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| Section | Weight | Objectives |
|---|---|---|
| Apache Hive Development | 25% | - Create and manage Hive tables, partitions, and buckets - Use Hive functions, views, and metastore - Write and optimize HiveQL queries |
| Data Ingestion | 25% | - Ingest streaming data with Flume - Import/export data using Sqoop - Load data into HDFS from external sources |
| Apache Pig Development | 30% | - Data transformation, filtering, joining, and aggregation - Debug and tune Pig jobs - Write and optimize Pig Latin scripts |
| Hadoop Fundamentals & Architecture | 20% | - MapReduce concepts and job lifecycle - YARN architecture and job execution - HDFS operations and file management |
You use the hadoop fs -put command to write a 300 MB file using and HDFS block size of 64 MB. Just after this command has finished writing 200 MB of this file, what would another user see when trying to access this life?
Correct Answer: C 🗳️
Which two of the following statements are true about Pig's approach toward data? Choose 2 answers
Correct Answer: B,D 🗳️
Which TWO of the following statements are true regarding Hive? Choose 2 answers
Correct Answer: C,D 🗳️
You have a directory named jobdata in HDFS that contains four files: _first.txt, second.txt, .third.txt and #data.txt. How many files will be processed by the FileInputFormat.setInputPaths () command when it's given a path object representing this directory?
Correct Answer: C 🗳️
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What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster?
Correct Answer: B 🗳️
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