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About the test:

Data Warehouse Online-testet använder scenariebaserade flervalsfrågor för att utvärdera kandidater på sin expertis inom datalager, som innebär att utforma, bygga och underhålla lager, databaser och datamart.

Covered skills:

  • SQL Basics
  • SQL Subqueries och går med
  • ER -diagram
  • Faktabord och normalisering
  • SQL CRUD -frågor
  • ETL -grunderna
  • Datamodellering
  • Grundläggande datalagring

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9 reasons why
9 reasons why

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



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:

  • Möjlighet att skriva SQL -frågor för att manipulera och hämta data från databaser
  • Förståelse av datalagerkoncept och principer
  • Kunskap om ETL (Extract, Transform, Load) -processer
  • Kunskaper i att skapa och optimera ER -diagram
  • Förmåga att designa och implementera datamodeller
  • Bekanta med fakta tabeller och databasnormalisering
  • Förståelse av datalagring
  • Förmåga att analysera och tolka data
  • Färdigheter i att utföra CRUD (skapa, läsa, uppdatera, ta bort) operationer med SQL
  • Kompetens i att använda underkunskaper och sammanfogningar 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

Dessa är bara ett litet urval från vårt bibliotek med 10 000+ frågor. De faktiska frågorna om detta Datalager online -test kommer att vara icke-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

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:
 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
🧐 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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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
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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
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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Data Merging
Data Merging
Conditional Logic
ETL
Medium2 mins
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Data Updates
Staging
Data Warehouse
ETL
Medium2 mins
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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 kunde vi optimera vår initiala screeningprocess med uppemot 75 %, vilket frigjorde dyrbar tid för både anställande chefer och vårt team för att förvärva talang!


Brandon Lee, Chef för människor, Love, Bonito

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Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment Datalager 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 Datalager 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

Visa exempelskort
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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 Datalager 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 -frågor
  • SQL Subqueries och går med
  • ETL -grunderna
  • ER -diagram
  • Datamodellering
  • Faktabord och normalisering
  • Grundläggande datalagring
  • Hantera undantag och fel
  • Optimera SQL -frågor för prestanda

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 CRUD -frågor

    SQL CRUD -frågor. Involvera skapa, läsa, uppdatera och ta bort operationer i en databas. This skill should be measured in the test to evaluate a candidate's proficiency in performing these essential database operations using SQL.

  • SQL Subqueries and Joins

    SQL subqueries and joins are advanced techniques used to combine Data från flera tabeller och hämtar specifik information från en databas. Denna färdighet bör mätas i testet för att bedöma en kandidats förmåga att optimera komplexa SQL -frågor och hämta data effektivt.

  • ETL Fundamentals

    ETL Fundamentals hänvisar till principerna och teknikerna som är involverade i extrahering , Omvandla och ladda data från olika källor till ett datalager. Denna färdighet bör mätas i testet för att utvärdera en kandidats förståelse av ETL -processer, dataintegration och deras förmåga att arbeta med stora datasätt.

  • er -diagram

    erdiagram eller enhet -Relationship -diagram, är visuella representationer av ett databasschema som illustrerar enheterna, attributen och förhållandena mellan dem. Denna färdighet bör mätas i testet för att bedöma en kandidats förmåga att analysera och designa databasstrukturer med hjälp av ER -diagram.

  • datamodellering

    datamodellering innebär att utforma och definiera struktur, begränsningar, begränsningar, begränsningar, och förhållanden mellan en databas. Denna färdighet bör mätas i testet för att utvärdera en kandidats kunskaper i konceptualisering, planering och implementering av databasmodeller baserade på kraven i en organisation.

  • fakta tabeller och normalisering

    faktatabeller och normalisering är tekniker som används i databasdesign för att eliminera dataredundans och säkerställa dataintegritet. Denna färdighet bör mätas i testet för att bedöma en kandidats förståelse av de olika nivåerna av databasnormalisering och deras förmåga att utforma effektiva och skalbara databasscheman.

  • Datalager Fundamentals

    Datalagring Grundläggande omfattar koncept, arkitektur och processer som är involverade i att bygga och hantera datalager. Denna färdighet bör mätas i testet för att utvärdera en kandidats kunskap om datalagringsprinciper, inklusive datauttag, transformation, lastning och 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 Datalager online -test to be based on.

