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About the test:

The Informatica test evaluates a candidate's ability to use PowerCenter for ETL. It assesses ability to execute data synchronization/ replication tasks, design data transformations, manage source/ target definitions and data wrangling by applying filter, join, aggregate, categorize, merge, and expression logic without writing SQL.

Covered skills:

  • Data warehousing
  • Data integration
  • Database Joins
  • Parameterization
  • Sessions and Tasks
  • Extract Transform Load (ETL)
  • Relational database CRUD operations
  • Mapplets
  • Workflows
  • Transformations

9 reasons why
9 reasons why

Adaface Informatica Test is the most accurate way to shortlist Informatica Developers



Reason #1

Tests for on-the-job skills

The Informatica 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:

  • Ability to design and implement data warehousing solutions
  • Capability to perform Extract Transform Load (ETL) operations on large datasets
  • Proficiency in integrating various data sources into a unified database
  • Skill in executing relational database CRUD operations
  • Ability to construct and optimize database joins
  • Knowledge in working with Mapplets for data transformation
  • Expertise in parameterization of data workflows
  • Competence in managing sessions and tasks in a data integration process
  • Proficiency in using various data transformations
  • Capability to troubleshoot and handle errors in data processing
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

These are just a small sample from our library of 10,000+ questions. The actual questions on this Informatica Online Test will be non-googleable.

🧐 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

With Adaface, we were able to optimise our initial screening process by upwards of 75%, freeing up precious time for both hiring managers and our talent acquisition team alike!


Brandon Lee, Head of People, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Informatica Online Test 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 Online Test 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

View sample scorecard
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 Assessment Test

Why you should use Pre-employment Informatica Online Test?

The Informatica Online Test 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:

  • Data warehousing concepts and principles
  • Extract Transform Load (ETL) process
  • Data integration techniques and best practices
  • Relational database CRUD operations
  • Database join types and optimization
  • Mapplets and their usage in Informatica PowerCenter
  • Parameterization to enhance flexibility in ETL processes
  • Workflow creation and management in Informatica PowerCenter
  • Session and task configurations in Informatica PowerCenter
  • Transformation types and usage in ETL processes

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 Online Test?

  • Data Warehousing

    Data warehousing is the process of collecting and managing data from various sources to support business intelligence and reporting. It involves designing and implementing a centralized repository for storing large volumes of data that can be queried and analyzed efficiently. This skill is measured in the test to assess candidates' knowledge in building and maintaining data warehouses, which is crucial for organizations to make informed decisions based on data-driven insights.

  • Extract Transform Load (ETL)

    ETL is the process of extracting data from various sources, transforming it into a consistent format, and loading it into a target system, typically a data warehouse. This skill is assessed in the test to evaluate candidates' ability to handle complex data integration tasks and ensure the quality and reliability of data in the target system.

  • Data Integration

    Data integration involves combining data from multiple sources, which may be structured or unstructured, to provide a unified view for analysis and reporting. Candidates' proficiency in this skill is measured in the test to gauge their capability to integrate diverse data sources and ensure data consistency and accuracy across the organization.

  • Relational Database CRUD Operations

    CRUD operations refer to Create, Read, Update, and Delete actions performed on a relational database. This skill is evaluated in the test to assess candidates' understanding of database management and their ability to manipulate data using SQL statements. Proficiency in CRUD operations is essential for maintaining and retrieving data efficiently from relational databases.

  • Database Joins

    Database joins are used to combine data from multiple tables based on common fields or keys. This skill is measured in the test to determine candidates' expertise in constructing complex SQL queries involving different types of joins, such as inner join, outer join, and cross join. Proficiency in database joins is essential for retrieving and analyzing data from relational databases efficiently.

  • Mapplets

    Mapplets are reusable mapping components in Informatica PowerCenter, which allow developers to define and store common transformations that can be called from multiple mappings. This skill is assessed in the test to evaluate candidates' knowledge of Mapplet creation, configuration, and usage, as well as their understanding of data transformations and mapping design principles.

  • Parameterization

    Parameterization is the process of making mapping components dynamic and configurable by using parameters. This skill is measured in the test to assess candidates' ability to design mappings that can adapt to different runtime scenarios by parameterizing various properties and values. Proficiency in parameterization helps in creating flexible and reusable mappings in Informatica PowerCenter.

