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About the test:

数据挖掘测试对候选人的数据挖掘技术,数据预处理,关联规则挖掘,分类,聚类,聚类和数据可视化使用基于方案的MCQ进行评估。除了这些关键技能外,该测试还评估了候选人对数据仓库,数据清洁和大数据技术的理解。

Covered skills:

  • 数据处理
  • 数据预处理
  • 数据清洁
  • 数据挖掘过程
  • 数据仓库和OLAP技术
  • 采矿频繁的模式
  • 减少数据
  • 数据集成和转换

9 reasons why
9 reasons why

Adaface Data Mining Assessment Test is the most accurate way to shortlist 数据科学家s



Reason #1

Tests for on-the-job skills

The Data Mining 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:

  • 能够从大型数据集中提取有意义的见解
  • 熟练数据建模技术
  • 了解ETL(提取,转换,负载)过程
  • 数据处理和分析知识
  • 熟悉数据仓库和OLAP技术
  • 能够用于采矿目的的预处理数据
  • 在数据集中有频繁模式的经验
  • 清洁和减少数据噪音的能力
  • 了解数据挖掘过程
  • 数据整合和转换的能力
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

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 数据挖掘测试 将是不可行的.

🧐 Question

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity
Solve
You are designing a data model for a healthcare system with the following requirements:
 image
A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients.
B: A separate table for each entity with foreign keys as specified, without additional tables.
C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription.
D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients.
E: A single table combining Patient, Doctor, Appointment, and Prescription into one.
F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.

Hard

ER Diagram and minimum tables
ER Diagram
Solve
Look at the given ER diagram. What do you think is the least number of tables we would need to represent M, N, P, R1 and R2?
 image
 image
 image

Medium

Normalization Process
Normalization
Database Design
Anomaly Elimination
Solve
Consider a healthcare database with a table named PatientRecords that stores patient visit information. The table has the following attributes:

- VisitID
- PatientID
- PatientName
- DoctorID
- DoctorName
- VisitDate
- Diagnosis
- Treatment
- TreatmentCost

In this table:

- Each VisitID uniquely identifies a patient's visit and is associated with one PatientID.
- PatientID is associated with exactly one PatientName.
- Each DoctorID is associated with a unique DoctorName.
- TreatmentCost is a fixed cost based on the Treatment.

Evaluating the PatientRecords table, which of the following statements most accurately describes its normalization state and the required actions for higher normalization?
A: The table is in 1NF. To achieve 2NF, remove partial dependencies by separating Patient information (PatientID, PatientName) and Doctor information (DoctorID, DoctorName) into different tables.
B: The table is in 2NF. To achieve 3NF, remove transitive dependencies by creating separate tables for Patients (PatientID, PatientName), Doctors (DoctorID, DoctorName), and Visits (VisitID, PatientID, DoctorID, VisitDate, Diagnosis, Treatment, TreatmentCost).
C: The table is in 3NF. To achieve BCNF, adjust for functional dependencies such as moving DoctorName to a separate Doctors table.
D: The table is in 1NF. To achieve 3NF, create separate tables for Patients, Doctors, and Visits, and remove TreatmentCost as it is a derived attribute.
E: The table is in 2NF. To achieve 4NF, address any multi-valued dependencies by separating Visit details and Treatment details.
F: The table is in 3NF. To achieve 4NF, remove multi-valued dependencies related to VisitID.

Medium

University Courses
ER Diagrams
Complex Relationships
Integrity Constraints
Solve
 image
Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details?
A: A Student can major in multiple Departments.
B: An Instructor can belong to multiple Departments.
C: A Course can be offered by multiple Departments.
D: Enrollment records can link a Student to multiple Courses in a single semester.
E: Each Course must be associated with an Enrollment record.
F: A Department can offer courses without having any instructors.

Medium

Data Merging
Data Merging
Conditional Logic
Solve
A data engineer is tasked with merging and transforming data from two sources for a business analytics report. Source 1 is a SQL database 'Employee' with fields EmployeeID (int), Name (varchar), DepartmentID (int), and JoinDate (date). Source 2 is a CSV file 'Department' with fields DepartmentID (int), DepartmentName (varchar), and Budget (float). The objective is to create a summary table that lists EmployeeID, Name, DepartmentName, and YearsInCompany. The YearsInCompany should be calculated based on the JoinDate and the current date, rounded down to the nearest whole number. Consider the following initial SQL query:
 image
Which of the following modifications ensures accurate data transformation as per the requirements?
A: Change FLOOR to CEILING in the calculation of YearsInCompany.
B: Add WHERE e.JoinDate IS NOT NULL before the JOIN clause.
C: Replace JOIN with LEFT JOIN and use COALESCE(d.DepartmentName, 'Unknown').
D: Change the YearsInCompany calculation to YEAR(CURRENT_DATE) - YEAR(e.JoinDate).
E: Use DATEDIFF(YEAR, e.JoinDate, CURRENT_DATE) for YearsInCompany calculation.

