zugriff, Verwendung, Änderung und Offenlegung zu verringern
Answer: D

NEW QUESTION: 3
Which of the following statements about the Wide & Deep Learning model are true? (Select 2 answers.)
A. The wide model is used for generalization, while the deep model is used for memorization.
B. A good use for the wide and deep model is a small-scale linear regression problem.
C. The wide model is used for memorization, while the deep model is used for generalization.
D. A good use for the wide and deep model is a recommender system.
Answer: C,D
Explanation:
Can we teach computers to learn like humans do, by combining the power of memorization and generalization? It's not an easy question to answer, but by jointly training a wide linear model (for memorization) alongside a deep neural network (for generalization), one can combine the strengths of both to bring us one step closer. At Google, we call it Wide & Deep Learning. It's useful for generic large-scale regression and classification problems with sparse inputs (categorical features with a large number of possible feature values), such as recommender systems, search, and ranking problems.
Reference: https://research.googleblog.com/2016/06/wide-deep-learning-better-together-with.html