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Apache Pig 101 Cognitive Class Exam Quiz Answers

Apache Pig 101 Cognitive Class Certification Answers

Question 1: What are the five ways to invoke Pig?

  • Script, Interactive Mode, Java Command, Interactive Local Mode, Interactive MapReduce Mode
  • Interactive External Mode, Interactive Mode, Script, Java Command, Interactive MapReduce Mode
  • Interactive Service Mode, Interactive Local Mode, Interactive External Mode, Interactive MapReduce Mode, Java Command
  • Interactive Local Mode, Interactive MapReduce Mode, Interactive External Mode, Interactive Mode, Script

Question 2: Bags are groups of tuples, tuples are groups of fields, and fields are composed of scalar data types. True or false?

  • True
  • False

Question 3: Which of the following statements is true?

  • Names of relations and fields, as well as keywords and operators, are case sensitive. However, function names are case insensitive.
  • Keywords and operator names are case sensitive.
  • Function names are case sensitive.
  • Names of relations are case sensitive, but names of fields are case insensitive.

Question 1: For the tuples (3,5,2) (5,2,1) (3,7,3) (3,6,1), using the GROUP operator on the third field produces the following: (2,{(3,5,2)}), (1,{(5,2,1),(3,6,1)}), (3,{(3,7,3)}). True or false? Disregard order when answering.

  • True
  • False

Question 2: UNION, GROUP, and COGROUP can be used interchangeably without creating different outputs. True or false?

  • True
  • False

Question 3: Which operators can be used within a nested FOREACH block?

  • LIKE, COUNT, LIMIT, ORDER BY
  • COUNT, ORDER BY, AVG, DISTINCT
  • AVG, LIMIT, FILTER, LIKE
  • LIMIT, DISTINCT, ORDER BY, FILTER

Question 1: The COUNT operator does NOT require the use of the GROUP BY operator. True or false?

  • True
  • False

Question 2: The TOKENIZE() function splits a string and outputs a bag of words. True or false?

  • True
  • False

Question 3: The two types of UDFs are DEFINE and REGISTER. True or false?

  • True
  • False

Question 1: What is the primary purpose of Pig in the Hadoop architecture?

  • To provide logging support for Hadoop jobs
  • To support the execution of workflows consisting of a collection of actions
  • To provide a high-level programming language so that developers can simplify the task of writing MapReduce applications
  • To move data into HDFS

Question 2: When executing Pig in local mode, the process runs locally, but all of the data files are accessed via HDFS. True or false?

  • True
  • False

Question 3: Data can be loaded into Pig with or without defining a schema. True or false?

  • True
  • False

Question 4: In Pig, you can specify the delimiter used to load data by

  • doing nothing. Pig can automatically detect the delimiter used in your data file
  • adding a schema definition to your LOAD statement
  • adding ‘using PigStorage(delimiter)’ to your LOAD statement
  • All of the above

Question 5: Which of the following can be used to pass parameters into a Pig Script? Select all that apply.

  • Command line parameters
  • A parameter files
  • JSON
  • Web Services

Question 6: Which Pig Operator is used to save data into a file?

  • SAVE
  • LOAD
  • STORE
  • DUMP

Question 7: In Pig, all tuples in a relation must have the same number of fields. True or false?

  • True
  • False

Question 8: Which Pig relational operator is used to select tuples from a relation based on some criteria?

  • transform
  • filter
  • group
  • order by

Question 9: Which Pig relational operator is used to combine all the tuples in a relation that have the same key?

  • union
  • transform
  • filter
  • group
  • join

Question 10: Which Pig relational operator is used to combine two or more relations using one or more common field values?

  • union
  • transform
  • filter
  • group
  • join

Question 11: The Pig Tokenize evaluation operator splits a string and outputs a bag of words. True or false?

  • True
  • False

Question 12: When using the Pig Count evaluation operator, you must also use either the Group All or the Group By operator. True or false?

  • True
  • False

Question 13: Which of the following Pig operators can be used to review the logical, physical, and MapReduce execution plans?

  • Verbose
  • Dump
  • Store
  • Explain

Question 14: Which of the following is a valid Pig evaluation operator?

  • isempty
  • count_star
  • diff
  • count
  • All of the Above

Question 15: You can extend Pig via user defined functions. True or false?

  • True
  • False

Introduction to Apache Pig 101


Apache Pig is a high-level platform for analyzing large datasets in Apache Hadoop. It provides a high-level language called Pig Latin, which simplifies the programming of complex data transformations such as joins, filters, and aggregations. Here’s a basic introduction to Apache Pig:

  1. What is Apache Pig? Apache Pig is a platform for analyzing large datasets that reside in Hadoop clusters. It provides an abstraction over the MapReduce programming model, allowing developers to focus more on the data manipulation tasks rather than dealing with the complexities of MapReduce programming.
  2. Pig Latin: Pig Latin is the language used in Apache Pig for expressing data transformations. It is similar to SQL in terms of its declarative nature but is specifically designed for Hadoop processing. Pig Latin scripts are translated into MapReduce jobs by the Pig runtime.
  3. Key Concepts:
    • Relations: In Pig, data is organized into relations, which are similar to tables in a database. Each relation consists of a schema that defines the fields and their types.
    • Load and Store: Pig provides functions to load data from various sources into relations and to store the results of computations back to disk.
    • Transformations: Pig Latin supports a wide range of transformations such as filtering, grouping, joining, and sorting.
    • User-Defined Functions (UDFs): Pig allows users to write custom functions in Java, Python, or other languages, which can then be used within Pig Latin scripts.
  4. Execution: Pig scripts are executed by the Pig runtime, which translates them into a series of MapReduce jobs. These jobs are then submitted to the Hadoop cluster for execution.
  5. Advantages:
    • Simplicity: Pig Latin abstracts away the complexities of MapReduce programming, making it easier to write and maintain data processing workflows.
    • Flexibility: Pig supports a wide range of data transformations and can integrate with other Hadoop ecosystem tools.
    • Extensibility: Users can write custom functions and incorporate them into Pig scripts.

Apache Pig is a powerful tool for data processing in Hadoop environments, particularly for users who are more comfortable with scripting languages than low-level programming with MapReduce.

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