Pig Online Test

Q: What are the main Big Data tests?

Some of our main Big Data tests include: Big Data - Pig Test Hadoop Online Test MapReduce Test

The Big Data - Pig Test evaluates a candidate's proficiency with Apache Pig, focusing on Pig Latin programming, data transformations, Hadoop integration, and MapReduce concepts. It screens for skills in data loading, error handling, and performance tuning within Pig scripts using scenario-based MCQs.

Covered skills:

Apache Pig Basics
Pig Latin Programming
Data Transformations
Debugging and Optimization
Hadoop Integration
MapReduce Concepts
Data Loading and Storage
UDFs (User Defined Functions)
Data Analysis with Pig
Schema Design in Pig
Error Handling
Performance Tuning in Pig

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About the Pig Test

The Pig Online Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

Ability to write Apache Pig queries for data processing
Understanding of Hadoop framework and its components
Knowledge of MapReduce programming model
Understanding of Pig Latin scripting language
Ability to optimize and tune Pig scripts for performance

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Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

These are just a small sample from our library of 15,000+ questions. The actual questions on this Big Data - Pig Test will be non-googleable.

🧐 Question
Easy Count number of occurrences Mappers Reducers Data Transformation Mapreduce	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
Medium Hive ngrams Hive Query Understanding Data Analysis	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
Easy P Q relations Pig	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill
	Easy Count number of occurrences Mappers Reducers Data Transformation Mapreduce	3 mins Hadoop	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Medium Hive ngrams Hive Query Understanding Data Analysis	2 mins Hadoop	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	Easy P Q relations Pig	2 mins Hadoop	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

	🧐 Question	🔧 Skill	💪 Difficulty	⌛ Time
	Count number of occurrences Mappers Reducers Data Transformation Mapreduce	Hadoop	Easy	3 mins	Solve
Chusk works as Hadoop developer at Pesla Inc. Chusk is tasked with processing input data to count number of occurrences of each unique word. Chusk did the following to achieve this: 1. Tokenize each word and emit lateral value 1 with Mapper 2. Reducer increments counter for each literal 1 it receives Chusk is now tasked with optimizing this by using a combiner. Will Chusk be able to reuse existing reducers as combiners? A: Yes B: No C: Because the sum operation is both associative and commutative and the input and output types to the reduce method match D: Because the sum operation in the Reducer is incompatible with the operation of a combiner E: Because the combiner is incompatible with a Mapper, which doesn't use the same data type for both the key and value F: Insufficient information
	Hive ngrams Hive Query Understanding Data Analysis	Hadoop	Medium	2 mins	Solve
Assuming the following Hive statements execute successfully, choose the correct statements that describe the result: from fooddata select context_ngrams(sentences(lines), array("twiggy", "romato", null), 68); A. A bigram of the top 68 sentences that contain the substring "twiggy romato" in the lines column of the input data A1 table. B. An 68-value ngram of sentences that contain the words "twiggy" or "romato" in the lines column of the fooddata table. C. A trigram of the top 68 sentences that contain "twiggy romato" followed by a null space in the lines column of the fooddata table. D. A frequency distribution of the top 68 words that follow the subsequence "twiggy romato" in the lines column of the fooddata table.
	P Q relations Pig	Hadoop	Easy	2 mins	Solve
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

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Why you should use Pre-employment Pig Online Test?

The Big Data - Pig Test makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

Working knowledge of Apache Pig and its ecosystem
Understanding of Hadoop and its components
Proficiency in MapReduce programming paradigm
Ability to write Pig Latin scripts for data processing
Knowledge of Pig Latin functions and their usage
Experience in debugging and optimizing Pig scripts
Familiarity with Apache Pig UDFs (User Defined Functions)
Understanding of Pig Latin data types and schemas
Knowledge of Pig Latin built-in functions and operators
Ability to analyze and process large data sets efficiently

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Pig Online Test?

