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Cloudera Certified Developer for Apache Hadoop Sample Questions:

1. How does the NameNode detect that a DataNode has failed?

A) The NameNode does not need to know that a DataNode has failed.
B) The NameNode periodically pings the datanode. If the DataNode does not respond, the NameNode considers the DataNode as failed.
C) When HDFS starts up, the NameNode tries to communicate with the DataNode and considers the DataNode as failed if it does not respond.
D) When the NameNode fails to receive periodic heartbeats from the DataNode, it considers the DataNode as failed.

2. Can you use MapReduce to perform a relational join on two large tables sharing a key? Assume that the two tables are formatted as comma-separated file in HDFS.

A) Yes, but only if one of the tables fits into memory.
B) No, MapReduce cannot perform relational operations.
C) Yes, so long as both tables fit into memory.
D) No, but it can be done with either Pig or Hive.
E) Yes.

3. Which happens if the NameNode crashes?

A) The Secondary NameNode seamlessly takes over and there is no service interruption.
B) HDFS becomes unavailable to new MapReduce jobs, but running jobs will continue until completion.
C) HDFS becomes unavailable until the NameNode is restored.
D) HDFS becomes temporarily unavailable until an administrator starts redirecting client requests to the Secondary NameNode.

4. You have an employee who is a Date Analyst and is very comfortable with SQL. He would like to run ad-hoc analysis on data in your HDFS duster. Which of the following is a data warehousing software built on top of Apache Hadoop that defines a simple SQL-like query language well-suited for this kind of user?

A) Hue
B) Hadoop Streaming
C) Oozie
D) Pig
E) Hive
F) Flume
G) Sqoop

5. Custom programmer-defined counters in MapReduce are:

A) Lightweight devices for synchronization within MapReduce programs. You can use counters to coordinate execution between a mapper and a reducer.
B) Lightweight devices for bookkeeping within MapReduce programs.
C) Lightweight devices for ensuring the correctness of a MapReduce program. Mappers Increment counters, and reducers decrement counters. If at the end of the program the counters read zero, then you are sure that the job completed correctly.

Solutions: