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About the test:

数据仓库在线测试使用基于方案的多项选择问题来评估候选人在数据仓库方面的专业知识,其中涉及设计,构建和维护仓库,数据库和数据集市。

Covered skills:

  • SQL基础知识
  • SQL子征服和加入
  • ER图
  • 事实表和归一化
  • SQL Crud查询
  • ETL基本面
  • 数据建模
  • 数据仓库基本面

9 reasons why
9 reasons why

Adaface Data Warehouse Test is the most accurate way to shortlist 数据仓库开发人员s



Reason #1

Tests for on-the-job skills

The Data Warehouse 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SQL查询以操纵和检索数据库的能力
  • 了解数据仓库概念和原理
  • 了解ETL(提取,转换,负载)过程
  • 熟练创建和优化ER图
  • 设计和实施数据模型的能力
  • 熟悉事实表和数据库归一化
  • 了解数据仓库基本面
  • 分析和解释数据的能力
  • 使用SQL执行CRUD(创建,阅读,更新,删除)操作的技能
  • 使用子征服和加入SQL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 数据仓库在线测试 将是不可行的.

🧐 Question

Medium

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

Medium

Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Solve
You are a data warehouse engineer at a marketing agency, managing a large-scale database that stores extensive data on customer interactions, campaign metrics, and market research. The database is used predominantly for complex analytical queries, such as segment analysis, trend identification, and campaign performance evaluation. These queries often involve aggregations, filtering, and joining over large datasets.

The existing setup, using traditional row-oriented storage, is struggling with performance issues, particularly for ad-hoc analytical queries that span multiple tables and require aggregating large volumes of data.

The main tables in the database are:

- Customer_Interactions (millions of rows): Stores individual customer interaction data.
- Campaign_Metrics (hundreds of thousands of rows): Contains detailed metrics for each marketing campaign.
- Market_Research (tens of thousands of rows): Holds market research data and findings.

Considering the nature of the queries and the structure of the data, which of the following changes would most effectively optimize the query performance for analytical purposes?
A: Normalize the database further by splitting large tables into smaller, more focused tables and creating indexes on frequently joined columns.
B: Implement an in-memory database system to facilitate faster data retrieval and processing.
C: Convert the database to use columnar storage, optimizing for the types of analytical queries performed in the marketing context.
D: Create a series of materialized views to pre-aggregate data for common query patterns.
E: Increase the hardware capacity of the server, focusing on faster CPUs and more RAM.
F: Implement partitioning on the main tables based on commonly filtered attributes, such as campaign IDs or time periods.

Medium

Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design
Solve
As a senior data warehouse engineer at a large retail company, you are tasked with designing a multidimensional data model to support complex OLAP (Online Analytical Processing) operations for retail analytics. The company operates in multiple countries and deals with a wide range of products. The primary requirement is to enable efficient analysis of sales performance across various dimensions such as time, geography, product categories, and sales channels.

The source data resides in a transactional system with the following tables:

- Transactions (Transaction_ID, Date, Store_ID, Product_ID, Quantity, Unit_Price)
- Stores (Store_ID, Store_Name, Country, Region)
- Products (Product_ID, Product_Name, Category, Supplier_ID)
- Suppliers (Supplier_ID, Supplier_Name, Country)

You need to design a schema in the data warehouse that facilitates fast querying for aggregations and comparisons along the mentioned dimensions. Which of the following schemas would best serve this purpose?
A: A star schema with a central fact table linking to dimension tables for Time, Store, Product, and Supplier.
B: A snowflake schema where dimension tables for Store, Product, and Supplier are normalized.
C: A galaxy schema with separate fact tables for Transactions, Inventory, and Supplier Orders, linked to shared dimension tables.
D: A flat schema combining all source tables into a single wide table to avoid joins during querying.
E: An OLTP-like normalized schema to maintain data integrity and minimize redundancy.
F: A hybrid schema using a star schema for frequently queried dimensions and a snowflake schema for less queried, more detailed dimensions.

Medium

Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning
Solve
As a senior data warehouse developer, you are tasked with optimizing query performance in a large-scale data warehouse that primarily stores transactional data for a global retail company. The data warehouse is facing significant performance issues, particularly with certain types of queries that are crucial for business operations. After analysis, you identify that the most problematic queries are those that involve filtering and aggregating transaction data based on time periods (e.g., monthly sales) and specific product categories.

The main transaction table (Transactions) in the data warehouse has the following structure and characteristics:

- Columns: Transaction_ID (bigint), Transaction_Date (date), Product_ID (int), Quantity (int), Price (decimal), Category_ID (int)
- Row count: Approximately 2 billion rows
- Most common query pattern: Aggregating Quantity and Price by Category_ID and Transaction_Date (e.g., total sales per category per month)
- Current indexing: Primary key index on Transaction_ID, no other indexes

Based on this information, which of the following approaches would most effectively optimize the query performance for the given use case?
A: Add a non-clustered index on Transaction_Date and Category_ID.
B: Normalize the Transactions table by splitting Transaction_Date and Category_ID into separate dimension tables.
C: Implement partitioning on the Transactions table by Transaction_Date, and add a bitmap index on Category_ID.
D: Convert the Transactions table to use a columnar storage format.
E: Create a materialized view that pre-aggregates data by Category_ID and Transaction_Date.
F: Increase the hardware capacity of the data warehouse server, focusing on CPU and memory upgrades.

