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About the test:

Data mining-test evaluerer kandidater om deres viden om dataminingsteknikker, datapræbehandling, foreningsregelminedrift, klassificering, klynger og datavisualisering ved hjælp af scenariebaserede MCQ'er. Ud over disse nøglefærdigheder vurderer testen også en kandidats forståelse af datalagring, datarensning og big datateknologier.

Covered skills:

  • Databehandling
  • Dataforarbejdning
  • Datarensning
  • Data miningproces
  • Datalager og OLAP -teknologi
  • Minedrift hyppige mønstre
  • Data reduktion
  • Dataintegration og transformation

9 reasons why
9 reasons why

Adaface Data Mining Assessment Test is the most accurate way to shortlist Dataforskers



Reason #1

Tests for on-the-job skills

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:

  • Evne til at udtrække meningsfuld indsigt fra store datasæt
  • Færdighed i datamodelleringsteknikker
  • Forståelse af ETL (uddrag, transformation, belastning) processer
  • Kendskab til databehandling og analyse
  • Fortrolighed med datavarehus og OLAP -teknologi
  • Evne til at forbehandle data til minedrift
  • Erfaring med minedrift hyppige mønstre i datasæt
  • Evne til at rengøre og reducere dataløj
  • Forståelse af dataindvindingsprocessen
  • Kompetence i dataintegration og transformation
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
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 kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Data mining -test vil være ikke-gåbart.

🧐 Question

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity
Solve
You are designing a data model for a healthcare system with the following requirements:
 image
A: A separate table for each entity with foreign keys as specified, and a DoctorPatient table linking Doctors to Patients.
B: A separate table for each entity with foreign keys as specified, without additional tables.
C: A combined PatientDoctor table replacing Patient and Doctor, and separate tables for Appointment and Prescription.
D: A separate table for each entity with foreign keys, and a PatientPrescription table to track prescriptions directly linked to patients.
E: A single table combining Patient, Doctor, Appointment, and Prescription into one.
F: A separate table for each entity with foreign keys as specified, and an AppointmentDetails table linking Appointments to Prescriptions.

Hard

ER Diagram and minimum tables
ER Diagram
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?
 image
 image
 image

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
 image
Based on the ER diagram, which of the following statements is accurate and requires specific knowledge of the ER diagram's details?
A: A Student can major in multiple Departments.
B: An Instructor can belong to multiple Departments.
C: A Course can be offered by multiple Departments.
D: Enrollment records can link a Student to multiple Courses in a single semester.
E: Each Course must be associated with an Enrollment record.
F: A Department can offer courses without having any instructors.

Medium

Data Merging
Data Merging
Conditional Logic
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:
 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
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:
 image
What specific function does this SQL code perform in the context of the ETL process, and how does it contribute to the reporting goal?
A: The code calculates the total and average sales amount for each product annually.
B: It aggregates sales data by month and product, computing total and average sales amounts.
C: This query generates a daily breakdown of sales, both total and average, for each product.
D: The code is designed to identify the best-selling products on a monthly basis by sales amount.
E: It calculates the overall sales and average price per product, without considering the time dimension.

Medium

Trade Index
Index
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:
 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

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity

2 mins

Data Modeling
Solve

Hard

ER Diagram and minimum tables
ER Diagram

2 mins

Data Modeling
Solve

Medium

Normalization Process
Normalization
Database Design
Anomaly Elimination

3 mins

Data Modeling
Solve

Medium

University Courses
ER Diagrams
Complex Relationships
Integrity Constraints

2 mins

Data Modeling
Solve

Medium

Data Merging
Data Merging
Conditional Logic

2 mins

ETL
Solve

Medium

Data Updates
Staging
Data Warehouse

2 mins

ETL
Solve

Medium

SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions

3 mins

ETL
Solve

Medium

Trade Index
Index

3 mins

ETL
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Healthcare System
Data Integrity
Normalization
Referential Integrity
Data Modeling
Easy2 mins
Solve
ER Diagram and minimum tables
ER Diagram
Data Modeling
Hard2 mins
Solve
Normalization Process
Normalization
Database Design
Anomaly Elimination
Data Modeling
Medium3 mins
Solve
University Courses
ER Diagrams
Complex Relationships
Integrity Constraints
Data Modeling
Medium2 mins
Solve
Data Merging
Data Merging
Conditional Logic
ETL
Medium2 mins
Solve
Data Updates
Staging
Data Warehouse
ETL
Medium2 mins
Solve
SQL in ETL Process
SQL Code Interpretation
Data Transformation
SQL Functions
ETL
Medium3 mins
Solve
Trade Index
Index
ETL
Medium3 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

Med Adaface var vi i stand til at optimere vores indledende screeningsproces med op til 75%, hvilket frigør dyrebar tid for både ansættelsesledere og vores talentindsamlingsteam!