Pig: Pig is a high-level scripting language developed for Apache Hadoop, used for analyzing large data sets. It provides a simple and expressive way to handle data, allowing users to write complex MapReduce tasks easily. Measuring Pig skills in this test helps recruiters assess the candidate's ability to process and analyze big data efficiently.

Big Data Analysis: Big Data Analysis refers to the process of examining and interpreting large and complex data sets to uncover hidden patterns, trends, and insights. Measuring Big Data Analysis skills in this test enables recruiters to evaluate a candidate's proficiency in processing and analyzing massive amounts of data, which is essential in today's data-driven world.

Hadoop: Hadoop is an open-source framework that enables distributed storage and processing of large data sets across clusters of computers. Measuring Hadoop skills in this test helps recruiters gauge a candidate's understanding of this popular technology and their ability to work with distributed systems effectively.

Full list of covered topics

The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for Big Data - Pig Test to be based on.

Pig Latin

Loading and storing data in Pig

Filtering and transforming data in Pig

Joining and grouping data in Pig

Using built-in functions in Pig

Working with Pig UDFs

Debugging and troubleshooting in Pig

Performance optimization in Pig

Pig and Hadoop integration

Hadoop Distributed File System (HDFS)

Hadoop MapReduce framework

Hadoop YARN resource management

Hadoop ecosystem components (Hive, HBase, etc.)

Input formats in Hadoop

Output formats in Hadoop

Mapper and Reducer classes

Hadoop cluster setup and configuration

Hadoop job submission and monitoring

Data serialization in Hadoop

Hadoop batch processing

Hadoop streaming

MapReduce partitioning and sorting

Combiners and partitioners in MapReduce

MapReduce input and output formats

MapReduce counters

MapReduce job chaining

Distributed cache in MapReduce

Job scheduling and task scheduling in Hadoop

Hadoop security and authentication

Pig and MapReduce integration

What roles can I use the Pig Online Test for?

Big Data Engineer
Data Analyst
ETL Developer
Hadoop Developer
Data Scientist
Business Intelligence Developer
Data Architect
Big Data Consultant
Software Engineer
Systems Analyst

How is the Pig Online Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

Experience in working with Pig Latin joins and aggregations
Understanding of Hadoop Distributed File System (HDFS)
Familiarity with Pig Latin transformations and filters
Proficiency in writing MapReduce programs in Java
Knowledge of Hadoop cluster setup and configuration
Ability to troubleshoot and resolve Pig execution errors
Experience in working with Pig Latin control structures
Understanding of data loading and storing in Pig
Knowledge of Pig Latin data transformations
Ability to perform data validation and cleansing in Pig

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Have questions about the Pig Hiring Test?

What is Big Data - Pig Test?

The Big Data - Pig Test evaluates candidates' skills in Apache Pig and related technologies. It is used by recruiters to identify candidates with strong capabilities in Pig Latin Programming, Data Transformations, and Hadoop Integration.

Can I combine Big Data - Pig Test with MapReduce questions?

Yes, you can request a custom test that includes both Big Data - Pig and MapReduce questions. For more details on how we assess MapReduce skills, please visit our MapReduce Test.

What skills are evaluated in the Big Data - Pig Test?

The test evaluates Apache Pig Basics, Pig Latin Programming, Data Transformations, Debugging and Optimization, Hadoop Integration, MapReduce Concepts, Data Loading and Storage, UDFs, Data Analysis with Pig, Schema Design in Pig, Error Handling, and Performance Tuning in Pig.

How to use the Big Data - Pig Test in my hiring process?

Use this test as a pre-screening tool at the start of your recruitment. Add the test link to your job post or invite candidates via email. Adaface helps find skilled candidates faster.

Can I test Big Data - Pig and Hadoop together in a test?

Yes, you can. Combining Big Data - Pig and Hadoop in one test helps evaluate candidates' proficiency in both areas. Check out our Big Data - Pig and Hadoop Combo Test.

What are the main Big Data tests?

Some of our main Big Data tests include:

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

Non-googleable questions
IP proctoring
Screen proctoring
Web proctoring
Webcam proctoring
Plagiarism detection
Secure browser
Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

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