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Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:

1. You want to understand more about how users browse your public website, such as which pages they visit prior to placing an order. You have a farm of 200 web servers hosting your website. How will you gather this data for your analysis?

A) Channel these clickstreams inot Hadoop using Hadoop Streaming.
B) Ingest the server web logs into HDFS using Flume.
C) Import all users' clicks from your OLTP databases into Hadoop, using Sqoop.
D) Write a MapReduce job, with the web servers for mappers, and the Hadoop cluster nodes for reduces.
E) Sample the weblogs from the web servers, copying them into Hadoop using curl.

2. Which two of the following statements are true about Pig's approach toward data? Choose 2 answers

A) Accepts any data: structured or unstructured
B) Accepts tab-delimited text data only
C) Accepts only data that is defined by metadata tables stored in a database
D) Accepts data whether it has metadata or not
E) Accepts only data that has a key/value pair structure

3. Which process describes the lifecycle of a Mapper?

A) The TaskTracker spawns a new Mapper to process each key-value pair.
B) The TaskTracker spawns a new Mapper to process all records in a single input split.
C) The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
D) The JobTracker spawns a new Mapper to process all records in a single file.

4. Your client application submits a MapReduce job to your Hadoop cluster. Identify the Hadoop daemon on which the Hadoop framework will look for an available slot schedule a MapReduce operation.

A) Secondary NameNode
B) DataNode
C) NameNode
D) JobTracker
E) TaskTracker

5. MapReduce v2 (MRv2/YARN) is designed to address which two issues?

A) Single point of failure in the NameNode.
B) HDFS latency.
C) Ability to run frameworks other than MapReduce, such as MPI.
D) Reduce complexity of the MapReduce APIs.
E) Resource pressure on the JobTracker.
F) Standardize on a single MapReduce API.

Solutions: