Pig Online Test

Q: What roles can I use the Pig Online Test for?

大数据开发人员 大数据开发人员 - 猪

About the test:

Pig Online测试使用基于方案的MCQ来评估候选人对Apache Pig的了解，包括他们编写猪拉丁文字，使用数据类型和内置功能并优化Pig查询的能力。该测试旨在评估候选人使用猪的能力并有效地执行数据分析任务。

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9 reasons why

Adaface Pig Test is the most accurate way to shortlist 大数据开发人员s

Reason #1

Tests for on-the-job skills

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:

能够编写Apache Pig查询以进行数据处理
了解Hadoop框架及其组件
MAPREDUCE编程模型的知识
了解猪拉丁脚本语言
能够优化和调整猪脚本以进行性能

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。关于此的实际问题大数据 - 猪测试将是不可行的.

🧐 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 大数据 - 猪测试 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 大数据 - 猪测试 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 Pig Assessment Test

Why you should use Pre-employment Pig Online Test?

The 大数据 - 猪测试 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:

Apache Pig及其生态系统的工作知识
对Hadoop及其组成部分的了解
精通MapReduce编程范式
能够编写猪拉丁脚本以进行数据处理的能力
了解猪拉丁功能及其用法
有调试和优化猪脚本的经验
熟悉Apache Pig UDF（用户定义的功能）
了解猪拉丁数据类型和模式
了解猪拉丁的内置功能和操作员
有效分析和处理大数据集的能力

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?

大数据分析
大数据分析是指检查和解释大型和解释的过程复杂的数据集可揭示隐藏的模式，趋势和见解。测量该测试中的大数据分析技能使招聘人员能够评估候选人在处理和分析大量数据方面的熟练程度，这在当今数据驱动的世界中至关重要。
hadoop
hadoop是一个开源框架，可以启用分布式存储和处理跨计算机群体的大型数据集。在此测试中测量Hadoop技能有助于招聘人员衡量候选人对这项流行技术的理解及其与分布式系统合作的能力。
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 大数据 - 猪测试 to be based on.
猪拉丁
在猪中加载和存储数据
过滤和转换猪的数据
加入和分组猪的数据
在猪中使用内置功能
与猪UDF一起工作
在猪中调试和故障排除
猪的性能优化
猪和哈杜普整合
Hadoop分布式文件系统（HDFS）
Hadoop MapReduce框架
Hadoop纱资源管理
Hadoop生态系统组件（Hive，HBase等）
Hadoop中的输入格式
Hadoop中的输出格式
映射器和还原类
Hadoop群集设置和配置
Hadoop作业提交和监视
Hadoop中的数据序列化
Hadoop批处理处理
hadoop流
MAPREDUCE分区和分类
MAPREDUCE中的组合和分区者
MAPREDUCE输入和输出格式
MAPREDUCE计数器
MAPREDUCE工作链接
MapReduce中的分布式缓存
Hadoop中的作业安排和任务安排
Hadoop安全和身份验证
猪和MapReduce整合

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What roles can I use the Pig Online Test for?

大数据开发人员
大数据开发人员 - 猪

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