Hadoop在线测试

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

安装和配置Hadoop群集 核心Hadoop架构（HDFS; Yarn; MapReduce） 编写有效的蜂巢和猪查询 将数据发布到集群 处理流数据 使用不同的文件格式 故障排除和监视

About the test:

Hadoop在线测试使用基于方案的MCQ问题来评估候选人在云上安装Hadoop簇的能力，在Hadoop簇上运行优化的MapReduce作业，并编写有效的Pig指令和Hive查询，以对复杂数据集进行数据分析。招聘经理在Hadoop开发人员和管理员中寻找的核心Hadoop框架知识的测试屏幕。

Covered skills:

安装和配置Hadoop群集
编写有效的蜂巢和猪查询
处理流数据
故障排除和监视

核心Hadoop架构（HDFS; Yarn; MapReduce）
将数据发布到集群
使用不同的文件格式

Try practice test

Start 14-day free trial

9 reasons why

Adaface Hadoop在线测试 is the most accurate way to shortlist Hadoop管理员s

Reason #1

Tests for on-the-job skills

The Hadoop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:

能够安装和配置Hadoop群集
了解核心Hadoop架构（HDFS，纱线，MapReduce）
熟练写作有效的蜂巢和猪查询
能够将数据发布到群集
能够处理流数据
了解使用不同文件格式的知识
故障排除和监视的技巧

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

Try practice test

Start 14-day free trial

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 Hadoop在线测试将是不可行的.

🧐 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

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

Try practice test

Start 14-day free trial

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Hadoop在线测试 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 Hadoop在线测试 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

查看样本记分卡

Try practice test

Start 14-day free trial

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

Preview this test

查看样本记分卡

About the Hadoop在线测试

Why you should use Hadoop在线测试?

The Hadoop在线测试 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:

安装和配置Hadoop群集
核心Hadoop架构，包括HDF，纱线和MapReduce
编写有效的蜂巢和猪查询
将数据发布到集群
处理流数据
使用不同的文件格式
故障排除和监视
优化Hadoop群集性能
在Hadoop中实施数据安全
设计和实施Hadoop数据模型

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 Hadoop在线测试?

安装和配置Hadoop簇
此技能涉及设置和配置多个机器以作为分布式群集一起工作，每个节点上安装了Hadoop软件。它需要了解系统管理，网络和理解Hadoop生态系统。
核心Hadoop体系结构（HDFS; Yarn; MapReduce）
此技能涵盖了Hadoop的基本组成部分，包括Hadoop分布式文件系统（HDFS）提供可靠的存储，用于资源管理和作业计划的纱线以及并行处理大型数据集的MAPREDUCE。了解这种体系结构对于设计和优化Hadoop应用程序至关重要。
撰写有效的Hive和Pig查询
此技能涉及在Hive和Pig中编写查询，这是用于用于的高级语言处理和分析Hadoop中的数据。写作有效的查询对于实现最佳性能和从大型数据集中提取有价值的见解很重要。
将数据发布到簇
此技能需要能够从外部来源导入数据源的数据，确保正确格式化数据并优化用于存储。它还涉及管理数据复制，压缩和安全性，以确保集群中的数据完整性和可用性。
处理流数据
此技能涉及处理和分析实时流媒体数据hadoop。它需要了解Apache Kafka或Apache Storm等技术的知识才能生成并处理数据，从而实现实时分析和决策。
使用不同的文件格式
该技能涉及使用Hadoop中的各种文件格式，例如CSV，JSON，AVRO或PARQUET。它需要了解文件格式及其针对不同用例的特定优点和权衡，以及以不同格式处理和转换数据的工具知识。
故障排除和监视
该技能是在Hadoop群集中识别和解决问题，例如性能瓶颈，数据不一致或配置问题。它涉及使用各种监视工具和诊断技术来确保Hadoop环境的平稳操作。
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 Hadoop在线测试 to be based on.
安装Hadoop
配置Hadoop簇
Hadoop分布式文件系统（HDFS）
纱线资源管理
MAPREDUCE编程模型
蜂巢查询语言
猪脚本
将数据发布到Hadoop群集
在Hadoop中处理流数据
使用AVRO文件格式
使用镶木quet文件格式
使用ORC文件格式
在Hadoop中处理JSON数据
如何解决Hadoop绩效问题
监视Hadoop群集
确保Hadoop群集
Hadoop中的数据摄入技术
Hadoop中的数据处理工作流程
优化Hadoop工作
了解Hadoop生态系统组件
使用Hadoop命令行工具
分析Hadoop日志
管理Hadoop群集
Hadoop的备份和恢复策略
使用Apache Spark
Hadoop群集调整
在Hadoop中处理数据偏差
使用Hadoop连接器
Hadoop数据压缩技术
Hadoop中的数据序列化
Hadoop群集部署模型
Hadoop的设计模式
在Hadoop中重新分配数据
Hadoop高可用性
Hadoop的工作时间安排
Hadoop中的数据划分
了解Hadoop分布式缓存
Hadoop数据局部性
使用Hadoop流
Hadoop中的文件输入/输出
了解Hadoop配置文件
纱线申请生命周期
hadoop输入/输出格式
hadoop中的投机执行
了解Hadoop资源管理
Hadoop安全机制
管理Hadoop用户和组
Hadoop集群尺寸和能力计划
Hadoop数据加密
HDFS数据复制
与Hadoop图书馆合作
使用Hadoop生态系统工具

Try practice test

Start 14-day free trial

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

Hadoop管理员
Hadoop工程师
Hadoop测试仪
Hadoop应用程序开发人员
大数据开发人员-HADOOP
高级大数据工程师-Hadoop
高级Hadoop管理员

How is the Hadoop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

使用Hadoop生态系统工具（例如Spark，HBase和Impala）的经验
管理和调整Hadoop群集
微调和优化MapReduce工作
有Hadoop数据摄入和处理工具的经验
实施Hadoop群集的高可用性和灾难恢复
编写和优化大型数据集的Apache Hive查询
具有数据仓库概念和技术的经验
在Hadoop中设计和实施数据管道
在Hadoop中使用分布式计算框架
Hadoop中实时数据处理的经验

Preview this test

查看样本记分卡

招聘经理认为，通过小组面试中提出的技术问题，他们能够判断哪些候选人得分更高，并与得分较差的候选人区分开来。他们是非常满意通过 Adaface 筛选入围的候选人的质量。

85%

减少筛查时间

查看完整的案例研究

Hadoop在线测试常见问题解答

我可以在同一个测试中评估 Hadoop 和 Spark 吗？

是的。您可以在同一次测试中评估多种技能。如果您的 Hadoop 职位描述要求候选人具备 Hadoop 和 Apache Spark 的经验，我们会包含有关这两种技能的问题。您可以查看我们的标准Spark在线测试来了解将使用什么样的问题来评估Spark知识。

我可以在同一测试中评估其他大数据技术，例如 Hive、Pig、SQL 吗？

是的。不同公司的大数据职位要求候选人拥有不同技术的经验。我们支持根据您的职位描述定制评估。这包括在同一测试中包含评估多种技能的问题。流行的 Hadoop 职位要求候选人具备 Hive、Pig、SQL、Spark 等相关技术的经验。如果您需要，我们可以在同一测试中包含有关这些主题的问题，以便您可以为您的公司找到合适的候选人。您可以查看我们的标准SQL在线测试和[Spark在线测试](https://www.adaface.com/assessment- test/spark-online-test）以了解如何评估这些技能。