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About the test:

Teradata在线测试使用基于方案的MCQ来评估候选者对Teradata数据库,SQL查询,性能调整,数据建模,数据库体系结构和数据仓库概念的了解。此外,该测试还评估了候选人在Teradata实用程序,Teradata平行转运蛋白(TPT)和Teradata QueryGrid方面的熟练程度。该测试旨在评估候选人设计,开发和维护Teradata数据库的能力。

Covered skills:

  • Teradata
  • 数据库管理
  • 数据建模
  • 数据集成
  • Teradata实用程序
  • SQL
  • etl
  • 数据分析
  • 数据迁移

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9 reasons why
9 reasons why

Adaface Teradata在线测试 is the most accurate way to shortlist Teradata数据库管理员s



Reason #1

Tests for on-the-job skills

The Teradata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:

  • 设计和优化Teradata数据库的能力
  • 精通SQL和数据操纵语言(DML)
  • ETL过程和工具的知识
  • 具有数据建模和设计的经验
  • 分析和解释数据的能力
  • 了解Teradata实用程序和性能调整
  • 了解数据集成和迁移
  • Teradata数据库管理方面的强大技能
  • 精通Teradata SQL语法和功能
  • 能够使用Teradata执行数据分析
  • 数据仓库概念的知识
  • 熟悉Teradata的数据治理框架
  • 能够编写有效的SQL查询的能力
  • 了解数据安全和隐私最佳实践
  • Teradata系统管理经验
  • 能够故障排除和解决Teradata数据库问题
  • Teradata并行处理体系结构的知识
  • 了解Teradata数据仓库设备
  • 有Teradata备份和恢复程序的经验
  • 能够与Teradata Teradata多加载和快速载荷合作
  • Teradata Teradata QueryGrid和Queryband的知识
  • 了解Teradata的数据字典和元数据管理
  • Teradata Teradata观点和数据移动的经验
  • 能够创建和管理Teradata数据库用户和角色
  • Teradata Teradata索引向导和Teradata SQL助手的知识
  • 了解Teradata的工作负载管理和查询优化
  • 具有Teradata Teradata Tpump和TPT(Teradata平行转运蛋白)的经验
  • 能够使用Teradata的逻辑和物理数据模型执行数据建模
  • Teradata Teradata Presto的知识
  • 了解Teradata的压缩技术和数据存储选项
  • Teradata Teradata BTEQ和FastExport的经验
  • 能够使用Teradata Teradatad守护程序和公用事业
  • Teradata Teradata地理空间数据类型和功能的知识
  • 了解Teradata的数据谱系和影响分析
  • Teradata Teradata Unity与团结总监的经验
  • 能够调整和优化Teradata SQL查询和数据库性能的能力
  • Teradata Teradata SQL参考和Teradata数据库管理的知识
  • 了解Teradata的数据归档和数据保留
  • Teradata Teradata ODBC和JDBC司机的经验
  • 能够故障排除和识别Teradata数据加载和数据移动问题的能力
  • Teradata Teradata统计向导和Teradata数据库设计的知识
  • 了解Teradata的数据复制和数据同步
  • 具有Teradata Teradata Lob数据类型和功能的经验
  • 能够设计和实施Teradata数据集市和数据仓库
  • Teradata Teradata Studio和Teradata数据库查询调度程序的知识
  • 了解Teradata的数据掩盖和数据匿名化
  • 具有Teradata Teradata安全区域以及Teradata工具和公用事业的经验
  • 能够执行Teradata数据库容量计划和性能调整的能力
  • Teradata Teradata SQL Formatter和Teradata QueryGrid Manager的知识
  • 了解Teradata的数据访问控制和授权
  • Teradata Teradata Data Mover和Teradata ET和LT的经验
  • 能够执行Teradata数据库备份和还原过程
  • Teradata Teradata系统仿真工具和Teradata审核的知识
  • 了解Teradata的数据质量和数据治理计划
  • Teradata Teradata并行数据泵和Teradata多系统经理的经验
  • 设计和实施Teradata数据集成解决方案的能力
  • Teradata Teradata元数据和Teradata数据实验室的知识
  • 了解Teradata的能力计划和系统性能监控
  • Teradata Teradata JDBC提供商和Teradata ODBC司机的经验
  • 能够对Teradata数据复制和同步问题进行故障排除和解决问题
  • Teradata Teradata数据词典和Ter​​adata数据保护的知识
  • 了解Teradata的数据虚拟化和分析功能
  • Teradata Teradata Appcenter和Teradata XML服务的经验
  • 能够执行Teradata数据库升级和修补程序
  • Teradata Teradata数据库管理和Teradata数据库管理的知识
  • 了解Teradata的查询重写和查询优化技术
  • Teradata Teradata主动系统管理和Teradata远程控制台的经验
  • 能够设计和实施Teradata数据安全和隐私机制
  • Teradata Teradata生态系统经理和Teradata Unity Data Mover的知识
  • 了解Teradata的数据加密和密钥管理
  • Teradata Teradata安全企业搜索和Teradata投资组合的经验
  • 配置和管理Teradata数据仓库体系结构的能力
  • Teradata Teradata数据隐私和Teradata数据流实用程序的知识
  • 了解Teradata的数据压缩和数据归档策略
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
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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 Teradata在线测试 将是不可行的.

