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About the test:

The Data Engineer Online Test uses scenario-based multiple-choice questions to evaluate candidates on their expertise in data engineering, which involves designing, building, and maintaining data architectures, databases, and processing systems. The test gauges candidates' proficiency in data modeling and warehousing, ETL (Extract, Transform, Load) processes, data pipeline construction, distributed computing systems, database systems, data security principles, and performance optimization strategies for data systems.

Covered skills:

  • Data Modeling
  • ETL (Extract
  • Load)
  • SQL CRUD Queries
  • Data Analysis and Visualization
  • Data Warehousing
  • Transform
  • Database Design
  • SQL Joins and Indexes
  • Coding

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9 reasons why
9 reasons why

Adaface Data Engineer Assessment Test is the most accurate way to shortlist Data Engineers



Reason #1

Tests for on-the-job skills

The Data Engineer 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 efficient and scalable data models
  • Proficiency in ETL processes and tools
  • Knowledge of data warehouse concepts and architecture
  • Ability to write complex SQL queries for data analysis
  • Experience in database design and optimization
  • Skills in data analysis and visualization
  • Proficiency in coding and problem-solving
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

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

🧐 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

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.

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:
 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
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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
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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
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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.

Easy

Registration Queue
Logic
Queues
Solve
We want to register students for the next semester. All students have a receipt which shows the amount pending for the previous semester. A positive amount (or zero) represents that the student has paid extra fees, and a negative amount represents that they have pending fees to be paid. The students are in a queue for the registration. We want to arrange the students in a way such that the students who have a positive amount on the receipt get registered first as compared to the students who have a negative amount. We are given a queue in the form of an array containing the pending amount.
For example, if the initial queue is [20, 70, -40, 30, -10], then the final queue will be [20, 70, 30, -40, -10]. Note that the sequence of students should not be changed while arranging them unless required to meet the condition.
⚠️⚠️⚠️ Note:
- The first line of the input is the length of the array. The second line contains all the elements of the array.
- The input is already parsed into an array of "strings" and passed to a function. You will need to convert string to integer/number type inside the function.
- You need to "print" the final result (not return it) to pass the test cases.

For the example discussed above, the input will be:
5
20 70 -40 30 -10

Your code needs to print the following to the standard output:
20 70 30 -40 -10

Medium

Visitors Count
Strings
Logic
Solve
A manager hires a staff member to keep a record of the number of men, women, and children visiting the museum daily. The staff will note W if any women visit, M for men, and C for children. You need to write code that takes the string that represents the visits and prints the count of men, woman and children. The sequencing should be in decreasing order. 
Example:

Input:
WWMMWWCCC

Expected Output: 
4W3C2M

Explanation: 
‘W’ has the highest count, then ‘C’, then ‘M’. 
⚠️⚠️⚠️ Note:
- The input is already parsed and passed to a function.
- You need to "print" the final result (not return it) to pass the test cases.
- If the input is- “MMW”, then the expected output is "2M1W" since there is no ‘C’.
- If any of them have the same count, the output should follow this order - M, W, C.
🧐 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
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Hard

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

2 mins

SQL
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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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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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Medium

Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries

2 mins

Data Warehouse
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Medium

Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design

2 mins

Data Warehouse
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Medium

Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning

2 mins

Data Warehouse
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Easy

Registration Queue
Logic
Queues

30 mins

Coding
Solve

Medium

Visitors Count
Strings
Logic

30 mins

Coding
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Multi Select
JOIN
GROUP BY
SQL
Medium2 mins
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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
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
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
Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Data Warehouse
Medium2 mins
Try practice test
Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design
Data Warehouse
Medium2 mins
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Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning
Data Warehouse
Medium2 mins
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Registration Queue
Logic
Queues
Coding
Easy30 minsSolve
Visitors Count
Strings
Logic
Coding
Medium30 minsSolve
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

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Data Engineer 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 Data Engineer 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
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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


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About the Data Engineer Online Test

Why you should use Pre-employment Data Engineer Test?

