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NEW QUESTION: 1
An 7240 controller is Licensed for 560 APs. The controller has 500 Campus APs terminating on the controller. How many Remote APs can terminate on this controller?
A. 0
B. 1
C. 2
D. 3
E. 4
Answer: B
Explanation:
Explanation/Reference:
Explanation:
NEW QUESTION: 2
Which three design considerations apply to an if-then rule?
A. A while loop can be employed.
B. A rule function can be called.
C. A BPEL scope variable can be defined.
D. Fact object structures can be changed.
E. Aggregations such as count, max, and average can be used.
Answer: A,B,E
NEW QUESTION: 3
A customer is running a production application in an LPAR on a Power 750, and multiple development Workload Partitions (WPARs) in another partition on the same system.What is a benefit of WPARs in this scenario when related to unexpected processing demands?
A. Development WPARs do not interfere with the production LPAR since they run in different partitions, isolated from each other.
B. WPARs can have both shared and private filesystems, allowing for specific application read/write requirements to be satisfied, as well as keeping management efforts reduced.
C. WPARS share a single operating system image, lowering the efforts required to perform operating system maintenance, management and updates.
D. The development WPARs could be moved from one physical system to another using Live Application Mobility, and resources reallocated to the production partition.
Answer: D
NEW QUESTION: 4
기계 학습 전문가는 회사의 판매를 개선하기 위한 시스템을 설계하고 있습니다. 회사의 사용자 행동 및 제품 선호도에 관한 많은 양의 정보를 사용하여 사용자가 다른 사용자와의 유사성을 기반으로 사용자가 원하는 제품을 예측하는 것이 목표입니다.
이 목표를 달성하기 위해 전문가는 어떻게 해야 합니까?
A. Amazon EMR에서 Apache Spark ML을 사용하여 모델 기반 필터링 권장 엔진을 빌드하십시오.
B. Amazon EMR에서 Apache Spark ML과 결합 필터링 권장 엔진을 구축하십시오.
C. Amazon EMR에서 Apache Spark ML을 사용하여 컨텐츠 기반 필터링 권장 엔진을 구축하십시오.
D. Amazon EMR에서 Apache Spark ML을 사용하여 협업 필터링 권장 엔진을 구축하십시오.
Answer: D
Explanation:
Many developers want to implement the famous Amazon model that was used to power the
"People who bought this also bought these items" feature on Amazon.com. This model is based on a method called Collaborative Filtering. It takes items such as movies, books, and products that were rated highly by a set of users and recommending them to other users who also gave them high ratings. This method works well in domains where explicit ratings or implicit user actions can be gathered and analyzed.
https://aws.amazon.com/blogs/big-data/building-a-recommendation-engine-with-spark-ml-on- amazon-emr-using-zeppelin/