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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

9 reasons why
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

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
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
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
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

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

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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

View sample scorecard
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.

test completion rate
Reason #9

Advanced Proctoring


Learn more

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

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
Singapore government logo

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

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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