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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Hive Development | 35% | - HiveQL queries, joins, aggregations, and partitioning - Performance tuning and indexing - Data types, serialization, and file formats - Hive architecture, metastore, and table management |
| Topic 2: Hadoop Fundamentals & HDFS | 20% | - HDFS file operations, permissions, and data management - Hadoop 2.0 architecture & YARN |
| Topic 3: Data Ingestion & Ecosystem Integration | 5% | - Workflow orchestration with Oozie - Sqoop and Flume usage basics |
| Topic 4: Apache Pig Development | 40% | - Data transformation, filtering, grouping, and joining - Integration with HCatalog and Hive - Pig Latin syntax, data loading, and storage - User-defined functions (UDFs) and optimization |
MapReduce v2 (MRv2/YARN) is designed to address which two issues?
Correct Answer: E,F 🗳️
Which HDFS command copies an HDFS file named foo to the local filesystem as localFoo?
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?
Correct Answer: B 🗳️
Which of the following tool was designed to import data from a relational database into HDFS?
Correct Answer: C 🗳️
Examine the following Pig commands:
Which one of the following statements is true?
Correct Answer: A 🗳️
Rosalind
Valerie
Allen
Benedict
Chapman
Earl
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