Brandon Lee, Leder af mennesker, Love, Bonito

Reason #5

Designed for elimination, not selection

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

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 Mining Online Test

Why you should use Pre-employment Data Mining Test?

The Data mining -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:

  • Databehandling og manipulationsteknikker
  • Kendskab til datalagring og OLAP -teknologi
  • Forståelse af det grundlæggende og koncepter med datamining
  • Dataforarbejdningsteknikker og metoder
  • Evne til at udnytte hyppige mønstre i store datasæt
  • Rengøring og håndtering af beskidte data
  • Data reduktionsteknikker til effektiv minedrift
  • Forståelse og efter dataindvindingsprocessen
  • Dataintegration og transformationsevner
  • Evne til at fortolke og analysere minedriftresultater

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 Mining Test?

  • minedrift Hyppige mønstre

    Minedrift Hyppige mønstre fokuserer på at opdage tilbagevenden Itemsets eller sekvenser i et datasæt. Det involverer teknikker som markedskurvanalyse og foreningsregelminedrift. Denne færdighed skal måles i testen for at vurdere en kandidats færdigheder i at identificere fælles mønstre, som kan være værdifulde til forskellige applikationer, såsom anbefalingssystemer og markedsanalyse.

  • Datarensning </H4> <p> Data Rengøring er processen med at identificere og korrigere eller fjerne fejl, uoverensstemmelser og outliers i datasættet. Det inkluderer opgaver som at håndtere duplikatregistre, løse uoverensstemmelser og håndtere støjende eller irrelevante data. Måling af denne færdighed i testen hjælper med at evaluere en kandidats evne til at sikre dataintegritet og pålidelighed, hvilket er afgørende for nøjagtige minedriftresultater. </p> <h4> datareduktion

    Datareduktion involverer teknikker til at reducere størrelsen og dimensionaliteten af ​​datasættet uden væsentligt at miste relevant information. Det sigter mod at fjerne overflødige eller irrelevante funktioner og omdanne dataene til en mere kompakt repræsentation. Måling af denne færdighed i testen hjælper med at evaluere en kandidats evne til at optimere dataminingprocessen ved at reducere beregningskompleksitet og forbedre effektiviteten.

  • Data Mining Process

    Data miningproces omfatter de involverede systematiske trin. Ved at udtrække meningsfulde mønstre og indsigt fra data. Det inkluderer opgaver som dataudforskning, modeludvælgelse, mønstervaluering og resultatfortolkning. Måling af denne færdighed i testen hjælper med at evaluere en kandidats forståelse af den samlede arbejdsgang og deres evne til at anvende passende teknikker på hvert trin.

  • dataintegration og transformation

    Dataintegration og transformation Involver konsolidering af data fra forskellige kilder, løsning af datakonflikter og omdannelse af data til et samlet format til analyse. Det kræver viden om dataintegrationsteknikker, datakortlægning og datatransformationsoperationer. Måling af denne færdighed i testen hjælper med at evaluere en kandidats evne til effektivt at integrere og transformere forskellige datakilder, hvilket sikrer konsistens og nøjagtighed i minedriftprocessen.

  • 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 mining -test to be based on.

    Databehandling
    Data varehus
    OLAP -teknologi
    Dataforarbejdning
    Minedrift hyppige mønstre
    Datarensning
    Data reduktion
    Data miningproces
    Dataintegration
    Datatransformation
    Dataekstraktion
    Dataindlæsning
    Datamodellering
    Dataanalyse
    Overvåget læring
    Uovervåget læring
    Foreningsregler
    Beslutningstræer
    Klynger
    Klassifikation
    Datavisualisering
    Dataudforskning
    Big data
    Forudsigelig modellering
    Mønster genkendelse
    Tekst minedrift
    Webminedrift
    Social netværksanalyse
    Funktionsvalg
    Dimensionalitetsreduktion
    Outlier -detektion
    Data -imputation
    Naive Bayes
    Understøtt vektormaskiner
    Neurale netværk
    Genetiske algoritmer
    Regressions analyse
    Tidsserieanalyse
    Rumlig data mining
    Databeskyttelse
    Etik inden for data mining
    Market Basket Analysis
    Foreningsregelminedrift
    Sekventiel mønster mining
    Anomali -detektion
    Modelevaluering
    Overfitting
    Ensemble -metoder
    Krydsvalidering
    Prøveudtagning af data
    Datafusion
    Parallel og distribueret data mining
    Data skalerbarhed
    Datakvalitetsvurdering
    Dataprofilering
    Funktionsteknik
    Data Wrangling

