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HDPCD Exam

The HDP Certified Developer Exam provides organizations that use Hadoop with a means of identifying qualified staff to develop Hadoop applications for storing, processing, and analyzing data stored in Hadoop using the open-source tools of the Hortonworks Data Platform including Pig, Hive, and Sqoop.

  • Time Limit: 120 minutes
  • Passing Score: 75%
  • Language: English
  • Price: USD $250

Exam Format

The exam is based on the Hortonworks Data Platform 3.1 installed and managed with Ambari 2.7, which includes Pig, Hive, and Sqoop. Each candidate will be given access to an HDP 3.1 cluster along with a list of tasks to be performed on that cluster. No further exam performance details will be provided upon request.

Evaluation, Scoring and Reporting

Exam results are usually reported within 10 business days from Hortonworks University. Proctors and training partners are not authorized to report results directly to candidates. Exam results include the candidate’s final score and the required passing score.

Audience and Pre-requisites

The Minimally Qualified Candidate (MQC) for this certification can develop Hadoop applications for ingesting, transforming, and analyzing data stored in Hadoop using the open-source tools of the Hortonworks Data Platform, including Pig, Hive, and Sqoop. Those certified are recognized as having high level of skill in Hadoop application development and have demonstrated that knowledge by performing the objectives of the HDPCD exam on a live HDP cluster.

Exam Objectives:

The HDP Certified Developer (HDPCD) has three main categories of tasks that involve:

Data Ingestion

  • Import data from a table in a relational database into HDFS
  • Import the results of a query from a relational database into HDFS
  • Import a table from a relational database into a new or existing Hive table
  • Insert or update data from HDFS into a table in a relational database

Data Transformation

  • Write and execute  a Pig script
  • Load data into a Pig relation without a schema
  • Load data into a Pig relation with a schema
  • Load data from a Hive table into a Pig relation
  • Use Pig to transform data into a specified format
  • Transform data to match a given Hive schema
  • Group the data of one or more Pig relations
  • Use Pig to remove records with null values from a relation
  • Store the data from a Pig relation into a folder in HDFS
  • Store the data from a Pig relation into a Hive table
  • Sort the output of a Pig relation
  • Remove the duplicate tuples of a Pig relation
  • Specify the number of reduce tasks for a Pig MapReduce job
  • Join two datasets using Pig
  • Perform a replicated join using Pig
  • Run a Pig job using Tez
  • Within a Pig script, register a JAR file of User Defined Functions
  • Within a Pig script, define an alias for a User Defined Function
  • Within a Pig script, invoke a User Defined Function

Data Analysis

  • Write and execute a Hive query
  • Define a Hive-managed table
  • Define a Hive external table
  • Define a partitioned Hive table
  • Define a bucketed Hive table
  • Define a Hive table from a select query
  • Define a Hive table that uses the ORCFile format
  • Create a new ORCFile table from the data in an existing non-ORCFile Hive table
  • Specify the storage format of a Hive table
  • Specify the delimiter of a Hive table
  • Load data into a Hive table from a local directory
  • Load data into a Hive table from an HDFS directory
  • Load data into a Hive table as the result of a query
  • Load a compressed data file into a Hive table
  • Update a row in a Hive table
  • Delete a row from a Hive table
  • Insert a new row into a Hive table
  • Join two Hive tables
  • Run a Hive query using Tez
  • Run a Hive query using vectorization
  • Output the execution plan for a Hive query
  • Use a subquery within a Hive query
  • Output data from a Hive query that is totally ordered across multiple reducers
  • Set a Hadoop or Hive configuration property from within a Hive query

"Cloudera has not only prepared us for success today, but has also trained us to face and prevail over our big data challenges in the future by using Hadoop."

-Persado

Have questions? Read our Certification FAQ

Contact us at certification@cloudera.com

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