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

Medium

Multi Select
JOIN
GROUP BY

2 mins

SQL
Solve

Medium

nth highest sales
Nested queries
User Defined Functions

3 mins

SQL
Solve

Medium

Select & IN
Nested queries

3 mins

SQL
Solve

Medium

Sorting Ubers
Nested queries
Join
Comparison operators

3 mins

SQL
Solve

Hard

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

2 mins

SQL
Solve

Medium

Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries

2 mins

Data Warehouse
Solve

Medium

Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design

2 mins

Data Warehouse
Solve

Medium

Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning

2 mins

Data Warehouse
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
Multi Select
JOIN
GROUP BY
SQL
Medium2 mins
Solve
nth highest sales
Nested queries
User Defined Functions
SQL
Medium3 mins
Solve
Select & IN
Nested queries
SQL
Medium3 mins
Solve
Sorting Ubers
Nested queries
Join
Comparison operators
SQL
Medium3 mins
Solve
With, AVG & SUM
MAX() MIN()
Aggregate functions
SQL
Hard2 mins
Solve
Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Data Warehouse
Medium2 mins
Solve
Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design
Data Warehouse
Medium2 mins
Solve
Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning
Data Warehouse
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 数据仓库在线测试 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 数据仓库在线测试 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
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 Warehouse Assessment Test

Why you should use Pre-employment Data Warehouse Online Test?

The 数据仓库在线测试 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:

  • SQL基础知识
  • SQL Crud查询
  • SQL子征服和加入
  • ETL基本面
  • ER图
  • 数据建模
  • 事实表和归一化
  • 数据仓库基本面
  • 处理数据库异常和错误
  • 优化SQL查询以进行性能

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 Warehouse Online Test?

  • sql crud coolies

    sql crud crud查询涉及数据库上的创建,读取,更新和删除操作。应在测试中测量此技能,以评估候选人使用SQL进行这些基本数据库操作的熟练程度。

  • SQL Subquies和Joins

    <p> SQL Subquies and Joins and Joins and Joins and Joins是用于组合的高级技术来自多个表的数据,并从数据库中检索特定信息。应在测试中测量此技能,以评估候选人优化复杂的SQL查询并有效地检索数据的能力。

  • etl基础

    etl基本面是指提取的原理和技术,将数据从不同来源转换为数据仓库。应在测试中测量此技能,以评估候选人对ETL过程,数据集成的理解及其与大型数据集合作的能力。

  • er图</h4>

    er图或实体图 - 关联图,是数据库模式的可视表示表示,该数据库模式说明了它们之间的实体,属性和关系。应在测试中测量此技能,以评估候选人使用ER图分析和设计数据库结构的能力。

  • 数据建模

    数据建模涉及设计和定义结构,约束,约束,约束,和数据库的关系。该技能应在测试中衡量,以评估候选人在基于组织要求的概念化,计划和实施数据库模型方面的熟练程度。

  • 事实表和归一化

    事实表标准化是数据库设计中使用的技术,以消除数据冗余并确保数据完整性。应在测试中测量此技能,以评估候选人对数据库标准化级别的理解及其设计高效且可扩展的数据库模式的能力。

  • 数据仓库基础知识

    数据仓库基本原理包括构建和管理数据仓库所涉及的概念,架构和过程。该技能应在测试中衡量,以评估候选人对数据仓库原理的了解,包括数据提取,转换,加载和报告。

  • 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 数据仓库在线测试 to be based on.

    SQL基础知识
    创建表
    选择语句
    插入语句
    更新语句
    删除语句
    SQL加入
    内部联接
    外联机
    交叉加入
    自我加入
    子征服
    相关的子征服
    标量亚征
    通用表表达式
    SQL聚合
    通过...分组
    有子句
    独特的关键字
    SQL功能
    字符串操纵
    日期和时间功能
    数学功能
    案例声明
    合并
    nullif
    SQL约束
    首要的关键
    外键
    独特的约束
    不是无效的约束
    检查约束
    索引
    数据仓库概念
    星模架
    雪花图架
    尺寸建模
    缓慢改变尺寸
    数据集市
    数据立方体
    ETL过程
    提炼
    转换
    加载
    数据集成
    数据质量
    数据分析
    数据清理
    ER图
    实体
    关系
    属性
    基数
    正常化
    第一种正常形式
    第二个正常形式
    第三正常形式
    BCNF
    事实表
    尺寸表
    代理钥匙
    数据仓库生命周期
    数据仓库架构
    ETL工具和技术
    数据可视化
    商业智能
    OLAP(在线分析处理)
    数据仓库安全性
    数据治理

What roles can I use the Data Warehouse Online Test for?

  • 数据仓库开发人员
  • 高级数据仓库开发人员
  • 数据仓库专家
  • ETL开发人员
  • 数据工程师数据仓库

How is the Data Warehouse 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

  • 在SQL中实施数据安全措施
  • 设计和构建ETL工作流程
  • 从各种数据源中提取数据
  • 转换和清洁数据进行分析
  • 将数据加载到数据仓库中
  • 理解和创建图表
  • 标准化数据
  • 创建和管理事实表
  • 实施数据完整性约束
  • 使用数据仓库工具和框架
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 Warehouse 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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