What roles can I use the Teradata MDM Test for?

Here are few roles for which we recommend this test: Teradata MDM Developer Sr. Teradata MDM Developer Sr MDM Developer MDM Applications

What is the Teradata MDM Test?

The Teradata MDM Test is designed to evaluate candidates' proficiency in Teradata Master Data Management and related data management skills. It is used by recruiters to find candidates with strong Teradata MDM functionalities knowledge and data integration abilities.

Can I combine Teradata MDM Test with ETL questions?

Yes, recruiters can request a custom test that includes both Teradata MDM and ETL questions. To know more about our ETL assessment, you can check the ETL Assessment Test .

How to use the Teradata MDM Test in my hiring process?

Utilize the Teradata MDM Test as part of your initial candidate screening. Add the test link to your job post or invite candidates via email. This helps you identify skilled candidates early in the recruitment process.

Can I test both Teradata and general Data Modeling skills together?

Yes, you can combine multiple skills in a single test. For a focused assessment on data modeling, you can refer to our Data Modeling Skills Test .

What are the main Data Management tests?

The primary data management tests include: Informatica MDM Test SAP MDM and MDG Test Data Modeling Skills Test Data Warehousing Online Test

Teradata MDM Online Test

Q: What skills are assessed in the Teradata MDM Test?

The Teradata MDM Test assesses skills such as data management, data governance, master data management, data integration, data quality, data modeling, and data warehousing. It includes MCQs on ETL (5 questions) and Teradata MDM (10 questions).

The Teradata MDM Online Test evaluates a candidate's knowledge and skills in Teradata Master Data Management (MDM). It assesses understanding of data management, data governance, data integration, data quality, data modeling, data warehousing, data analytics, and data security.

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Screen candidates with a 35 mins test

Test duration: ~ 35 mins

Difficulty level: Moderate

Availability: Available as custom test

Questions:

5 ETL MCQs
10 Teradata MDM MCQs

Covered skills:

Teradata MDM

Data Management

Data Governance

Master Data Management

Data Integration

Data Quality

Data Modeling

Data Warehousing

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

Use the Teradata MDM Test to shortlist qualified candidates

The Teradata MDM 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 understand and implement Teradata MDM principles and best practices
Ability to design and implement data governance policies and procedures
Ability to integrate data from various sources into a Teradata MDM system
Ability to ensure data quality and accuracy in a Teradata MDM environment
Ability to model and structure data in a Teradata MDM solution
Ability to develop and maintain data warehousing solutions using Teradata MDM
Ability to troubleshoot and resolve issues related to Teradata MDM implementation
Ability to collaborate with stakeholders to define and prioritize master data requirements
Ability to manage and maintain master data within a Teradata MDM system
Ability to perform data analysis and identify data patterns and trends in a Teradata MDM environment

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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 Teradata MDM Test will be non-googleable.

🧐 Question
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
	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
	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 Teradata MDM Hiring Test topics

Teradata MDM: Teradata MDM (Master Data Management) refers to the processes, governance, and tools used to ensure that an organization's critical data is complete, accurate, and up-to-date across various systems and applications. This skill is measured in the test to assess a candidate's understanding of how to implement and manage Teradata MDM solutions that provide a consolidated, reliable, and single view of master data.

Data Management: Data Management involves the processes, policies, and tools used to process, store, and protect data throughout its lifecycle. This skill is measured in the test to evaluate a candidate's ability to effectively manage and govern data assets, ensuring data quality, security, and compliance with industry standards and regulatory requirements.

Data Governance: Data Governance refers to the overall management and control of an organization's data assets, including data policies, standards, and processes. This skill is measured in the test to assess a candidate's knowledge of data governance frameworks, best practices, and their ability to establish and enforce data governance policies to ensure data quality, integrity, and consistency.

Master Data Management: Master Data Management (MDM) is a comprehensive approach to identify, define, and manage an organization's most critical data entities, such as customers, products, and suppliers, to ensure consistency and accuracy across multiple systems and applications. This skill is measured in the test to evaluate a candidate's understanding of MDM concepts, methodologies, and technologies, and their ability to design and implement effective MDM solutions.

Data Integration: Data Integration involves combining data from different sources and formats into a unified view for analysis, reporting, and decision making. This skill is measured in the test to assess a candidate's knowledge of data integration techniques, tools, and technologies, and their ability to design and implement data integration solutions that enable seamless data flow and interoperability between various systems.

Data Quality: Data Quality refers to the accuracy, consistency, completeness, and reliability of data. This skill is measured in the test to evaluate a candidate's understanding of data quality assessment, data profiling, and data cleansing techniques, as well as their ability to implement data quality frameworks and processes to ensure high-quality data for reliable decision making and operational efficiency.

Data Modeling: Data Modeling is the process of creating a conceptual or logical representation of data structures, entities, and relationships for an organization's data assets. This skill is measured in the test to assess a candidate's ability to design and develop data models that meet the business requirements, ensure data integrity, and facilitate efficient data storage, retrieval, and manipulation.

Data Warehousing: Data Warehousing involves the collection, storage, and management of large volumes of data from various sources for analysis and reporting purposes. This skill is measured in the test to evaluate a candidate's understanding of data warehousing concepts, architecture, and design principles, as well as their ability to develop and implement data warehousing solutions that enable efficient data storage, retrieval, and analysis for improved decision making.

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