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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Apache Hive Development | 35% | - HiveQL queries, joins, aggregations, and partitioning - Hive architecture, metastore, and table management - Data types, serialization, and file formats - Performance tuning and indexing |
| Topic 2: Hadoop Fundamentals & HDFS | 20% | - Hadoop 2.0 architecture & YARN - HDFS file operations, permissions, and data management |
| Topic 3: Data Ingestion & Ecosystem Integration | 5% | - Sqoop and Flume usage basics - Workflow orchestration with Oozie |
| Topic 4: Apache Pig Development | 40% | - Data transformation, filtering, grouping, and joining - Pig Latin syntax, data loading, and storage - User-defined functions (UDFs) and optimization - Integration with HCatalog and Hive |
Which project gives you a distributed, Scalable, data store that allows you random, realtime read/write
access to hundreds of terabytes of data?
Correct Answer: A 🗳️
What data does a Reducer reduce method process? Reference: Yahoo! Hadoop Tutorial, Module 4:
MapReduce
Correct Answer: A 🗳️
How are keys and values presented and passed to the reducers during a standard sort and shuffle phase of MapReduce?
Correct Answer: C 🗳️
Which Hadoop component is responsible for managing the distributed file system metadata?
Correct Answer: A 🗳️
You want to Ingest log files Into HDFS, which tool would you use?
Correct Answer: C 🗳️
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