What roles can I use the Data Mining Assessment Test for?

Here are few roles for which we recommend this test: Data Scientist Business Analyst Data Analyst Data Engineer Database Administrator Research Scientist

What is the Data Mining Test?

The Data Mining Test assesses candidates' proficiency in various data mining techniques including statistical analysis, decision trees, and clustering. It is useful for recruiters hiring data analysts and data scientists to ensure they have the necessary skills.

How to use the Data Mining Test in my hiring process?

Administer this test early in your recruitment process as a pre-screening tool. You can add a link to the assessment in your job post or invite candidates directly by email. This approach helps identify skilled candidates faster and more accurately.

Can I combine Data Mining Test with Data Modeling questions?

Yes, recruiters can request a custom test that includes both Data Mining and Data Modeling questions. Check out our Data Modeling Skills Test for more details.

What are the main Data Analysis tests?

Some important tests in the Data Analysis category include: Data Analysis Test Data Science Assessment Test Data Wrangling Test Statistics Test

Data Mining Test

Q: What skills are evaluated in this test?

The test covers skills such as Data Processing, Data Warehouse and OLAP Technology, Data Preprocessing, Mining Frequent Patterns, Data Cleaning, Data Reduction, Data Mining Process, and Data Integration and Transformation.

The Data Mining Test evaluates candidates on their knowledge of data mining techniques, data preprocessing, association rule mining, classification, clustering, and data visualization using scenario-based MCQs. In addition to these key skills, the test also assesses a candidate's understanding of data warehousing, data cleaning, and big data technologies.

Get started for free

Preview questions

Screen candidates with a 40 mins test

Test duration: ~ 40 mins

Difficulty level: Moderate

Availability: Available as custom test

Questions:

7 Data Mining MCQs
5 Data Modeling MCQs
5 ETL MCQs

Covered skills:

Data Processing

Data Warehouse and OLAP Technology

Data Preprocessing

Mining Frequent Patterns

Data Cleaning

Data Reduction

Data Mining Process

Data Integration and Transformation

Get started for free

Preview questions

Use Adaface tests trusted by recruitment teams globally

Adaface is used by 1500+ businesses in 80 countries.

Adaface skill assessments measure on-the-job skills of candidates, providing employers with an accurate tool for screening potential hires.

Use the Data Mining Assessment Test to shortlist qualified candidates

The Data Mining 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 extract meaningful insights from large datasets
Proficiency in data modeling techniques
Understanding of ETL (Extract, Transform, Load) processes
Knowledge of data processing and analysis
Familiarity with data warehouse and OLAP technology
Ability to preprocess data for mining purposes
Experience with mining frequent patterns in datasets
Ability to clean and reduce data noise
Understanding of the data mining process
Competency in data integration and transformation

Get started for free

Preview questions

Screen candidates with the highest quality 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 15,000+ questions. The actual questions on this Data Mining Test will be non-googleable.

🧐 Question
Easy Healthcare System Data Integrity Normalization Referential Integrity	Solve
You are designing a data model for a healthcare system with the following requirements: 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	Solve
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?
Medium Normalization Process Normalization Database Design Anomaly Elimination	Solve
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	Solve
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	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: 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: 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: 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
	Easy Healthcare System Data Integrity Normalization Referential Integrity	2 mins Data Modeling	Solve
You are designing a data model for a healthcare system with the following requirements: 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	2 mins Data Modeling	Solve
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?
	Medium Normalization Process Normalization Database Design Anomaly Elimination	3 mins Data Modeling	Solve
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	2 mins Data Modeling	Solve
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	2 mins ETL	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: 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	2 mins ETL	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	3 mins ETL	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: 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	3 mins ETL	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: 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	💪 Difficulty	⌛ Time
	Healthcare System Data Integrity Normalization Referential Integrity	Data Modeling	Easy	2 mins	Solve
You are designing a data model for a healthcare system with the following requirements: 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.
	ER Diagram and minimum tables ER Diagram	Data Modeling	Hard	2 mins	Solve
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?
	Normalization Process Normalization Database Design Anomaly Elimination	Data Modeling	Medium	3 mins	Solve
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.
	University Courses ER Diagrams Complex Relationships Integrity Constraints	Data Modeling	Medium	2 mins	Solve
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.
	Data Merging Data Merging Conditional Logic	ETL	Medium	2 mins	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: 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.
	Data Updates Staging Data Warehouse	ETL	Medium	2 mins	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
	SQL in ETL Process SQL Code Interpretation Data Transformation SQL Functions	ETL	Medium	3 mins	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: 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.
	Trade Index Index	ETL	Medium	3 mins	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: 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

