최신Cloudera Certified Developer for Apache Hadoop (CCDH) - CCD-410무료샘플문제

A. The default partitioner assigns key-values pairs to reduces based on an internal random number generator.

B. The default partitioner computes the hash of the key and divides that valule modulo the number of reducers. The result determines the reducer assigned to process the key-value pair.

C. The default partitioner computes the hash of the value and takes the mod of that value with the number of reducers. The result determines the reducer assigned to process the key-value pair.

D. The default partitioner implements a round-robin strategy, shuffling the key-value pairs to each reducer in turn. This ensures an event partition of the key space.

E. The default partitioner computes the hash of the key. Hash values between specific ranges are associated with different buckets, and each bucket is assigned to a specific reducer.

A. The client queries the NameNode for the block location(s). The NameNode returns the block location(s) to the client. The client reads the data directory off the DataNode(s).

B. The client queries all DataNodes in parallel. The DataNode that contains the requested data responds directly to the client. The client reads the data directly off the DataNode.

C. The client contacts the NameNode for the block location(s). The NameNode then queries the DataNodes for block locations. The DataNodes respond to the NameNode, and the NameNode redirects the client to the DataNode that holds the requested data block(s). The client then reads the data directly off the DataNode.

D. The client contacts the NameNode for the block location(s). The NameNode contacts the DataNode that holds the requested data block. Data is transferred from the DataNode to the NameNode, and then from the NameNode to the client.

A. Two

B. One

C. Three

D. Five

E. Six

F. Four

A. KeyValueFileInputFormat

B. SequenceFileInputFormat

C. BDBInputFormat

D. SequenceFileAsTextInputFormat

A. HDFS latency.

B. Single point of failure in the NameNode.

C. Standardize on a single MapReduce API.

D. Ability to run frameworks other than MapReduce, such as MPI.

E. Resource pressure on the JobTracker.

F. Reduce complexity of the MapReduce APIs.

A. Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for reduces.

B. Sample the weblogs from the web servers, copying them into Hadoop using curl.

C. Import all users' clicks from your OLTP databases into Hadoop, using Sqoop.

D. Channel these clickstreams inot Hadoop using Hadoop Streaming.

E. Ingest the server web logs into HDFS using Flume.