Medium

Data Updates
Staging
Data Warehouse
Solve
Jaylo is hired as Data warehouse engineer at Affflex Inc. Jaylo is tasked with designing an ETL process for loading data from SQL server database into a large fact table. Here are the specifications of the system:
1. Orders data from SQL to be stored in fact table in the warehouse each day with prior day’s order data
2. Loading new data must take as less time as possible
3. Remove data that is more then 2 years old
4. Ensure the data loads correctly
5. Minimize record locking and impact on transaction log
Which of the following should be part of Jaylo’s ETL design?

A: Partition the destination fact table by date
B: Partition the destination fact table by customer
C: Insert new data directly into fact table
D: Delete old data directly from fact table
E: Use partition switching and staging table to load new data
F: Use partition switching and staging table to remove old data

Medium

SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions
Solve
In an ETL process designed for a retail company, a complex SQL transformation is applied to the 'Sales' table. The 'Sales' table has fields SaleID, ProductID, Quantity, SaleDate, and Price. The goal is to generate a report that shows the total sales amount and average sale amount per product, aggregated monthly. The following SQL code snippet is used in the transformation step:
 image
What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal?
A: The code calculates the total and average sales amount for each product annually.
B: It aggregates sales data by month and product, computing total and average sales amounts.
C: This query generates a daily breakdown of sales, both total and average, for each product.
D: The code is designed to identify the best-selling products on a monthly basis by sales amount.
E: It calculates the overall sales and average price per product, without considering the time dimension.

Medium

Trade Index
Index
Solve
Silverman Sachs is a trading firm and deals with daily trade data for various stocks. They have the following fact table in their data warehouse:
Table: Trades
Indexes: None
Columns: TradeID, TradeDate, Open, Close, High, Low, Volume
Here are three common queries that are run on the data:
 image
Dhavid Polomon is hired as an ETL Developer and is tasked with implementing an indexing strategy for the Trades fact table. Here are the specifications of the indexing strategy:

- All three common queries must use a columnstore index
- Minimize number of indexes
- Minimize size of indexes
Which of the following strategies should Dhavid pick:
A: Create three columnstore indexes: 
1. Containing TradeDate and Close
2. Containing TradeDate, High and Low
3. Container TradeDate and Volume
B: Create two columnstore indexes:
1. Containing TradeID, TradeDate, Volume and Close
2. Containing TradeID, TradeDate, High and Low
C: Create one columnstore index that contains TradeDate, Close, High, Low and Volume
D: Create one columnstore index that contains TradeID, Close, High, Low, Volume and Trade Date
🧐 Question🔧 Skill

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity

2 mins

Data Modeling
Solve

Hard

ER Diagram and minimum tables
ER Diagram

2 mins

Data Modeling
Solve

Medium

Normalization Process
Normalization
Database Design
Anomaly Elimination

3 mins

Data Modeling
Solve

Medium

University Courses
ER Diagrams
Complex Relationships
Integrity Constraints

2 mins

Data Modeling
Solve

Medium

Data Merging
Data Merging
Conditional Logic

2 mins

ETL
Solve

Medium

Data Updates
Staging
Data Warehouse

2 mins

ETL
Solve

Medium

SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions

3 mins

ETL
Solve

Medium

Trade Index
Index

3 mins

ETL
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Healthcare System
Data Integrity
Normalization
Referential Integrity
Data Modeling
Easy2 mins
Solve
ER Diagram and minimum tables
ER Diagram
Data Modeling
Hard2 mins
Solve
Normalization Process
Normalization
Database Design
Anomaly Elimination
Data Modeling
Medium3 mins
Solve
University Courses
ER Diagrams
Complex Relationships
Integrity Constraints
Data Modeling
Medium2 mins
Solve
Data Merging
Data Merging
Conditional Logic
ETL
Medium2 mins
Solve
Data Updates
Staging
Data Warehouse
ETL
Medium2 mins
Solve
SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions
ETL
Medium3 mins
Solve
Trade Index
Index
ETL
Medium3 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

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

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment 数据挖掘测试 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 数据挖掘测试 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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

查看样本记分卡
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 Data Mining Online Test

Why you should use Pre-employment Data Mining Test?

The 数据挖掘测试 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:

  • 数据处理和操纵技术
  • 数据仓库和OLAP技术知识
  • 了解数据挖掘的基础和概念
  • 数据预处理技术和方法
  • 能够在大数据集中开采频繁模式的能力
  • 清洁和处理脏数据
  • 降低有效采矿的数据
  • 了解并遵循数据挖掘过程
  • 数据集成和转型技能
  • 解释和分析采矿结果的能力

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 Data Mining Test?

