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About the test:

Data Warehouse Online-testen bruker scenariabaserte flervalgsspørsmål for å evaluere kandidater på sin ekspertise innen datavarehus, som innebærer å designe, bygge og vedlikeholde lager, databaser og datamarter.

Covered skills:

  • SQL Basics
  • SQL Subqueries og blir med
  • ER -diagrammer
  • Faktabeller og normalisering
  • SQL CRUD -spørsmål
  • ETL Fundamentals
  • Datamodellering
  • Datavarehusfundament

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9 reasons why
9 reasons why

Adaface Data Warehouse Test is the most accurate way to shortlist Datavarehusutviklers



Reason #1

Tests for on-the-job skills

The Data Warehouse 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:

  • Evne til å skrive SQL -spørsmål for å manipulere og hente data fra databaser
  • Forståelse av datavarehusbegreper og prinsipper
  • Kunnskap om ETL (Extract, Transform, Load) -prosesser
  • Kompetanse i å lage og optimalisere ER -diagrammer
  • Evne til å designe og implementere datamodeller
  • Kjennskap til fakta tabeller og databasens normalisering
  • Forståelse av datavarehusfundamentals
  • Evne til å analysere og tolke data
  • Ferdigheter i å utføre CRUD (opprette, lese, oppdatere, slette) operasjoner ved hjelp av SQL
  • Kompetanse i å bruke subqueries og blir med i SQL
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

Dette er bare en liten prøve fra biblioteket vårt med 10.000+ spørsmål. De faktiske spørsmålene om dette Data Warehouse Online Test vil være ikke-googlable.

🧐 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

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.

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:
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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:
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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

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
Try practice test

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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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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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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🧐 Question🔧 Skill💪 Difficulty⌛ Time
Multi Select
JOIN
GROUP BY
SQL
Medium2 mins
Try practice test
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
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
Try practice test
Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning
Data Warehouse
Medium2 mins
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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
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Trade Index
Index
ETL
Medium3 mins
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Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

Med Adaface var vi i stand til å optimalisere den første screeningsprosessen vår med opp mot 75 %, og frigjorde dyrebar tid for både ansettelsesledere og vårt talentanskaffelsesteam!


Brandon Lee, Leder for mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

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

Vis eksempler på 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


Learn more

About the Data Warehouse Assessment Test

Why you should use Pre-employment Data Warehouse Online Test?

The Data Warehouse 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:

  • SQL Basics
  • SQL CRUD -spørsmål
  • SQL Subqueries og blir med
  • ETL Fundamentals
  • ER -diagrammer
  • Datamodellering
  • Faktabeller og normalisering
  • Datavarehusfundament
  • Håndtering av database unntak og feil
  • Optimalisering av SQL -spørsmål for ytelse

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 Warehouse Online Test?

  • SQL Basics

    SQL Basics refererer til den grunnleggende kunnskapen om strukturert spørringsspråk, som brukes til å kommunisere med og manipulere relasjonsdatabaser. Denne ferdigheten bør måles i testen for å vurdere en kandidats forståelse av SQL -syntaks, databasedesignprinsipper og deres evne til å skrive grunnleggende SQL -spørsmål.

  • SQL CRUD -spørsmål

    SQL Crud Queries Involver opprette, les, oppdater og slett operasjoner i en database. Denne ferdigheten bør måles i testen for å evaluere en kandidats ferdighet i Data fra flere tabeller og hente spesifikk informasjon fra en database. Denne ferdigheten bør måles i testen for å vurdere en kandidats evne til å optimalisere komplekse SQL -spørsmål og hente data effektivt.

  • ETL -grunnleggende

    ETL -grunnleggende refererer til prinsippene og teknikkene som er involvert i å trekke ut , Transformere og laste inn data fra forskjellige kilder til et datavarehus. Denne ferdigheten bør måles i testen for å evaluere en kandidats forståelse av ETL -prosesser, dataintegrasjon og deres evne til å jobbe med store datasett.

  • er diagrammer

    er diagrammer, eller enhet -Relationship Diagrams, er visuelle representasjoner av et databaseskjema som illustrerer enhetene, attributtene og forholdene mellom dem. Denne ferdigheten bør måles i testen for å vurdere en kandidats evne til å analysere og designe databasestrukturer ved bruk av ER -diagrammer.

  • Datamodellering

    Datamodellering innebærer å designe og definere strukturen, begrensningene, og forhold til en database. Denne ferdigheten bør måles i testen for å evaluere en kandidats ferdighet i å konseptualisere, planlegge og implementere databasemodeller basert på kravene til en organisasjon.

  • Faktabeller og normalisering

    Faktabeller Og normalisering er teknikker som brukes i databasedesign for å eliminere dataredundans og sikre dataintegritet. Denne ferdigheten bør måles i testen for å vurdere en kandidats forståelse av de forskjellige nivåene av databasens normalisering og deres evne til å designe effektive og skalerbare databaseskjema Grunnleggende omfatter konseptene, arkitekturen og prosessene som er involvert i å bygge og administrere datavarehus. Denne ferdigheten bør måles i testen for å evaluere en kandidats kunnskap om datavarehusprinsipper, inkludert datautvinning, transformasjon, lasting og rapportering.

  • 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 Warehouse Online Test to be based on.

