Apache Pig 101 Cognitive Class Exam Answers

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Introduction to Apache Pig 101

Apache Pig is a high-level platform for processing and analyzing large datasets on Apache Hadoop. It provides a high-level language called Pig Latin, which is used to express data analysis programs. Pig Latin abstracts the complexities of MapReduce programming, making it easier for developers to write data processing pipelines without diving into the intricacies of MapReduce code.

Key Features of Apache Pig:

1. Ease of Use: Pig Latin is designed to be easy to write and understand, especially for those familiar with SQL or scripting languages. It allows developers to focus more on the data flow and transformations rather than low-level programming.
2. Extensibility: Pig provides a rich set of built-in operators for data operations like joins, filters, grouping, and sorting. Additionally, developers can extend Pig by writing User-Defined Functions (UDFs) in Java, Python, or other languages.
3. Optimization: Pig optimizes the data flow automatically, converting Pig Latin scripts into sequences of MapReduce jobs. It applies optimization techniques to improve performance, such as query optimization and execution plans.
4. Integration: Pig integrates seamlessly with other components of the Apache Hadoop ecosystem, such as HDFS (Hadoop Distributed File System) for storage and YARN for resource management.

How Apache Pig Works:

• Script Execution: Developers write Pig scripts in Pig Latin, which describe the data transformations and operations. These scripts are then executed using the Pig runtime environment.
• MapReduce Execution: Internally, Pig translates these scripts into a series of MapReduce jobs, which are executed on the Hadoop cluster. Each operation in Pig Latin corresponds to one or more MapReduce jobs.
• Data Flow: Pig processes data in a pipeline fashion, where each operation takes input data, performs transformations, and produces output that serves as input to the next operation.

In summary, Apache Pig simplifies the development of data processing applications on Hadoop by providing a higher-level abstraction through its Pig Latin language. It is particularly suited for scenarios where complex data transformations and analyses need to be performed on large-scale datasets efficiently.

Apache Pig 101 Cognitive Class Certification Answers

Module 1: Pig Basics Quiz 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.

Module 2: Pig Relational Operators Quiz Answers

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

Module 3: Pig Evolution Function Quiz Answers

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

Apache Pig 101 Final Exam Answers

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