Test candidates on core Data Mining Hiring Test topics

Data Preprocessing: Data preprocessing involves preparing and cleaning the data before the actual mining process takes place. It includes tasks like removing noise, handling missing values, standardizing data, and transforming variables. Measuring this skill in the test helps evaluate a candidate's ability to preprocess data effectively, ensuring the quality and reliability of the mining results.

Mining Frequent Patterns: Mining frequent patterns focuses on discovering recurring itemsets or sequences in a dataset. It involves techniques like market basket analysis and association rule mining. This skill should be measured in the test to assess a candidate's proficiency in identifying common patterns, which can be valuable for various applications such as recommendation systems and market analysis.

Data Cleaning: Data cleaning is the process of identifying and correcting or removing errors, inconsistencies, and outliers in the dataset. It includes tasks like handling duplicate records, resolving inconsistencies, and dealing with noisy or irrelevant data. Measuring this skill in the test helps evaluate a candidate's ability to ensure data integrity and reliability, which is crucial for accurate mining results.

Data Reduction: Data reduction involves techniques for reducing the size and dimensionality of the dataset without significantly losing relevant information. It aims to remove redundant or irrelevant features and transform the data into a more compact representation. Measuring this skill in the test helps evaluate a candidate's ability to optimize the data mining process by reducing computational complexity and improving efficiency.

Data Mining Process: Data mining process encompasses the systematic steps involved in extracting meaningful patterns and insights from data. It includes tasks like data exploration, model selection, pattern evaluation, and result interpretation. Measuring this skill in the test helps evaluate a candidate's understanding of the overall data mining workflow and their ability to apply appropriate techniques at each stage.

Data Integration and Transformation: Data integration and transformation involve consolidating data from various sources, resolving data conflicts, and transforming data into a unified format for analysis. It requires knowledge of data integration techniques, data mapping, and data transformation operations. Measuring this skill in the test helps evaluate a candidate's ability to effectively integrate and transform disparate data sources, ensuring consistency and accuracy in the mining process.

Get started for free

Preview questions

Make informed decisions with actionable reports and benchmarks

View sample scorecard

Screen candidates in 3 easy steps

Pick a test from over 500+ tests

The Adaface test library features 500+ tests to enable you to test candidates on all popular skills- everything from programming languages, software frameworks, devops, logical reasoning, abstract reasoning, critical thinking, fluid intelligence, content marketing, talent acquisition, customer service, accounting, product management, sales and more.

Invite your candidates with 2-clicks

Make informed hiring decisions

Get started for free

Preview questions

Try the most advanced candidate assessment platform

ChatGPT Protection

Non-googleable Questions

Web Proctoring

IP Proctoring

Webcam Proctoring

MCQ Questions

Coding Questions

Typing Questions

Personality Questions

Custom Questions

Ready-to-use Tests

Custom Tests

Custom Branding

Bulk Invites

Public Links

ATS Integrations

Multiple Question Sets

Custom API integrations

Role-based Access

Priority Support

GDPR Compliance

Pick a plan based on your hiring needs

The most advanced candidate screening platform.
14-day free trial. No credit card required.

From

$15

per month (paid annually)

View pricing plans

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

It's very easy to share assessments with candidates and for candidates to use. We get good feedback from candidates about completing the tests. Adaface are very responsive and friendly to deal with.

Kirsty Wood, Human Resources, WillyWeather

We were able to close 106 positions in a record time of 45 days! Adaface enables us to conduct aptitude and psychometric assessments seamlessly. My hiring managers have never been happier with the quality of candidates shortlisted.

Amit Kataria, CHRO, Hanu

We evaluated several of their competitors and found Adaface to be the most compelling. Great library of questions that are designed to test for fit rather than memorization of algorithms.

Swayam Narain, CTO, Affable