Apache Pig Analyze, Transform & Optimize Data Exam Questions and Answers

Apache Pig: Analyze, Transform & Optimize Data certification exam assessment practice question and answer (Q&A) dump including multiple choice questions (MCQ) and objective type questions, with detail explanation and reference available free, helpful to pass the Apache Pig: Analyze, Transform & Optimize Data exam and earn Apache Pig: Analyze, Transform & Optimize Data certificate.

Question 1

Which organization originally developed Apache Pig?

A. Facebook
B. Google
C. Yahoo
D. Microsoft

Answer

C. Yahoo

Explanation

Apache Pig was originally developed at Yahoo Research (around 2006) to give researchers an ad‑hoc, higher-level way to create and run Hadoop MapReduce jobs on very large datasets, before later moving into the Apache Software Foundation.

Question 2

What type of syntax does Pig Latin resemble?

A. Python
B. C++
C. SQL
D. XML

Answer

C. SQL

Explanation

Pig Latin is intentionally designed to feel familiar to people who know SQL: its data-processing statements are written in a high-level, query-like style, even though Pig Latin is more procedural (a sequence of steps) than purely declarative SQL.

Question 3

Which prerequisite knowledge is helpful before learning Pig?

A. Hadoop and HDFS basics
B. Cloud deployment strategies
C. HTML basics
D. JavaScript programming

Answer

A. Hadoop and HDFS basics

Explanation

Apache Pig is built directly on top of Hadoop and runs in two primary modes — Local mode and MapReduce mode — both of which interact deeply with the Hadoop ecosystem. To set up and run even a basic Pig script, you need a working Hadoop installation and access to HDFS (Hadoop Distributed File System), since Pig reads input data from HDFS and writes results back to it.

Without a foundational understanding of how Hadoop clusters work, how HDFS stores and retrieves data, and how MapReduce processes jobs, learning Pig becomes significantly harder — making Hadoop and HDFS basics the most essential and practical prerequisite before diving into Apache Pig.

Question 4

Which Pig mode requires Hadoop to be installed?

A. Shell Mode
B. Spark Mode
C. Local Mode
D. MapReduce Mode

Answer

D. MapReduce Mode

Explanation

MapReduce Mode (also called Hadoop Mode) is Apache Pig’s default execution mode, and it explicitly requires access to a working Hadoop cluster and an HDFS installation to function — Pig translates Pig Latin scripts into a series of MapReduce jobs that are then executed on the Hadoop cluster.

In contrast, Local Mode runs entirely on a single JVM using the local file system, making it Hadoop-free and ideal for development, testing, and debugging with smaller datasets. This distinction is fundamental: when you simply type pig at the command line with no flags, you are already in MapReduce Mode and Hadoop must be running and accessible, whereas Local Mode is explicitly invoked with pig -x local and requires no Hadoop setup at all.

Question 5

Which Pig data type is used to represent strings?

A. float
B. long
C. chararray
D. int

Answer

C. chararray

Explanation

In Apache Pig’s type system, chararray is the designated data type for representing text/string values — it stores a sequence of Unicode characters, essentially functioning as Pig Latin’s equivalent of a string in other programming languages.

The other options in this question are all numeric types: int holds 32-bit signed integers, long holds 64-bit signed integers, and float holds 32-bit floating-point numbers — none of which can store textual data the way chararray does. When loading or defining data that contains names, labels, or any text-based fields in a Pig script, chararray is the correct and standard type to use, making it one of the most commonly used data types in everyday Pig Latin scripting.

Question 6

Why might someone use Local Mode in Pig?

A. To run jobs on a large Hadoop cluster
B. To debug Hive scripts
C. To test data quickly on a single machine
D. To store results in Piggy Bank

Answer

C. To test data quickly on a single machine

Explanation

Apache Pig’s Local Mode runs entirely within a single JVM using the local file system, requiring no Hadoop cluster or HDFS installation whatsoever — making it the go-to choice for developers who want to rapidly develop, debug, and test Pig Latin scripts without the overhead of a full distributed environment.

The typical workflow is to use Local Mode with a small, representative subset of data to verify that the logic and structure of your Pig script works correctly, and only once everything runs smoothly do you switch to MapReduce Mode to execute the script against the full large-scale dataset on a Hadoop cluster.

The other options are incorrect because Local Mode has nothing to do with debugging Hive scripts (a separate tool entirely), running jobs on large Hadoop clusters (that is MapReduce Mode’s purpose), or storing results in Pig’s UDF library called PiggyBank.

Question 7

Which command is used to read data into Pig?

A. DUMP
B. FILTER
C. LOAD
D. STORE

Answer

C. LOAD

Explanation

In Pig Latin, the LOAD statement is the operator used to read data from a file system source (local filesystem or HDFS) into a Pig relation so it can be transformed by subsequent operations. By contrast, DUMP prints a relation’s contents to the console (mainly for debugging), FILTER selects records based on a condition, and STORE writes a relation out to storage.

