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NEW QUESTION: 1
Which of the following is an IDS that acquires data and defines a "normal" usage profile for the network or host?
A. inferential anomaly-based ID
B. Statistical Anomaly-Based ID
C. Signature-Based ID
D. dynamical anomaly-based ID
Answer: B
Explanation:
Statistical Anomaly-Based ID - With this method, an IDS acquires data and defines a "normal" usage profile for the network or host that is being monitored. Source: KRUTZ, Ronald L. & VINES, Russel D., The CISSP Prep Guide: Mastering the Ten Domains of Computer Security, 2001, John Wiley & Sons, Page 49
NEW QUESTION: 2
Which two items are examples of IBM Connections applications? (Choose two.)
A. Files
B. IBM Lotus iNotes
C. IBM WebSphere Portal
D. Instant Messaging
E. Wikis
Answer: A,E
NEW QUESTION: 3
What are two methods that can be used to denormalize tables in BigQuery?
A. 1) Use a partitioned table; 2) Join tables into one table
B. 1) Join tables into one table; 2) Use nested repeated fields
C. 1) Use nested repeated fields; 2) Use a partitioned table
D. 1) Split table into multiple tables; 2) Use a partitioned table
Answer: B
Explanation:
The conventional method of denormalizing data involves simply writing a fact, along with all its dimensions, into a flat table structure. For example, if you are dealing with sales transactions, you would write each individual fact to a record, along with the accompanying dimensions such as order and customer information.
The other method for denormalizing data takes advantage of BigQuery's native support for nested and repeated structures in JSON or Avro input data. Expressing records using nested and repeated structures can provide a more natural representation of the underlying data. In the case of the sales order, the outer part of a JSON structure would contain the order and customer information, and the inner part of the structure would contain the individual line items of the order, which would be represented as nested, repeated elements.
Reference: https://cloud.google.com/solutions/bigquery-data-warehouse#denormalizing_data