  • Workflows, Sessions, and Tasks

    Workflows, sessions, and tasks are building blocks of Informatica PowerCenter that allow developers to create and manage complex data integration processes. This skill is assessed in the test to evaluate candidates' understanding of workflow design, session configuration, and task dependencies. Proficiency in working with workflows, sessions, and tasks is essential for effectively orchestrating data integration processes in Informatica PowerCenter.

  • Transformations

    Transformations in Informatica PowerCenter are used to manipulate, validate, and aggregate data during the ETL process. This skill is measured in the test to determine candidates' knowledge and expertise in different types of transformations, such as Aggregator, Expression, Lookup, and Filter. Proficiency in transformations is crucial for data cleansing, enrichment, and integration in data warehousing projects.

  • 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 Online Test to be based on.

    Data modeling
    Dimensional modeling
    Star schema
    Snowflake schema
    Fact table
    Dimension table
    ETL process
    Source system analysis
    Data profiling
    Data cleansing
    Data transformation
    Data integration
    Data loading
    Surrogate keys
    Incremental loads
    Change data capture
    Slowly changing dimensions
    Metadata management
    Relational database
    SQL operations
    CRUD operations
    Database joins
    Inner join
    Outer join
    Left join
    Right join
    Full outer join
    Cross join
    Self join
    Aggregate transformations
    Joiner transformation
    Filter transformation
    Expression transformation
    Router transformation
    Lookup transformation
    Merge transformation
    Normalizer transformation
    Rank transformation
    Sequence generator transformation
    Aggregator transformation
    Union transformation
    Sorter transformation
    Router transformation
    Conditional transformations
    Reusable transformations
    Expression language
    Workflow design
    Task dependencies
    Session properties
    Parameter file
    Sessions and tasks monitoring
    Error handling
    Workflow scheduling
    Data warehouse architecture

What roles can I use the Informatica Online Test for?

  • Informatica Developer
  • Senior Informatica Developer
  • Informatica Architect
  • Data Integration Developer (Informatica)
  • Software Engineer (Informatica)
  • Data Engineer (Informatica)
  • Informatica ETL Developer
  • Informatica BI Consultant

How is the Informatica 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

  • Data quality management and cleansing techniques
  • Error handling and exception management in ETL processes
  • Performance optimization and tuning for ETL processes
  • Real-time data integration and streaming
  • Change Data Capture (CDC) techniques
  • Data validation and testing strategies
  • Metadata management and impact analysis
  • Dimensional modeling concepts for data warehousing
  • Database indexing strategies and query optimization
  • Scripting and automation in Informatica PowerCenter
Singapore government logo

The hiring managers felt that through the technical questions that they asked during the panel interviews, they were able to tell which candidates had better scores, and differentiated with those who did not score as well. They are highly satisfied with the quality of candidates shortlisted with the Adaface screening.


85%
reduction in screening time

Informatica Hiring Test FAQs

Can I evaluate other relevant skills like ETL, SQL in the same test?

Yes. We support screening multiple skills in a single test. You can review our standard SQL test and standard ETL test to understand what type of questions we use to evaluate SQL and ETL skills. Once you sign up for any plan, you can request a custom assessment that will be customized to your job description. The customized assessment will include questions for all the must-have skills required for your Informatica role.

Can I combine multiple skills into one custom assessment?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

Do you have any anti-cheating or proctoring features in place?

We have the following anti-cheating features in place:

  • Non-googleable questions
  • IP proctoring
  • Screen proctoring
  • Web proctoring
  • Webcam proctoring
  • Plagiarism detection
  • Secure browser
  • Copy paste protection

Read more about the proctoring features.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

What experience level can I use this test for?

Each Adaface assessment is customized to your job description/ ideal candidate persona (our subject matter experts will pick the right questions for your assessment from our library of 10000+ questions). This assessment can be customized for any experience level.

Does every candidate get the same questions?

Yes, it makes it much easier for you to compare candidates. Options for MCQ questions and the order of questions are randomized. We have anti-cheating/ proctoring features in place. In our enterprise plan, we also have the option to create multiple versions of the same assessment with questions of similar difficulty levels.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

What is the cost of using this test?

You can check out our pricing plans.

Can I get a free trial?

Yes, you can sign up for free and preview this test.

I just moved to a paid plan. How can I request a custom assessment?

Here is a quick guide on how to request a custom assessment on Adaface.

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