  • 采矿频繁的模式

    挖掘频繁的模式集中于发现重新培训数据集中的项目集或序列。它涉及市场篮分析和关联规则挖掘等技术。该技能应在测试中衡量,以评估候选人在识别共同模式方面的熟练程度,这对于各种应用程序(例如建议系统和市场分析)都很有价值。

  • 数据清洁

    数据清洁是数据集中识别和纠正或删除错误,不一致和异常值的过程。它包括处理重复记录,解决不一致之处以及处理嘈杂或无关紧要的数据等任务。测量测试中的这一技能有助于评估候选人确保数据完整性和可靠性的能力,这对于准确的采矿结果至关重要。

  • 数据减少

    数据降低涉及降低尺寸的技术数据集的维度,而不会显着丢失相关信息。它旨在删除冗余或无关的功能,并将数据转换为更紧凑的表示。测量测试中的这一技能有助于评估候选人通过降低计算复杂性和提高效率来优化数据挖掘过程的能力。

  • 数据挖掘过程

    数据挖掘过程包括涉及的系统步骤从数据中提取有意义的模式和见解。它包括数据探索,模型选择,模式评估和结果解释等任务。测量测试中的这一技能有助于评估候选人对整体数据挖掘工作流程的理解及其在每个阶段应用适当技术的能力。

  • 数据集成和转换

    数据集成和转换涉及来自各种来源的数据,解决数据冲突,并将数据转换为统一格式进行分析。它需要了解数据集成技术,数据映射和数据转换操作。测量测试中的这一技能有助于评估候选人有效整合和转换不同数据源的能力,确保在采矿过程中的一致性和准确性。

  • 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 数据挖掘测试 to be based on.

    数据处理
    数据仓库
    OLAP技术
    数据预处理
    采矿频繁的模式
    数据清洁
    减少数据
    数据挖掘过程
    数据集成
    数据转换
    数据提取
    数据加载
    数据建模
    数据分析
    监督学习
    无监督的学习
    协会规则
    决策树
    聚类
    分类
    数据可视化
    数据探索
    大数据
    预测建模
    模式识别
    文字采矿
    网络挖掘
    社交网络分析
    功能选择
    减少维度
    异常值检测
    数据插补
    天真的贝叶斯
    支持向量机
    神经网络
    遗传算法
    回归分析
    时间序列分析
    空间数据挖掘
    数据隐私
    数据挖掘中的道德规范
    市场篮分析
    协会规则挖掘
    顺序模式挖掘
    异常检测
    模型评估
    过度拟合
    集合方法
    交叉验证
    数据采样
    数据融合
    并行和分布式数据挖掘
    数据可扩展性
    数据质量评估
    数据分析
    功能工程
    数据争吵

What roles can I use the Data Mining Test for?

  • 数据科学家
  • 业务分析师
  • 数据分析师
  • 数据工程师
  • 数据库管理员
  • 研究科学家

How is the Data Mining 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

  • 精通统计分析
  • 实施各种数据挖掘算法的能力
  • 了解受监督和无监督的学习技术
  • 有决策树算法的经验
  • 了解关联规则挖掘
  • 聚类技术的专业知识
  • 分类和回归模型的经验
  • 熟练处理大型数据集
  • 熟悉大数据技术
  • 数据可视化和报告方面的专业知识
Singapore government logo

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

Data Mining Hiring Test 常见问题解答

我可以将多个技能结合在一起,为一个自定义评估吗?

是的,一点没错。自定义评估是根据您的职位描述进行的,并将包括有关您指定的所有必备技能的问题。

您是否有任何反交换或策略功能?

我们具有以下反交易功能:

  • 不可解决的问题
  • IP策略
  • Web Protoring
  • 网络摄像头Proctoring
  • 窃检测
  • 安全浏览器

阅读有关[Proctoring功能](https://www.adaface.com/proctoring)的更多信息。

如何解释考试成绩?

要记住的主要问题是评估是消除工具,而不是选择工具。优化了技能评估,以帮助您消除在技术上没有资格担任该角色的候选人,它没有进行优化以帮助您找到该角色的最佳候选人。因此,使用评估的理想方法是确定阈值分数(通常为55%,我们为您提供基准测试),并邀请所有在下一轮面试中得分高于门槛的候选人。

我可以使用该测试的经验水平?

每个ADAFACE评估都是为您的职位描述/理想候选角色定制的(我们的主题专家将从我们的10000多个问题的图书馆中选择正确的问题)。可以为任何经验级别定制此评估。

每个候选人都会得到同样的问题吗?

是的,这使您比较候选人变得容易得多。 MCQ问题的选项和问题顺序是随机的。我们有[抗欺骗/策略](https://www.adaface.com/proctoring)功能。在我们的企业计划中,我们还可以选择使用类似难度级别的问题创建多个版本的相同评估。

我是候选人。我可以尝试练习测试吗?

不,不幸的是,我们目前不支持实践测试。但是,您可以使用我们的[示例问题](https://www.adaface.com/questions)进行练习。

使用此测试的成本是多少?

您可以查看我们的[定价计划](https://www.adaface.com/pricing/)。

我可以免费试用吗?

我刚刚搬到了一个付费计划。我如何要求自定义评估?

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