The Data Engineer 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:

  • Performing SQL CRUD queries
  • Designing data models
  • Implementing ETL processes
  • Creating data warehouses
  • Optimizing SQL joins and indexes
  • Analyzing and visualizing data
  • Writing efficient coding solutions
  • Developing database design
  • Ensuring data integrity and security
  • Troubleshooting and debugging

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 Engineer Test?

  • Data Modeling

    Data modeling involves creating and designing a logical representation of the data structures and relationships within a database, ensuring the integrity and efficiency of data storage and retrieval.

  • Data Warehousing

    Data warehousing is the process of collecting, organizing, and storing large amounts of structured data from different sources, enabling effective reporting, analysis, and decision-making.

  • ETL (Extract, Transform, Load)

    ETL refers to the three-step process of extracting data from various sources, transforming it into a consistent format, and loading it into a data warehouse or database for analysis and reporting purposes.

  • Database Design

    Database design involves creating the blueprint for organizing and structuring data in a database system, determining the tables, relationships, and constraints necessary to efficiently store and manage data.

  • SQL CRUD Queries

    SQL CRUD (Create, Read, Update, Delete) queries are used to manipulate data stored in relational databases, allowing users to insert new records, retrieve existing data, update information, and delete records.

  • SQL Joins and Indexes

    SQL Joins combine data from multiple tables based on common columns, enabling more complex queries and data retrieval. SQL Indexes improve database performance by providing fast access to specific subsets of data.

  • Data Analysis and Visualization

    Data analysis involves inspecting, cleaning, transforming, and modeling data to identify useful patterns and trends. Data visualization presents this analyzed data in graphical or visual formats, aiding in understanding and decision-making.

  • Coding

    Coding refers to the process of writing and implementing computer programs in programming languages to accomplish specific tasks. It is essential for developing efficient data processing and analysis solutions.

  • 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 Data Engineer Test to be based on.

    SQL Basics
    SQL Joins
    SQL Indexes
    SQL CRUD Operations
    Relational Data Modeling
    Dimensional Data Modeling
    Star Schema
    Snowflake Schema
    ETL Extraction
    ETL Transformation
    ETL Load
    Data Warehouse Architecture
    OLTP vs. OLAP
    Database Normalization
    Indexes and Optimization
    Data Analysis Techniques
    Data Visualization Tools
    Data Cleaning
    Data Aggregation
    SQL Aggregate Functions
    Common Table Expressions (CTE)
    Window Functions
    Database Partitioning
    Fact and Dimension Tables
    Data Mart
    Data Integration
    Slowly Changing Dimensions
    ETL Best Practices
    Data Quality Assurance
    Data Validation
    Data Warehousing Concepts
    Data Governance
    Data Analytics
    Big Data Technologies
    Data Modeling Techniques
    Logical Data Models
    Physical Data Models
    Data Transformation
    Database Joins
    Database Triggers
    Database Constraints
    Data Extraction Methods
    Data Loading Strategies
    Database Normal Forms
    Data Visualization Principles
    Coding Best Practices
    Coding Efficiency
    Debugging Techniques
    Code Optimization
    Error Handling
    Data Privacy and Security
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What roles can I use the Data Engineer Test for?

  • Data Engineer
  • Database Administrator
  • Data Analyst
  • Business Intelligence Developer
  • ETL Developer

How is the Data Engineer 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

  • Building scalable data pipelines
  • Optimizing data storage and retrieval
  • Constructing efficient data schemas
  • Implementing dimensional modeling
  • Transforming and cleaning data
  • Working with big data technologies
  • Building data processing frameworks
  • Employing data cleaning techniques
  • Utilizing data visualization tools
  • Managing large-scale data systems

The coding question for experienced candidates will be of a higher difficulty level to evaluate more hands-on experience.

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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

Data Engineer Hiring Test FAQs

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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