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NEW QUESTION: 1
Users have reported that an internally developed web application is acting erratically, and the response output is inconsistent. The issue began after a web application dependency patch was applied to improve security. Which of the following would be the MOST appropriate tool to help identify the issue?
A. HTTP interceptor
B. SCAP scanner
C. Vulnerability scanner
D. Fuzzer
Answer: A

NEW QUESTION: 2
"Automatic placements" on the Networks tab displays:
A. Performance on websites throughout the Google Display Network.
B. A complete listing of existing destination URLs.
C. Ad variations, such as text ads, image ads, video ads, and Google Places ads.
D. Search queries that triggered the ads.
Answer: A

NEW QUESTION: 3
In which of the following phases of system development life cycle (SDLC) is contingency planning most important?
A. Implementation
B. Development/acquisition
C. Operation/maintenance
D. Initiation
Answer: D
Explanation:
Contingency planning requirements should be considered at every phase of SDLC, but most importantly when a new IT system is being conceived. In the initiation phase, system requirements are identified and matched to their related operational processes, allowing determination of the system's appropriate recovery priority. Source: SWANSON, Marianne, & al., National Institute of Standards and Technology (NIST), NIST Special Publication 800-34, Contingency Planning Guide for Information Technology Systems, December 2001 (page 12). and The Official ISC2 Guide to the CBK, Second Edition, Application Security, page 180-185

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