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Hortonworks Apache-Hadoop-Developer Exam Overview:
| Certification Vendor: | Hortonworks |
| Exam Name: | Hortonworks Certified Apache Hadoop 2.0 Developer (Pig and Hive Developer) |
| Exam Number: | HADOOP-PR000007 / HDPCD / Apache-Hadoop-Developer |
| Exam Price: | $250 USD |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 8–12 performance-based tasks / approx. 110 multiple-choice |
| Certificate Validity Period: | 2 years |
| Exam Format: | Performance-based hands-on tasks, Multiple-choice questions, Scenario-based tasks |
| Related Certifications: | Hortonworks Certified Associate (HCA) HDP Certified Administrator (HDPCA) |
| Passing Score: | 70% |
| Available Languages: | English |
| Recommended Training: | Apache Pig & Hive Official Documentation Hortonworks Official Developer Training |
| Exam Registration: | Hortonworks Certification Portal |
| Sample Questions: | Hortonworks Apache-Hadoop-Developer Sample Questions |
| Exam Way: | Online proctored exam on a remote HDP 2.2 / 2.4 cluster |
| Pre Condition: | Recommended: 6+ months hands-on experience with HDP, Pig, Hive, Sqoop, Flume; no mandatory prerequisites |
| Official Syllabus URL: | https://hortonworks.com/services/training-certification/ (program retired) |
Hortonworks Apache-Hadoop-Developer Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Ingestion | 25% | - Load data into HDFS from external sources - Ingest streaming data with Flume - Import/export data using Sqoop |
| Apache Pig Development | 30% | - Debug and tune Pig jobs - Write and optimize Pig Latin scripts - Data transformation, filtering, joining, and aggregation |
| Hadoop Fundamentals & Architecture | 20% | - HDFS operations and file management - MapReduce concepts and job lifecycle - YARN architecture and job execution |
| Apache Hive Development | 25% | - Create and manage Hive tables, partitions, and buckets - Write and optimize HiveQL queries - Use Hive functions, views, and metastore |
Hortonworks Hadoop 2.0 Certification exam for Pig and Hive Developer Sample Questions:
1. You want to perform analysis on a large collection of images. You want to store this data in HDFS and process it with MapReduce but you also want to give your data analysts and data scientists the ability to process the data directly from HDFS with an interpreted high-level programming language like Python. Which format should you use to store this data in HDFS?
A) CSV
B) HTML
C) Avro
D) XML
E) SequenceFiles
F) JSON
2. Which one of the following statements is true regarding a MapReduce job?
A) The job's Partitioner shuffles and sorts all (key.value) pairs and sends the output to all reducers
B) The Mapper must sort its output of (key.value) pairs in descending order based on value
C) The default Hash Partitioner sends key value pairs with the same key to the same Reducer
D) The reduce method is invoked once for each unique value
3. Consider the following two relations, A and B.
What is the output of the following Pig commands?
X = GROUP A BY S1; DUMP X;
A) Option D
B) Option B
C) Option C
D) Option A
4. Which HDFS command displays the contents of the file x in the user's HDFS home directory?
A) hadoop fs -Is x
B) hdfs fs -get x
C) hadoop fs -cp x
D) hadoop fs -cat x
5. What is the disadvantage of using multiple reducers with the default HashPartitioner and distributing your workload across you cluster?
A) You will not be able to compress the intermediate data.
B) There are no concerns with this approach. It is always advisable to use multiple reduces.
C) By using multiple reducers with the default HashPartitioner, output files may not be in globally sorted order.
D) You will longer be able to take advantage of a Combiner.
Solutions:
| Question # 1 Answer: C | Question # 2 Answer: A | Question # 3 Answer: A | Question # 4 Answer: D | Question # 5 Answer: C |
By Christine