    SQL Basics
    Skapa bord
    Välj uttalande
    Infoga uttalande
    Uppdateringsuttalande
    Radera uttalande
    SQL går med
    Inre koppling
    Yttre sammanfogning
    Tvärfogning
    Självförfylla
    Underkunskaper
    Korrelerade underkroppar
    Skala undervillkor
    Vanliga tabelluttryck
    SQL -aggregat
    Grupp av
    Med klausul
    Distinkt nyckelord
    SQL -funktioner
    Strängmanipulation
    Datum- och tidsfunktioner
    Matematiska funktioner
    Falldeklaration
    Växa samman
    Noll
    SQL -begränsningar
    Primärnyckel
    Främmande nyckel
    Unik begränsning
    Inte nollbegränsning
    Kontrollera begränsningen
    Indexering
    Datalagringskoncept
    Stjärnsystem
    Snöflingans schema
    Dimensionell modellering
    Långsamt förändrade dimensioner
    Datamart
    Datakuber
    ETL -process
    Extrahera
    Omvandla
    Ladda
    Dataintegration
    Datakvalitet
    Dataprofilering
    Datarensning
    ER -diagram
    Entitet
    Relation
    Attribut
    Kardinalitet
    Normalisering
    Första normala formen
    Andra normala form
    Tredje normalform
    Bcnf
    Faktabord
    Dimensionstabeller
    Surrognycklar
    Livscykel
    Datalagerarkitektur
    ETL -verktyg och tekniker
    Datavisualisering
    Affärsintelligens
    OLAP (online analytisk bearbetning)
    Datalager säkerhet
    Dataledning
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What roles can I use the Data Warehouse Online Test for?

  • Datalagerutvecklare
  • Senior Data Warehouse Developer
  • Datalagerexpert
  • ETL -utvecklare
  • Datalagel

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

  • Implementera datasäkerhetsåtgärder i SQL
  • Designa och bygga ETL -arbetsflöden
  • Extrahera data från olika datakällor
  • Omvandla och rengöring av data för analys
  • Ladda data i ett datalager
  • Förstå och skapa ER -diagram
  • Normalisera och denormalisera data
  • Skapa och hantera faktabord
  • Implementering av dataintegritetsbegränsningar
  • Använda datalagerverktyg och ramverk
Singapore government logo

De rekryterande cheferna upplevde att de genom de tekniska frågorna som de ställde under panelintervjuerna kunde berätta vilka kandidater som hade bättre poäng och särskiljde sig med de som inte fick lika bra poäng. Dom är mycket nöjd med kvaliteten på de kandidater som nominerades med Adaface-screeningen.


85%
minskning av screeningstiden

Data Warehouse Hiring Test Vanliga frågor

Kan jag kombinera flera färdigheter till en anpassad bedömning?

Ja absolut. Anpassade bedömningar ställs in baserat på din arbetsbeskrivning och kommer att innehålla frågor om alla måste-ha färdigheter du anger.

Har du några anti-cheating eller proctoring-funktioner på plats?

Vi har följande anti-cheating-funktioner på plats:

  • Icke-Googleable-frågor
  • IP -proctoring
  • webbproctoring
  • webbkamera proctoring
  • Detektion av plagiering
  • säker webbläsare

Läs mer om proctoring -funktionerna.

Hur tolkar jag testresultat?

Det främsta att tänka på är att en bedömning är ett eliminationsverktyg, inte ett urvalsverktyg. En kompetensbedömning är optimerad för att hjälpa dig att eliminera kandidater som inte är tekniskt kvalificerade för rollen, den är inte optimerad för att hjälpa dig hitta den bästa kandidaten för rollen. Så det ideala sättet att använda en bedömning är att bestämma en tröskelpoäng (vanligtvis 55%, vi hjälper dig att jämföra) och bjuda in alla kandidater som gör poäng över tröskeln för nästa intervjurundor.

Vilken erfarenhetsnivå kan jag använda detta test för?

Varje AdaFace -bedömning anpassas till din arbetsbeskrivning/ idealisk kandidatperson (våra ämnesexperter kommer att välja rätt frågor för din bedömning från vårt bibliotek med 10000+ frågor). Denna bedömning kan anpassas för alla erfarenhetsnivåer.

Får varje kandidat samma frågor?

Ja, det gör det mycket lättare för dig att jämföra kandidater. Alternativ för MCQ -frågor och ordningen på frågor randomiseras. Vi har anti-cheating/proctoring -funktioner på plats. I vår företagsplan har vi också möjlighet att skapa flera versioner av samma bedömning med frågor om liknande svårighetsnivåer.

Jag är kandidat. Kan jag prova ett träningstest?

Nej. Tyvärr stöder vi inte övningstester just nu. Du kan dock använda våra exempelfrågor för övning.

Vad är kostnaden för att använda detta test?

Du kan kolla in våra prisplaner.

Kan jag få en gratis provperiod?

Plattformen är helt självbetjänande, så här är ett sätt att gå vidare:

  • Du kan registrera dig gratis för att få en känsla för hur det fungerar.
  • Den kostnadsfria provperioden inkluderar en provbedömning (Java/JavaScript) som du hittar i din instrumentpanel när du registrerar dig. Du kan använda den för att granska kvaliteten på frågorna och kandidaternas upplevelse av ett konversationstest på Adaface.
  • För att granska kvaliteten på frågorna kan du också granska våra offentliga frågor för 50+ färdigheter här.
  • När du är övertygad om att du vill testa det med riktiga bedömningar och kandidater kan du välja en plan enligt dina krav.

Jag flyttade precis till en betald plan. Hur kan jag begära en anpassad bedömning?

Här är en snabbguide om hur man begär en anpassad bedömning på Adaface.

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