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NEW QUESTION: 1
You decide to create a cluster which runs HDFS in High Availability mode with automatic failover, using Quorum-based Storage. What is the purpose of ZooKeeper in such a configuration?
A. It both keeps track of which NameNode is Active at any given time, and manages the Edits file, which is a log of changes to the HDFS filesystem
B. It manages the Edits file, which is a log changes to the HDFS filesystem.
C. Clients connect toZoneKeeper to determine which NameNode is Active
D. It monitors an NFS mount point and reports if the mount point disappears
E. It only keeps track of which NameNode is Active at any given time
Answer: E
Explanation:
Reference:http://www.cloudera.com/content/cloudera-content/clouderadocs/CDH4/latest/PDF/CDH4-High-Availability-Guide.pdf(page 15)
NEW QUESTION: 2
Which of the following information can be included in a basic network layer packet? (Multiple choice)
A. Path record
B. Network layer tail
C. Network layer header
D. Upper layer data
E. Data link layer header
Answer: C,D
NEW QUESTION: 3
You need to define an evaluation strategy for the crowd sentiment models.
Which three actions should you perform in sequence? To answer, move the appropriate actions from the list of actions to the answer area and arrange them in the correct order.
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
