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About the test:

Informatica测试评估了候选人将PowerCenter用于ETL的能力。它评估了执行数据同步/复制任务,设计数据转换,管理源/目标定义和数据争吵的能力,而无需编写SQL,可以应用过滤器,加入,汇总,合并,合并和表达逻辑。

Covered skills:

  • 数据仓库
  • 数据集成
  • 数据库加入
  • 参数化
  • 会议和任务
  • 提取转换负载(ETL)
  • 关系数据库CRUD操作
  • mapplet
  • 工作流程
  • 转型

9 reasons why
9 reasons why

Adaface Informatica在线测试 is the most accurate way to shortlist Informatica开发人员s



Reason #1

Tests for on-the-job skills

The Informatica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:

  • 设计和实施数据仓库解决方案的能力
  • 在大型数据集上执行提取提取转换负载(ETL)操作的能力
  • 精通将各种数据源集成到统一数据库中
  • 执行关系数据库CRUD操作的技巧
  • 能够构建和优化数据库连接的能力
  • 与Mapplet合作进行数据转换方面的知识
  • 数据工作流程参数化的专业知识
  • 在数据集成过程中管理会话和任务的能力
  • 熟练使用各种数据转换
  • 能够故障排除和处理数据处理中的错误
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 Informatica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

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

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.
🧐 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

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

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
🧐 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
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
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
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 Informatica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 Informatica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 Informatica在线测试

Why you should use Informatica在线测试?

The Informatica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:

  • 数据仓库概念和原理
  • 提取转换负载(ETL)过程
  • 数据集成技术和最佳实践
  • 关系数据库CRUD操作
  • 数据库加入类型和优化
  • Mapplet及其在Informatica PowerCenter中的用法
  • 参数化以增强ETL过程的灵活性
  • Informatica PowerCenter的工作流创建和管理
  • Informatica PowerCenter中的会话和任务配置
  • ETL过程中的转换类型和用法

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 Informatica在线测试?

  • 提取转换负载(ETL)</ H4> <p> ETL是从各种来源提取数据,将其转换为一致格式并将其加载到目标系统(通常是数据仓库)中的过程。在测试中评估了此技能,以评估候选人处理复杂数据集成任务并确保目标系统中数据的质量和可靠性的能力。</p> <h4>数据集成

    数据集成涉及结合了可能是结构化或非结构化的多个来源的数据,以提供分析和报告的统一视图。在测试中衡量了候选人对该技能的熟练程度,以评估其整合多种数据源并确保整个组织的数据一致性和准确性的能力。

  • 关系数据库CRUD CRUD操作

    crud操作请参阅在关系数据库上执行的创建,阅读,更新和删除操作。在测试中评估了此技能,以评估候选人对数据库管理的理解及其使用SQL语句操纵数据的能力。 CRUD操作的熟练程度对于从关系数据库中有效地维护和检索数据至关重要。

  • 数据库JOINS

    数据库联接使用用于根据公共字段或钥匙来组合来自多个表的数据。在测试中测量了该技能,以确定候选人在构建复杂的SQL查询方面的专业知识,这些查询涉及不同类型的连接,例如内联机,外部连接和交叉连接。数据库加入的熟练程度对于有效地从关系数据库中检索和分析数据至关重要。

  • mapplets

    mapplet是Informatica PowerCenter中可重复使用的映射组件,这使开发人员可以定义和存储公共转换,并将其存储通用转换。可以从多个映射中调用。在测试中评估了此技能,以评估候选人对Mapplet创建,配置和用法的了解,以及他们对数据转换和映射设计原理的理解。

  • 参数化

    参数化是通过使用参数使映射组件动态和可配置的过程。在测试中测量了该技能,以评估候选人设计映射的能力,这些映射可以通过参数化各种属性和值来适应不同的运行时方案。熟练参数化有助于在Informatica PowerCenter中创建灵活和可重复使用的映射。

  • 工作流程,会话和任务

    工作流程,会话,任务和任务是Informatica PowerCenter的基础,可让开发人员能够开发人员创建和管理复杂的数据集成过程。在测试中评估了此技能,以评估候选人对工作流程设计,会话配置和任务依赖性的理解。精通工作流,会话和任务的熟练程度对于有效地在Informatica PowerCenter中精心编排数据集成过程至关重要。

  • 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 Informatica在线测试 to be based on.

    数据建模
    尺寸建模
    星模架
    雪花图架
    事实表
    尺寸表
    ETL过程
    来源系统分析
    数据分析
    数据清理
    数据转换
    数据集成
    数据加载
    代理钥匙
    增量负载
    更改数据捕获
    缓慢改变尺寸
    元数据管理
    关系型数据库
    SQL操作
    Crud操作
    数据库加入
    内部联接
    外联机
    左加入
    正确加入
    完整的外部连接
    交叉加入
    自我加入
    聚合转换
    木匠转换
    滤波器转换
    表达转换
    路由器转换
    查找转换
    合并转换
    归一化器转换
    等级转换
    序列发生器变换
    聚合器转换
    工会转型
    混蛋转换
    路由器转换
    条件转换
    可重复使用的转换
    表达语言
    工作流程设计
    任务依赖性
    会话属性
    参数文件
    会议和任务监视
    错误处理
    工作流程计划
    数据仓库架构

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

  • Informatica开发人员
  • 高级Informatica开发人员
  • Informatica建筑师
  • 数据集成开发人员(Informatica)
  • 软件工程师(Informatica)
  • 数据工程师(Informatica)
  • Informatica ETL开发人员
  • Informatica BI顾问

How is the Informatica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

  • 数据质量管理和清洁技术
  • ETL流程中的错误处理和异常管理
  • ETL过程的性能优化和调整
  • 实时数据集成和流
  • 更改数据捕获(CDC)技术
  • 数据验证和测试策略
  • 元数据管理和影响分析
  • 数据仓库的尺寸建模概念
  • 数据库索引策略和查询优化
  • Informatica PowerCenter中的脚本和自动化
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

Informatica在线测试 常见问题解答

我可以在同一测试中评估其他相关技能,例如ETL,SQL吗?

是的。我们支持在一次测试中筛选多个技能。您可以查看我们的[标准SQL测试](https://www.adaface.com/assessment-test/sql-online-test)和[标准ETL测试](https://wwwww.adaface.com/assessment.com/assessment-test-test /ETL-ONLINE检验),以了解我们用来评估SQL和ETL技能的哪些类型的问题。注册任何计划后,您可以请求自定义评估,以根据您的职位描述进行自定义。自定义评估将包括有关您的Informatica角色所需的所有必备技能的问题。

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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