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Hortonworks HADOOP-PR000007 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Pig Development | 40% | - User-defined functions (UDFs) and optimization - Pig Latin syntax, data loading, and storage - Data transformation, filtering, grouping, and joining - Integration with HCatalog and Hive |
| Topic 2: Hadoop Fundamentals & HDFS | 20% | - HDFS file operations, permissions, and data management - Hadoop 2.0 architecture & YARN |
| Topic 3: Apache Hive Development | 35% | - Performance tuning and indexing - Data types, serialization, and file formats - HiveQL queries, joins, aggregations, and partitioning - Hive architecture, metastore, and table management |
| Topic 4: Data Ingestion & Ecosystem Integration | 5% | - Workflow orchestration with Oozie - Sqoop and Flume usage basics |
Hortonworks-Certified-Apache-Hadoop-2.0-Developer(Pig and Hive Developer) Sample Questions:
You are developing a MapReduce job for sales reporting. The mapper will process input keys
representing the year (IntWritable) and input values representing product indentifies (Text).
Indentify what determines the data types used by the Mapper for a given job.
- A. The data types specified in HADOOP_MAP_DATATYPES environment variable
- B. The InputFormat used by the job determines the mapper's input key and value types.
- C. The mapper-specification.xml file submitted with the job determine the mapper's input key and value
types. - D. The key and value types specified in the JobConf.setMapInputKeyClass and
JobConf.setMapInputValuesClass methods
Correct Answer: B 🗳️
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
- A. Standardize on a single MapReduce API.
- B. Resource pressure on the JobTracker.
- C. Reduce complexity of the MapReduce APIs.
- D. HDFS latency.
- E. Single point of failure in the NameNode.
- F. Ability to run frameworks other than MapReduce, such as MPI.
Correct Answer: B,E 🗳️
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
- A. Algorithms that require global, sharing states.
- B. Large-scale graph algorithms that require one-step link traversal.
- C. Relational operations on large amounts of structured and semi-structured data.
- D. Algorithms that require applying the same mathematical function to large numbers of individual binary
records. - E. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
Correct Answer: A 🗳️
You've written a MapReduce job that will process 500 million input records and generated 500 million
key-value pairs. The data is not uniformly distributed. Your MapReduce job will create a significant
amount of intermediate data that it needs to transfer between mappers and reduces which is a potential
bottleneck. A custom implementation of which interface is most likely to reduce the amount of
intermediate data transferred across the network?
- A. Partitioner
- B. Combiner
- C. Writable
- D. OutputFormat
- E. InputFormat
- F. WritableComparable
Correct Answer: B 🗳️
Review the following data and Pig code:
What command to define B would produce the output (M,62,95l02) when invoking the DUMP operator on
B?
- A. B= GROUP A BY (zip = = '95102' AND gender = = 'M');
- B. B = FILTER A BY (zip = = '95102' AND gender = = M");
- C. B= FOREACH A BY (gender = = 'M' AND zip = = '95102');
- D. B = JOIN A BY (gender = = 'M' AND zip = = '95102');
Correct Answer: B 🗳️
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