Why you should use MapReduce Online Test?

The MapReduce 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: Ability to implement MapReduce algorithms for big data processing Proficiency in Hadoop ecosystem and its components Understanding of distributed computing principles Capability to analyze data using MapReduce techniques Knowledge of Hadoop's architecture and its role in big data processing Expertise in data processing using MapReduce frameworks Proficient in parallel computing for efficient data processing Ability to aggregate and transform data using MapReduce Experience in performance optimization for MapReduce jobs

MapReduce Online Test

Q: What topics are covered in the MapReduce Online Test?

MapReduce Big Data Processing Distributed Computing Data Analysis Hadoop Data Processing Parallel Computing Data Aggregation Data Transformation Performance Optimization

About the test:

The MapReduce Online Test uses scenario-based MCQs to evaluate candidates on their knowledge of MapReduce framework, including their proficiency in working with Hadoop, HDFS, and YARN. The test also evaluates a candidate's familiarity with Pig and Hive for data analysis and their ability to work with Big Data technologies. The test aims to evaluate a candidate's ability to design and develop applications using MapReduce framework and related technologies effectively.

Covered skills:

MapReduce
Distributed Computing
Hadoop
Parallel Computing
Data Transformation

Big Data Processing
Data Analysis
Data Processing
Data Aggregation
Performance Optimization

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9 reasons why

Adaface MapReduce Test is the most accurate way to shortlist Big Data Developers

Reason #1

Tests for on-the-job skills

The MapReduce 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 efficient MapReduce programs
Understanding of Big Data processing principles
Knowledge of distributed computing concepts
Proficiency in data analysis techniques
Experience with Hadoop framework
Ability to process large volumes of data
Understanding of parallel computing principles
Skills in data aggregation and summarization
Proficiency in data transformation and manipulation
Knowledge of performance optimization techniques

Reason #2

No trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface

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Reason #3

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 10,000+ questions. The actual questions on this MapReduce Test will be non-googleable.

🧐 Question
Easy Count number of occurrences Mappers Reducers	Try practice test
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	Try practice test
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	Try practice test
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	3 mins Hadoop	Try practice test
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	2 mins Hadoop	Try practice test
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	Try practice test
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	Hadoop	Easy	3 mins	Try practice test
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	Hadoop	Medium	2 mins	Try practice test
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	Try practice test
Consider the following two relations, P and Q: What is the output of the following Pig command? Q = GROUP P BY p2; DUMP Q;

Reason #4

1200+ customers in 75 countries

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!

Brandon Lee, Head of People, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment MapReduce Test in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests

Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the MapReduce Test from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

Reason #7

Detailed scorecards & benchmarks

View sample scorecard

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Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

Reason #9

Advanced Proctoring

Learn more

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View sample scorecard

About the MapReduce Assessment Test

Why you should use Pre-employment MapReduce Online Test?

The MapReduce 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:

Ability to implement MapReduce algorithms for big data processing
Proficiency in Hadoop ecosystem and its components
Understanding of distributed computing principles
Capability to analyze data using MapReduce techniques
Knowledge of Hadoop's architecture and its role in big data processing
Expertise in data processing using MapReduce frameworks
Proficient in parallel computing for efficient data processing
Ability to aggregate and transform data using MapReduce
Experience in performance optimization for MapReduce jobs

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 MapReduce Online Test?

