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MapReduce Online 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.

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Screen candidates with a 40 mins test

Test duration:  ~ 40 mins
Difficulty level:  Moderate
Availability:  Available as custom test
Questions:
  • 8 MapReduce MCQs
  • 7 Hadoop MCQs
Covered skills:
MapReduce
Big Data Processing
Distributed Computing
Data Analysis
Hadoop
Data Processing
Parallel Computing
Data Aggregation
Data Transformation
Performance Optimization
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Use the MapReduce Test to shortlist qualified candidates

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
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Screen candidates with the highest quality 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 MapReduce Test will be non-googleable.

🧐 Question

Easy

Count number of occurrences
Mappers
Reducers
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
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:
 image
What is the output of the following Pig command?

Q = GROUP P BY p2;
DUMP Q;
 image
🧐 Question🔧 Skill

Easy

Count number of occurrences
Mappers
Reducers

3 mins

Hadoop
Solve

Medium

Hive ngrams

2 mins

Hadoop
Solve

Easy

P Q relations
Pig

2 mins

Hadoop
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Count number of occurrences
Mappers
Reducers
Hadoop
Easy3 mins
Solve
Hive ngrams
Hadoop
Medium2 mins
Solve
P Q relations
Pig
Hadoop
Easy2 mins
Solve

Test candidates on core MapReduce Hiring Test topics

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.

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Have questions about the MapReduce Hiring Test?

How does pricing work?

You can check out our pricing plans.

Can I customize the test?

Yes, absolutely. Custom assessments are set up within 48 hours 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. You can also customize a test by uploading your own questions.

Can I combine multiple skills into one test?

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.

What roles can I use the MapReduce Test for?

Here are few roles for which we recommend this test:

  • Big Data Developer
  • Hadopp Developer
  • Data Engineer
Can I see a sample test, or do you have a free trial?

Yes!

The free trial includes one sample technical test (Java/ JavaScript) and one sample aptitude test that you will find in your dashboard when you sign up. You can use it to review the quality of questions and the candidate experience of giving a test on Adaface.

You can preview any of the 500+ tests and see the sample questions to decide if it would be a good fit for your requirements.

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.

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.

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