🧐 Question

Medium

Multi Select
JOIN
GROUP BY
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Consider the following SQL table:
 image
How many rows does the following SQL query return?
 image

Medium

nth highest sales
Nested queries
User Defined Functions
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Consider the following SQL table:
 image
Which of the following SQL commands will find the ‘nth highest Sales’ if it exists (returns null otherwise)?
 image

Medium

Select & IN
Nested queries
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Consider the following SQL table:
 image
Which of the following SQL queries would return the year when neither a football or cricket winner was chosen?
 image

Medium

Sorting Ubers
Nested queries
Join
Comparison operators
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Consider the following SQL table:
 image
What will be the first two tuples resulting from the following SQL command?
 image

Hard

With, AVG & SUM
MAX() MIN()
Aggregate functions
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Consider the following SQL table:
 image
How many tuples does the following query return?
 image

Medium

Data Merging
Data Merging
Conditional Logic
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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
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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
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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
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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:
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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

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity
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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
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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
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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
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 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.
🧐 Question🔧 Skill

Medium

Multi Select
JOIN
GROUP BY

2 mins

SQL
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Medium

nth highest sales
Nested queries
User Defined Functions

3 mins

SQL
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Medium

Select & IN
Nested queries

3 mins

SQL
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Medium

Sorting Ubers
Nested queries
Join
Comparison operators

3 mins

SQL
Try practice test

Hard

With, AVG & SUM
MAX() MIN()
Aggregate functions

2 mins

SQL
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Medium

Data Merging
Data Merging
Conditional Logic

2 mins

ETL
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Medium

Data Updates
Staging
Data Warehouse

2 mins

ETL
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Medium

SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions

3 mins

ETL
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Medium

Trade Index
Index

3 mins

ETL
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Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity

2 mins

Data Modeling
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Hard

ER Diagram and minimum tables
ER Diagram

2 mins

Data Modeling
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Medium

Normalization Process
Normalization
Database Design
Anomaly Elimination

3 mins

Data Modeling
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Medium

University Courses
ER Diagrams
Complex Relationships
Integrity Constraints

2 mins

Data Modeling
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🧐 Question🔧 Skill💪 Difficulty⌛ Time
Multi Select
JOIN
GROUP BY
SQL
Medium2 mins
Try practice test
nth highest sales
Nested queries
User Defined Functions
SQL
Medium3 mins
Try practice test
Select & IN
Nested queries
SQL
Medium3 mins
Try practice test
Sorting Ubers
Nested queries
Join
Comparison operators
SQL
Medium3 mins
Try practice test
With, AVG & SUM
MAX() MIN()
Aggregate functions
SQL
Hard2 mins
Try practice test
Data Merging
Data Merging
Conditional Logic
ETL
Medium2 mins
Try practice test
Data Updates
Staging
Data Warehouse
ETL
Medium2 mins
Try practice test
SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions
ETL
Medium3 mins
Try practice test
Trade Index
Index
ETL
Medium3 mins
Try practice test
Healthcare System
Data Integrity
Normalization
Referential Integrity
Data Modeling
Easy2 mins
Try practice test
ER Diagram and minimum tables
ER Diagram
Data Modeling
Hard2 mins
Try practice test
Normalization Process
Normalization
Database Design
Anomaly Elimination
Data Modeling
Medium3 mins
Try practice test
University Courses
ER Diagrams
Complex Relationships
Integrity Constraints
Data Modeling
Medium2 mins
Try practice test
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 Teradata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 Teradata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.

invite candidates
Reason #7

Detailed scorecards & benchmarks

查看样本记分卡
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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.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Teradata在线测试

Why you should use Teradata在线测试?

The Teradata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:

  • Teradata数据库管理和管理
  • SQL查询优化和性能调整
  • ETL设计和实施
  • 数据建模和数据库设计
  • 数据分析和报告
  • 数据集成和合并
  • 数据迁移和转换
  • Teradata实用程序和工具能力
  • 了解和实施Teradata安全
  • 开发和维护Teradata数据仓库

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 Teradata在线测试?

  • sql

    <p> sql (结构化查询语言)是一种用于管理和操纵关系数据库的编程语言。它允许用户从数据库中检索,插入,更新和删除数据。测量测试中的SQL技能很重要,因为它是与Teradata互动并执行各种数据库操作的重要语言。

  • 数据库管理

    数据库管理涉及与设计,组织的任务,维护数据库。它包括创建和管理数据库模式,优化数据库性能,确保数据完整性和实施安全措施等活动。该技能应在测试中测量以评估候选人有效地管理和管理数据库的能力。

  • etl </h4>

    etl(提取,变换,负载)是从中提取数据的过程各种来源,将其转换为合适的格式,然后将其加载到目标数据库或数据仓库中。这是测试中的一项重要技能,因为它证明了候选人在ETL过程中处理数据集成,数据清理和数据质量的能力。

