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Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Hadoop Fundamentals & Architecture | 20% | - HDFS operations and file management - MapReduce concepts and job lifecycle - YARN architecture and job execution |
| Topic 2: Data Ingestion | 25% | - Ingest streaming data with Flume - Import/export data using Sqoop - Load data into HDFS from external sources |
| Topic 3: Apache Pig Development | 30% | - Write and optimize Pig Latin scripts - Debug and tune Pig jobs - Data transformation, filtering, joining, and aggregation |
| Topic 4: Apache Hive Development | 25% | - Create and manage Hive tables, partitions, and buckets - Use Hive functions, views, and metastore - Write and optimize HiveQL queries |
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
When is the earliest point at which the reduce method of a given Reducer can be called?
- A. Not until all mappers have finished processing all records.
- B. As soon as at least one mapper has finished processing its input split.
- C. As soon as a mapper has emitted at least one record.
- D. It depends on the InputFormat used for the job.
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Which process describes the lifecycle of a Mapper?
- A. The JobTracker spawns a new Mapper to process all records in a single file.
- B. The TaskTracker spawns a new Mapper to process each key-value pair.
- C. The TaskTracker spawns a new Mapper to process all records in a single input split.
- D. The JobTracker calls the TaskTracker's configure () method, then its map () method and finally its close () method.
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Which one of the following statements describes a Hive user-defined aggregate function?
- A. Operates on a single input row and produces a table as output
- B. Operates on multiple input rows and creates a single row as output
- C. Operates on multiple input rows and produces a table as output
- D. Operates on a single input row and produces a single row as output
What types of algorithms are difficult to express in MapReduce v1 (MRv1)?
- A. Text analysis algorithms on large collections of unstructured text (e.g, Web crawls).
- B. Large-scale graph algorithms that require one-step link traversal.
- C. Algorithms that require applying the same mathematical function to large numbers of individual binary records.
- D. Relational operations on large amounts of structured and semi-structured data.
- E. Algorithms that require global, sharing states.
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Which best describes how TextInputFormat processes input files and line breaks?
- A. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReaders of both splits containing the broken line.
- B. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the end of the broken line.
- C. The input file is split exactly at the line breaks, so each RecordReader will read a series of complete lines.
- D. Input file splits may cross line breaks. A line that crosses file splits is ignored.
- E. Input file splits may cross line breaks. A line that crosses file splits is read by the RecordReader of the split that contains the beginning of the broken line.
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