Question 8

Which Pig command is used to view relation results on screen?

A. DUMP
B. STORE
C. ORDER
D. LOAD

Answer

A. DUMP

Explanation

The DUMP command in Pig Latin is the diagnostic operator used to display the contents of a relation directly to the screen (standard output/console), making it an essential tool during development and debugging to verify that your data transformations are producing the expected results at any given step in the script.

The other commands each serve a distinctly different purpose: LOAD reads data into Pig from a file system, STORE writes relation results out to a persistent location on the file system or HDFS, and ORDER sorts a relation by one or more fields — none of which render data visually on screen the way DUMP does.

Question 9

What does the STORE command require in addition to the relation name?

A. A Pig Latin function
B. A MapReduce configuration file
C. A storage location
D. A Hive table name

Answer

C. A storage location

Explanation

The STORE command in Pig Latin follows the syntax STORE relation INTO ‘output_path’ USING storage_function;, meaning it requires at minimum the relation name and a storage location (the output directory path, typically on HDFS or the local file system) marked by the INTO keyword.

The USING clause with a storage function (e.g., PigStorage) is optional and defaults to tab-delimited text if omitted — so while a storage function can be specified, it is not strictly required. The other options are all incorrect: no MapReduce configuration file, Hive table name, or standalone Pig Latin function is needed as part of the STORE syntax to persist your results to disk.

Question 10

Which of the following best describes Apache Pig?

A. A cluster scheduling tool
B. A database management system
C. A replacement for Hadoop HDFS
D. A high-level platform for analyzing large datasets using Pig Latin

Answer

D. A high-level platform for analyzing large datasets using Pig Latin

Explanation

Apache Pig is a high-level data processing platform built on top of Hadoop that allows users to write complex data transformation and analysis tasks using Pig Latin — its own scripting language — which then gets automatically compiled into MapReduce jobs and executed on the Hadoop cluster.

This abstraction is the core value of Pig: instead of writing verbose, low-level MapReduce code in Java, developers can express multi-step data pipelines in far fewer, more readable Pig Latin statements.

The other options are all incorrect — Pig is not a cluster scheduler, not a database management system with CRUD-style operations like a traditional RDBMS, and certainly not a replacement for HDFS (in fact, it depends on HDFS as its underlying storage layer).

Question 11

In which scenario is Pig Local Mode most useful?

A. Running queries on petabyte-scale clusters
B. Performing ETL across multiple clusters
C. Deploying Pig in production environments
D. Testing small datasets on a single machine

Answer

D. Testing small datasets on a single machine

Explanation

Pig Local Mode is specifically designed for development, experimentation, and prototyping — it runs entirely within a single JVM using only the local file system, requiring no Hadoop cluster or HDFS at all. The most practical use case is working with a small, representative subset of your actual data to develop and debug your Pig Latin script logic before scaling up to the full dataset on a Hadoop cluster in MapReduce Mode.

The remaining options are all wrong: petabyte-scale processing, multi-cluster ETL, and production deployments all require the distributed power of MapReduce Mode, since Local Mode is explicitly limited to a single machine and is not suited for large-scale or production workloads.

Question 12

Which Pig data type is best suited for storing decimal values?

A. int
B. long
C. bytearray
D. float

Answer

D. float

Explanation

Pig’s float is the primitive numeric type intended for fractional (decimal) values, i.e., numbers with a decimal point, whereas int and long are integer-only types and bytearray is an untyped/raw bytes container that typically needs casting before numeric operations.

Question 13

What is the role of the Grunt Shell in Pig?

A. It executes Hive queries
B. It uploads files into HDFS
C. It provides an interactive environment for Pig Latin commands
D. It manages Hadoop jobs

Answer

C. It provides an interactive environment for Pig Latin commands

Explanation

The Grunt Shell is Apache Pig’s interactive command-line interface (CLI) that allows users to enter and execute Pig Latin statements one at a time, receiving immediate feedback — making it especially useful for exploratory data analysis, debugging, and learning Pig Latin interactively without needing to write and run a full script file.

It is the default shell that launches when you simply type pig in the terminal, and it also supports running basic file system commands (like ls, mkdir, and cat) directly against HDFS within the same session for convenience.

The other options are all incorrect — Grunt does not execute Hive queries (Hive has its own Beeline/CLI), it is not a file uploader tool, and while Pig jobs ultimately become MapReduce jobs, the Grunt Shell itself is not a Hadoop job manager; that responsibility belongs to Hadoop’s own resource management layer (YARN).

Question 14

Why does Pig Latin use lazy evaluation?

