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NEW QUESTION: 1
A program that defends against a port scanner will attempt to:
A. Limit access by the scanning system to publicly available ports only
B. Sends back bogus data to the port scanner
C. Update a firewall rule in real time to prevent the port scan from being completed
D. Log a violation and recommend use of security-auditing tools
Answer: C
NEW QUESTION: 2
Your database supports an online transaction processing (OLTP) application. The application is undergoing some major schema changes, such as addition of new indexes and materialized views. You want to check the impact of these changes on workload performance.
What should you use to achieve this?
A. Automatic Workload Repository compare reports
B. SQL Performance Analyzer
C. SQL Access Advisor
D. SQL Tuning Advisor
E. Database replay
Answer: B
Explanation:
You can use the SQL Performance Analyzer to analyze the SQL performance impact of any type of system change. Examples of common system changes include:
* Database upgrades
* Configuration changes to the operating system, hardware, or database
* Database initialization parameter changes
* Schema changes, such as adding new indexes or materialized views
* Gathering optimizer statistics
* SQL tuning actions, such as creating SQL profiles
References: http://docs.oracle.com/cd/B28359_01/server.111/b28318/intro.htm#CNCPT961
NEW QUESTION: 3
群衆感情モデルの評価戦略を定義する必要があります。
順番に実行する必要がある3つのアクションはどれですか？回答するには、適切なアクションをアクションのリストから回答エリアに移動し、正しい順序に並べます。
Answer:
Explanation:
Explanation:
Scenario:
Experiments for local crowd sentiment models must combine local penalty detection data.
Crowd sentiment models must identify known sounds such as cheers and known catch phrases. Individual crowd sentiment models will detect similar sounds.
Note: Evaluate the changed in correlation between model error rate and centroid distance In machine learning, a nearest centroid classifier or nearest prototype classifier is a classification model that assigns to observations the label of the class of training samples whose mean (centroid) is closest to the observation.
References:
https://en.wikipedia.org/wiki/Nearest_centroid_classifier
https://docs.microsoft.com/en-us/azure/machine-learning/studio-module-reference/sweep-clustering
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