What roles can I use the Data Mining Test for?

  • Dataforsker
  • Business analytiker
  • Dataanalytiker
  • Dataingeniør
  • Database Administrator
  • Forsker

How is the Data Mining 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

  • Færdighed i statistisk analyse
  • Evne til at implementere forskellige dataminingalgoritmer
  • Kendskab til overvågede og uovervågede læringsteknikker
  • Erfaring med beslutningstræalgoritmer
  • Forståelse af foreningsregelminedrift
  • Ekspertise i klyngeteknikker
  • Erfaring med klassificerings- og regressionsmodeller
  • Færdighed i håndtering af store datasæt
  • Fortrolighed med Big Data Technologies
  • Ekspertise inden for datavisualisering og rapportering
Singapore government logo

De ansættelsesledere mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, kunne fortælle, hvilke kandidater der havde bedre scoringer, og differentierede med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af ​​kandidater, der er på listen med Adaface -screeningen.


85%
Reduktion i screeningstid

Data Mining Hiring Test FAQS

Kan jeg kombinere flere færdigheder i en brugerdefineret vurdering?

Ja absolut. Brugerdefinerede vurderinger er oprettet baseret på din jobbeskrivelse og vil omfatte spørgsmål om alle must-have-færdigheder, du angiver.

Har du nogen anti-cheating eller proctoring-funktioner på plads?

Vi har følgende anti-cheating-funktioner på plads:

  • Ikke-gåbare spørgsmål
  • IP Proctoring
  • Webproctoring
  • Webcam Proctoring
  • Detektion af plagiering
  • Sikker browser

Læs mere om Proctoring Features.

Hvordan fortolker jeg testresultater?

Den primære ting at huske på er, at en vurdering er et elimineringsværktøj, ikke et udvælgelsesværktøj. En færdighedsvurdering er optimeret for at hjælpe dig med at eliminere kandidater, der ikke er teknisk kvalificerede til rollen, den er ikke optimeret til at hjælpe dig med at finde den bedste kandidat til rollen. Så den ideelle måde at bruge en vurdering på er at beslutte en tærskelværdi (typisk 55%, vi hjælper dig med benchmark) og inviterer alle kandidater, der scorer over tærsklen for de næste interviewrunder.

Hvilken oplevelsesniveau kan jeg bruge denne test til?

Hver Adaface -vurdering tilpasses til din jobbeskrivelse/ ideel kandidatperson (vores emneeksperter vælger de rigtige spørgsmål til din vurdering fra vores bibliotek på 10000+ spørgsmål). Denne vurdering kan tilpasses til ethvert erfaringsniveau.

Får hver kandidat de samme spørgsmål?

Ja, det gør det meget lettere for dig at sammenligne kandidater. Valgmuligheder for MCQ -spørgsmål og rækkefølgen af ​​spørgsmål randomiseres. Vi har anti-cheating/proctoring funktioner på plads. I vores virksomhedsplan har vi også muligheden for at oprette flere versioner af den samme vurdering med spørgsmål om lignende vanskelighedsniveauer.

Jeg er kandidat. Kan jeg prøve en øvelsestest?

Nej. Desværre understøtter vi ikke praksisforsøg i øjeblikket. Du kan dog bruge vores eksempler på spørgsmål til praksis.

Hvad er omkostningerne ved at bruge denne test?

Du kan tjekke vores prisplaner.

Kan jeg få en gratis prøve?

Ja, du kan tilmelde dig gratis og forhåndsvise denne test.

Jeg flyttede lige til en betalt plan. Hvordan kan jeg anmode om en brugerdefineret vurdering?

Her er en hurtig guide til hvordan man anmoder om en brugerdefineret vurdering på adaface.

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No trick questions.
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