A. To optimize execution by analyzing the full data flow first
B. To bypass Hadoop during execution
C. To ensure scripts execute in local mode
D. To load data instantly

Answer

A. To optimize execution by analyzing the full data flow first

Explanation

Pig Latin uses lazy evaluation — meaning no value or operation is actually computed until the result is truly required (triggered by a DUMP or STORE command) — so that Pig can first build a complete logical plan of the entire data pipeline before executing anything.

This full-pipeline visibility allows Pig’s optimizer to analyze all the steps together, eliminate redundant operations, reorder transformations for efficiency, and produce the most streamlined execution plan possible before a single MapReduce job is submitted to the cluster.

Without lazy evaluation, Pig would have to execute and materialize the results of every intermediate step one at a time, leading to enormous amounts of repeated and unnecessary computation — especially wasteful when working with petabyte-scale datasets in a distributed environment.

Question 15

Which command in Pig writes results into HDFS?

A. STORE
B. LOAD
C. FILTER
D. DUMP

Answer

A. STORE

Explanation

In Pig Latin, STORE is the command used to write the contents of a relation out to storage, and when you specify an HDFS path (e.g., STORE myrel INTO ‘hdfs://…/output/’ USING PigStorage(‘,’);), Pig persists the results into HDFS at that location. By contrast, LOAD reads data from storage into a relation, FILTER transforms a relation by selecting rows, and DUMP prints relation contents to the screen rather than writing them to HDFS.

Question 16

Which of the following best describes Pig Latin?

A. A database query optimizer
B. A procedural scripting language for data analysis
C. A markup language for Hadoop
D. A Java library for MapReduce

Answer

B. A procedural scripting language for data analysis

Explanation

Pig Latin is best described as a procedural scripting language because, unlike SQL which is purely declarative (you specify what result you want), Pig Latin requires you to define the exact step-by-step data flow — each transformation is written as an explicit, ordered statement that builds on the previous one.

The Pig interpreter parses these sequential commands, builds a logical plan for the full data pipeline, and ultimately compiles the script into MapReduce jobs for distributed execution on Hadoop.

The other options are all incorrect: Pig Latin is not a query optimizer (that is an internal component of Pig itself), not a markup language (it has no XML-like structure), and not a Java library — although Pig does allow custom User Defined Functions (UDFs) written in Java to extend its capabilities.

Question 17

What happens if Hadoop is not installed when running Pig in MR Mode?

A. Pig will use Spark as fallback
B. Pig uses HDFS but without MR jobs
C. Pig cannot execute in MR Mode
D. Pig switches to Local Mode automatically

Answer

C. Pig cannot execute in MR Mode

Explanation

MapReduce Mode absolutely requires a working Hadoop cluster and HDFS installation to function — without it, Pig throws a hard error (ERROR 4010: Cannot find hadoop configurations in classpath) and the script fails to execute entirely. Pig does not silently fall back to Spark, Local Mode, or any other execution engine — the user must explicitly resolve the issue either by properly configuring the Hadoop classpath or by manually switching to Local Mode using the pig -x local flag.

This behavior reinforces a key concept: MR Mode and Local Mode are distinct, manually selected execution environments, and Pig will never automatically switch between them — the responsibility for choosing the correct mode always lies with the user.

Question 18

Which Pig operator is best suited for dividing data based on conditions?

A. DISTINCT
B. UNION
C. ORDER
D. SPLIT

Answer

D. SPLIT

Explanation

In Pig Latin, the SPLIT operator is specifically designed to partition (divide) a single input relation into two or more output relations based on one or more boolean conditions, e.g., SPLIT A INTO B IF (cond1), C IF (cond2);. This makes it the best fit when you need multiple conditioned subsets (such as “high spenders” vs “regular users”) produced in one step, whereas DISTINCT removes duplicates, UNION merges relations, and ORDER sorts data rather than dividing it by rules.

Question 19

Which feature makes Pig different from writing native MapReduce jobs?

A. It cannot handle unstructured data
B. It provides a high-level scripting abstraction
C. It requires more lines of code
D. It cannot run on Hadoop

Answer

B. It provides a high-level scripting abstraction

Explanation

The single most defining feature that sets Apache Pig apart from native MapReduce is its high-level scripting abstraction — Pig Latin abstracts away the complex, verbose Java-based MapReduce programming model into a concise, readable, and SQL-like scripting language that dramatically reduces development effort.

A task that would require 200 lines of Java MapReduce code can typically be accomplished in as few as 10 lines of Pig Latin, making data pipeline development significantly faster, easier to maintain, and more accessible to analysts who are not Java programmers.

The other options are all factually wrong — Pig actually handles both structured and unstructured data, it requires fewer lines of code compared to MapReduce, and it runs directly on top of Hadoop by compiling Pig Latin scripts into MapReduce (or Tez/Spark) jobs under the hood.