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NEW QUESTION: 1
The DISK_REPAIR_TIME attribute has expired after a transient disk error. What happens next?
A. Oracle ASM drops the disk.
B. This means that the disk is repaired and is put back in the ASM disk group.
C. This means that Oracle will use ASM Fast Mirror Resync to sync the rest of the disks.
D. Nothing, it was only a warning. All the disks are OK
Answer: A

NEW QUESTION: 2
Which of the following MOST effectively demonstrates operational readiness to address information security risk issues?
A. Procedures have been established for assessing and mitigating information security risks.
B. Executive management has announced an information security risk initiative.
C. IT management has communicated the need for information security risk management to the business.
D. A policy has been communicated stating enterprise commitment and readiness to address information security risk.
Answer: A

NEW QUESTION: 3

A. Option E
B. Option B
C. Option D
D. Option A
E. Option C
Answer: A,D,E

NEW QUESTION: 4
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 recommender system.
C. A good use for the wide and deep model is a small-scale linear regression problem.
D. The wide model is used for memorization, while the deep model is used for generalization.
Answer: B,D
Explanation:
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