MapReduce
MapReduce is a programming model and software framework used for processing and generating large datasets in a distributed computing environment. It allows for parallel execution of data processing tasks across a cluster of computers, making it suitable for big data processing. Assessing MapReduce skills in this test will help recruiters evaluate candidates' ability to efficiently utilize this important technique in big data processing.
Big Data Processing
Big data processing involves the management and analysis of large volumes of complex data from various sources. It requires techniques and tools, such as MapReduce, to efficiently process and extract meaningful insights from the data. Evaluating candidates' skills in big data processing will help recruiters identify individuals who can handle the challenges related to working with massive datasets.
Distributed Computing
Distributed computing refers to the use of multiple computers to solve a problem or perform a task. It allows for parallel processing and can significantly improve overall performance and scalability. Measuring candidates' skills in distributed computing is essential as it indicates their ability to design and implement scalable and efficient solutions in a distributed environment.
Data Analysis
Data analysis involves the exploration, transformation, and modeling of data to extract valuable insights and support decision-making. Assessing candidates' skills in data analysis enables recruiters to identify individuals who can effectively analyze and interpret complex data sets, providing valuable insights to drive business outcomes.
Hadoop
Hadoop is an open-source framework that provides a distributed file system and supports the processing of big data using the MapReduce programming model. Evaluating candidates' Hadoop skills is crucial as it demonstrates their proficiency in utilizing this powerful tool for managing and processing large datasets.
Data Processing
Data processing refers to the manipulation and transformation of data to extract useful information or prepare it for further analysis. Assessing candidates' skills in data processing ensures that they can effectively manage and clean large datasets, enhancing their ability to work with big data effectively.
Parallel Computing
Parallel computing involves dividing a problem into smaller tasks that can be executed simultaneously on multiple processors or computers. It enables faster processing of complex computations and is particularly useful in big data processing. Measuring candidates' skills in parallel computing helps identify individuals capable of designing and implementing parallel algorithms for efficient data processing.
Data Aggregation
Data aggregation is the process of collecting and summarizing data from multiple sources into a single, easily manageable form. It plays a crucial role in big data processing as it allows for efficient storage and retrieval of relevant information. Evaluating candidates' skills in data aggregation ensures that they can effectively collect and consolidate data from different sources, supporting more advanced data analysis tasks.
Data Transformation
Data transformation involves converting data from one format or structure to another, often to prepare it for analysis or integration with other systems. It is an essential step in the data processing pipeline and requires knowledge of various techniques and tools. Measuring candidates' skills in data transformation helps recruiters identify individuals who can efficiently manipulate and reshape data to meet specific requirements.
Performance Optimization
Performance optimization involves enhancing the efficiency, speed, and scalability of software and systems. Evaluating candidates' skills in performance optimization is important as it indicates their ability to identify and resolve bottlenecks, improve computational efficiency, and optimize resource utilization. This skill is particularly relevant in the context of big data processing, where performance impacts the processing of massive datasets.
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 MapReduce Test to be based on.
MapReduce basics
Mapper function
Reducer function
Combiner function
Input and output formats
Secondary sort
Partitioning and shuffling
Counters in MapReduce
MapReduce job optimization
Join operations in MapReduce
Data serialization in MapReduce
Hadoop architecture
HDFS (Hadoop Distributed File System)
YARN (Yet Another Resource Negotiator)
Hadoop MapReduce framework
Hadoop streaming
Hadoop MapReduce job execution
Data processing in Hadoop
Data locality in Hadoop
Distributed storage and processing in Hadoop
Data analysis techniques
Exploratory data analysis
Data cleaning and preprocessing
Statistical analysis in MapReduce
Working with large data sets
Data transformation techniques
Data aggregation and summarization
Performance optimization in MapReduce
Fault tolerance in distributed computing
Parallel computing principles
Distributed computing frameworks
Cluster computing
Distributed resource management
Scalability in distributed computing
Data parallelism
Task parallelism
Concurrency control
Data integration and consolidation
Data warehouse design
Data quality assessment
ETL (Extract, Transform, Load)
Data visualization
Data warehousing
Performance tuning
Data streaming
Real-time data processing
Massively parallel processing
Cloud computing and Big Data
Data governance
Data security in distributed systems
Data integrity and consistency
Data privacy and compliance
Data backup and disaster recovery
Data replication

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What roles can I use the MapReduce Online Test for?

Big Data Developer
Hadopp Developer
Data Engineer

How is the MapReduce 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 data processing using MapReduce
Familiarity with parallel computing
Data aggregation and transformation skills
Performance optimization knowledge
Understanding of big data processing
In-depth knowledge of distributed computing principles
Ability to aggregate and transform complex data

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The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.

85%

reduction in screening time

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MapReduce Hiring Test FAQs

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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View sample scorecard

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MapReduce Online Test

Adaface MapReduce Test is the most accurate way to shortlist Big Data Developers

Tests for on-the-job skills

No trick questions

Non-googleable questions

1200+ customers in 75 countries

Designed for elimination, not selection

1 click candidate invites

Detailed scorecards & benchmarks

High completion rate

Advanced Proctoring

About the MapReduce Assessment Test

Why you should use Pre-employment MapReduce Online Test?

What topics are covered in the MapReduce Online Test?

MapReduce

Big Data Processing

Distributed Computing

Data Analysis

Hadoop

Data Processing

Parallel Computing

Data Aggregation

Data Transformation

Performance Optimization

Full list of covered topics

What roles can I use the MapReduce Online Test for?

How is the MapReduce Online Test customized for senior candidates?

MapReduce Hiring Test FAQs

Can I combine multiple skills into one custom assessment?

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

How do I interpret test scores?

What experience level can I use this test for?

Does every candidate get the same questions?

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

What is the cost of using this test?

Can I get a free trial?

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

Related Tests

Hadoop Online Test

MongoDB Test

Data Engineer Test

Hive Test

Big Data - Sqoop Test

40%