    SQL Basics
    Lag tabell
    Velg uttalelse
    Sett inn uttalelse
    Oppdateringserklæring
    Slett uttalelse
    SQL blir med
    Indre sammenføyning
    Ytre sammenføyning
    Kryss med
    Selv bli med
    Underlag
    Korrelerte subqueries
    SKALAR SUBQUERIES
    Vanlige tabelluttrykk
    SQL Aggregates
    Gruppe av
    Har klausul
    Distinkt nøkkelord
    SQL -funksjoner
    Strengmanipulering
    Dato og tidsfunksjoner
    Matematiske funksjoner
    Saksuttalelse
    Samles
    Nullif
    SQL -begrensninger
    Primærnøkkel
    Utenlandsk nøkkel
    Unik begrensning
    Ikke nullbegrensning
    Sjekk begrensning
    Indeksering
    Datalagerkonsepter
    Stjernerskjema
    Snøfnuggskjema
    Dimensjonell modellering
    Sakte skiftende dimensjoner
    Data MARTS
    Datakubber
    ETL -prosess
    Ekstrakt
    Forvandle
    Laste
    Dataintegrasjon
    Datakvalitet
    Dataprofilering
    Data rensing
    ER -diagrammer
    Enhet
    Forhold
    Egenskap
    Kardinalitet
    Normalisering
    Første normale form
    Andre normal form
    Tredje normal form
    Bcnf
    Faktabeller
    Dimensjonstabeller
    Surrogatnøkler
    Data lager livssyklus
    Datavarehusarkitektur
    ETL -verktøy og teknikker
    Datavisualisering
    Forretningsintelligens
    OLAP (online analytisk prosessering)
    Datavarehussikkerhet
    Datastyring
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What roles can I use the Data Warehouse Online Test for?

  • Datavarehusutvikler
  • Senior Data Warehouse Developer
  • Datavarehusekspert
  • ETL -utvikler
  • Data Engineer-Data Warehouse

How is the Data Warehouse 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

  • Implementering av datasikkerhetstiltak i SQL
  • Designe og bygge ETL -arbeidsflyter
  • Å trekke ut data fra forskjellige datakilder
  • Transformasjons- og rengjøringsdata for analyse
  • Laster inn data i et datavarehus
  • Forstå og lage ER -diagrammer
  • Normalisere og denormalisere data
  • Opprette og administrere faktabord
  • Implementering av dataintegritetsbegrensninger
  • Bruke datavarehusverktøy og rammer
Singapore government logo

Ansettelseslederne mente at de gjennom de tekniske spørsmålene de stilte under panelintervjuene, var i stand til å fortelle hvilke kandidater som scoret bedre, og differensierte med de som ikke skåret like godt. De er svært fornøyd med kvaliteten på kandidatene som er på listen med Adaface-screeningen.


85%
Reduksjon i screeningstid

Data Warehouse Hiring Test Vanlige spørsmål

Kan jeg kombinere flere ferdigheter til en tilpasset vurdering?

Ja absolutt. Tilpassede vurderinger er satt opp basert på stillingsbeskrivelsen din, og vil inneholde spørsmål om alle må-ha ferdigheter du spesifiserer.

Har du noen anti-juksende eller proktoreringsfunksjoner på plass?

Vi har følgende anti-juksede funksjoner på plass:

  • Ikke-googlable spørsmål
  • IP Proctoring
  • Nettproctoring
  • Webcam Proctoring
  • Deteksjon av plagiering
  • Sikker nettleser

Les mer om Proctoring -funksjonene.

Hvordan tolker jeg testresultater?

Den viktigste tingen å huske på er at en vurdering er et eliminasjonsverktøy, ikke et seleksjonsverktøy. En ferdighetsvurdering er optimalisert for å hjelpe deg med å eliminere kandidater som ikke er teknisk kvalifisert for rollen, det er ikke optimalisert for å hjelpe deg med å finne den beste kandidaten for rollen. Så den ideelle måten å bruke en vurdering på er å bestemme en terskelpoeng (vanligvis 55%, vi hjelper deg med å benchmark) og invitere alle kandidater som scorer over terskelen for de neste rundene med intervjuet.

Hvilken opplevelsesnivå kan jeg bruke denne testen til?

Hver ADAFACE -vurdering er tilpasset din stillingsbeskrivelse/ ideell kandidatperson (våre fageksperter vil velge de riktige spørsmålene for din vurdering fra vårt bibliotek med 10000+ spørsmål). Denne vurderingen kan tilpasses for ethvert opplevelsesnivå.

Får hver kandidat de samme spørsmålene?

Ja, det gjør det mye lettere for deg å sammenligne kandidater. Alternativer for MCQ -spørsmål og rekkefølgen på spørsmål er randomisert. Vi har anti-juksing/proctoring funksjoner på plass. I vår bedriftsplan har vi også muligheten til å lage flere versjoner av den samme vurderingen med spørsmål med lignende vanskelighetsnivåer.

Jeg er en kandidat. Kan jeg prøve en praksisprøve?

Nei. Dessverre støtter vi ikke praksisprøver for øyeblikket. Du kan imidlertid bruke eksemplet spørsmål for praksis.

Hva koster ved å bruke denne testen?

Du kan sjekke ut prisplanene våre.

Kan jeg få en gratis prøveperiode?

Ja, du kan registrere deg gratis og forhåndsvise denne testen.

Jeg flyttet nettopp til en betalt plan. Hvordan kan jeg be om en tilpasset vurdering?

Her er en rask guide om Hvordan be om en tilpasset vurdering på adaface.

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