MAPREDUCE在线测试

Q: What topics are covered in the MAPREDUCE在线测试?

MapReduce 大数据处理 分布式计算 数据分析 hadoop 数据处理 并行计算 数据聚合 数据转换 性能优化

About the test:

MAPREDUCE在线测试使用基于方案的MCQ来评估候选人对MapReduce框架的了解，包括他们能够与Hadoop，HDFS和Yarn合作。该测试还评估了候选人对猪和Hive的熟悉，以进行数据分析及其使用大数据技术的能力。该测试旨在评估候选人使用MapReduce框架和相关技术设计和开发应用程序的能力。

Covered skills:

MapReduce
分布式计算
hadoop
并行计算
数据转换

大数据处理
数据分析
数据处理
数据聚合
性能优化

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9 reasons why

Adaface MAPREDUCE在线测试 is the most accurate way to shortlist 大数据开发人员s

Reason #1

Tests for on-the-job skills

The MAPREDUCE在线测试 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:

能够编写有效的MapReduce程序
了解大数据处理原理
分布式计算概念的知识
熟练数据分析技术
有Hadoop框架的经验
处理大量数据的能力
对平行计算原理的理解
数据聚合和摘要的技能
熟练数据转换和操纵
绩效优化技术的知识

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

这些只是我们库中有10,000多个问题的一个小样本。关于此的实际问题 MapReduce测试将是不可行的.

🧐 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: 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	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	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	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	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;

Reason #4

1200+ customers in 75 countries

借助 Adaface，我们能够将初步筛选流程优化高达 75% 以上，为招聘经理和我们的人才招聘团队节省了宝贵的时间！

Brandon Lee, 人事主管, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment MapReduce测试 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测试 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

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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

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About the MAPREDUCE在线测试

Why you should use MAPREDUCE在线测试?

The MapReduce测试 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:

能够实现大数据处理的MAPReduce算法
能够熟练Hadoop生态系统及其组成部分
理解分布式计算原理
使用MapReduce技术分析数据的能力
了解Hadoop的架构及其在大数据处理中的作用
使用MapReduce框架进行数据处理方面的专业知识
精通并行计算以有效的数据处理
能够使用MapReduce汇总和转换数据
在MapReduce工作的绩效优化方面经验

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在线测试?

mapReduce
MapReduce是一种编程模型和软件框架，用于在分布式计算环境中处理和生成大型数据集。它允许并行执行跨计算机群的数据处理任务，从而适合大数据处理。在此测试中评估MAPREDUCE技能将有助于招聘人员评估候选人在大数据处理中有效利用这一重要技术的能力。
大数据处理
大数据处理涉及管理和分析来自各种来源的大量复杂数据。它需要技术和工具（例如MapReduce）来有效地处理并从数据中提取有意义的见解。评估候选人在大数据处理中的技能将有助于招聘人员确定可以应对与大型数据集有关的挑战的个人。
分布式计算
分布式计算是指使用多台计算机解决问题或执行任务。它允许并行处理，并可以显着提高整体性能和可扩展性。测量候选者在分布式计算方面的技能至关重要，因为它表明他们在分布式环境中设计和实施可扩展和高效的解决方案的能力。
数据分析
数据分析涉及探索，转换，，数据建模以提取有价值的见解并支持决策。评估候选人在数据分析方面的技能使招聘人员能够确定可以有效分析和解释复杂数据集的个人，从而提供有价值的见解以推动业务成果。
hadoop
hadoop是一个开放提供分布式文件系统并使用MapReduce编程模型来处理大数据的源框架。评估候选人的Hadoop技能至关重要，因为它证明了他们在利用此功能强大的工具来管理和处理大型数据集方面的熟练程度。
数据处理
数据处理是指数据处理和转换数据提取有用的信息或准备进一步分析。评估候选人在数据处理方面的技能可确保他们可以有效地管理和清洁大型数据集，从而增强其有效使用大数据的能力。
并行计算
并行计算涉及分开问题作为可以在多个处理器或计算机上同时执行的较小任务。它可以更快地处理复杂的计算，并且在大数据处理中特别有用。测量候选人在并行计算中的技能有助于确定能够设计和实施并行算法以进行有效数据处理的个人。
数据聚合
数据聚合是从多个收集和总结数据的过程来源分为单个易于管理的形式。它在大数据处理中起着至关重要的作用，因为它可以有效地存储和检索相关信息。评估候选人在数据聚合方面的技能可确保他们可以有效地收集和巩固来自不同来源的数据，支持更高级的数据分析任务。
数据转换
数据转换涉及从一个转换数据格式或结构为另一种，通常是为了与其他系统进行分析或集成。这是数据处理管道中的重要步骤，需要了解各种技术和工具。衡量候选人在数据转换方面的技能有助于招聘人员确定可以有效操纵和重塑数据以满足特定要求的个人。
绩效优化
性能优化涉及提高效率，速度和可伸缩性软件和系统。评估候选人在绩效优化方面的技能非常重要，因为它表明他们能够识别和解决瓶颈，提高计算效率并优化资源利用率。在大数据处理的背景下，这种技能尤其重要，在大数据处理中，性能会影响大量数据集的处理。
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测试 to be based on.
MAPREDUCE基础知识
映射器功能
还原功能
组合函数
输入和输出格式
次要
分区和改组
MAPREDUCE中的计数器
MAPREDUCE作业优化
加入MapReduce的操作
MAPREDUCE中的数据序列化
Hadoop架构
HDFS（Hadoop分布式文件系统）
纱线（另一个资源谈判者）
Hadoop MapReduce框架
hadoop流
Hadoop MapReduce工作执行
Hadoop中的数据处理
Hadoop中的数据局部性
Hadoop中的分布式存储和处理
数据分析技术
探索性数据分析
数据清洁和预处理
MAPREDUCE中的统计分析
使用大型数据集
数据转换技术
数据聚合和汇总
MapReduce的性能优化
分布式计算中的容错
并行计算原理
分布式计算框架
集群计算
分布式资源管理
分布式计算中的可伸缩性
数据并行性
任务并行性
并发控制
数据集成和合并
数据仓库设计
数据质量评估
ETL（提取，转换，负载）
数据可视化
数据仓库
性能调整
数据流
实时数据处理
大规模并行处理
云计算和大数据
数据治理
分布式系统中的数据安全
数据完整性和一致性
数据隐私和合规性
数据备份和灾难恢复
数据复制

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What roles can I use the MAPREDUCE在线测试 for?

大数据开发人员
hadopp开发人员
数据工程师

How is the MAPREDUCE在线测试 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