  • 数据建模

    数据建模是创建数据结构,关系和约束的概念表示以支持高效且准确的数据管理的过程。测量测试中的数据建模技能至关重要,因为它评估了候选人设计逻辑和物理数据模型的能力,该模型与业务需求保持一致并促进数据一致性和完整性。

  • 数据分析

    数据分析涉及分析和解释大量数据以发现可以推动业务决策的见解,趋势和模式。它需要统计分析,数据可视化和数据挖掘方面的技能。测量测试中的数据分析技能很重要,因为它评估了候选人使用各种分析技术从数据中获得有意义见解的能力。

  • 数据集成

    数据集成是指结合的过程来自不同来源的数据,并为用户提供集成数据的统一视图。它涉及将数据从不同的系统提取,转换和加载到合并数据存储库中。测量测试中的数据集成技能至关重要,因为它评估了候选人处理复杂数据集成方案,确保数据一致性并减轻数据质量问题的能力。

  • 数据迁移

    数据迁移是将数据从一个系统传输到另一个系统的过程,通常是在系统升级或过渡期间。它涉及从源系统中提取数据,将其转换为兼容格式,并将其加载到目标系统中。测量测试中的数据迁移技能很重要,因为它评估了候选人计划,执行和验证数据迁移活动的能力,同时确保数据的准确性,完整性和完整性。

  • 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 Teradata在线测试 to be based on.

    Teradata
    SQL
    数据库管理
    etl
    数据建模
    数据分析
    数据集成
    数据迁移
    Teradata实用程序
    Teradata系统管理
    Teradata数据库备份和恢复
    Teradata并行处理体系结构
    Teradata数据仓库设备
    Teradata备份和恢复程序
    Teradata多弹药
    Teradata快速载荷
    Teradata QueryGrid
    Teradata Queryband
    Teradata数据字典
    Teradata元数据管理
    Teradata观点
    Teradata数据移动器
    Teradata数据库用户和角色
    Teradata索引向导
    Teradata SQL助理
    Teradata工作负载管理
    Teradata查询优化
    Teradata tpump
    Teradata tpt
    Teradata逻辑数据模型
    Teradata物理数据模型
    Teradata Presto
    Teradata压缩技术
    Teradata数据存储选项
    Teradata Bteq
    Teradata FastExport
    Teradata守护程序程序
    Teradata公用事业计划
    Teradata地理空间数据
    Teradata数据谱系
    Teradata影响分析
    Teradata Unity
    Teradata Unity总监
    Teradata SQL查询优化
    Teradata SQL参考
    Teradata数据库管理
    Teradata数据归档
    Teradata数据保留
    Teradata ODBC驱动程序
    Teradata JDBC驱动程序
    Teradata数据加载问题
    Teradata数据运动问题
    Teradata统计向导
    Teradata数据库设计
    Teradata数据复制
    Teradata数据同步
    Teradata LOB数据类型
    Teradata数据集市
    Teradata数据仓库
    Teradata Studio
    Teradata数据库查询调度程序
    Teradata数据掩盖
    Teradata数据匿名
    Teradata安全区域
    Teradata工具和公用事业
    Teradata数据库容量计划
    Teradata性能调整
    Teradata SQL格式化
    Teradata QueryGrid Manager
    Teradata数据访问控制
    Teradata授权
    Teradata数据移动器
    Teradata ET和LT
    Teradata数据库备份
    Teradata数据库还原
    Teradata系统仿真工具
    Teradata审核
    Teradata数据质量
    Teradata数据治理
    Teradata并行数据泵
    Teradata多系统经理
    Teradata数据集成
    Teradata元数据服务
    Teradata数据实验室
    Teradata能力计划
    Teradata系统性能监控
    Teradata JDBC提供商
    Teradata ODBC驱动程序
    Teradata数据复制
    Teradata数据同步
    Teradata数据字典
    Teradata数据保护
    Teradata数据虚拟化
    Teradata分析功能
    Teradata AppCenter
    Teradata XML服务
    Teradata数据库升级
    Teradata数据库补丁
    Teradata查询重写
    Teradata查询优化
    Teradata主动系统管理
    Teradata远程控制台
    Teradata数据安全
    Teradata数据隐私
    Teradata生态系统经理
    Teradata Unity Data Mover
    Teradata数据加密
    Teradata密钥管理
    Teradata安全企业搜索
    Teradata投资组合Hadoop
    Teradata数据仓库架构
    Teradata数据隐私
    Teradata数据流实用程序
    Teradata数据压缩
    Teradata数据归档
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What roles can I use the Teradata在线测试 for?

  • Teradata数据库管理员
  • Teradata技术专家
  • ETL开发人员
  • 数据仓库开发人员
  • 数据集成专家
  • 数据迁移专家

How is the Teradata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

  • 数据库索引和分区
  • Teradata SQL语法和函数
  • Teradata文件系统和存储管理
  • 数据质量管理和治理
  • Teradata备份和恢复程序
  • 分析和故障排除Teradata性能问题
  • ETL工作计划和自动化
  • 数据建模方法和最佳实践
  • 数据仓库概念和架构
  • Teradata系统性能监视和优化
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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

Teradata在线测试 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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