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About the test:

ETL -vurderingen Test evaluerer en kandidats evne til å identifisere verktøy som brukes til å trekke ut dataene, slå sammen ekstraherte data logisk eller fysisk, definere transformasjoner for å gjelde for kildedata for å gjøre datakontekstuelle og skissere metoder for å laste inn data i destinasjonssystemet.

Covered skills:

  • Automatisere ETL -jobber
  • Datavarehusarkitektur
  • Datatilgangstyper
  • Stjerne- og snøfnuggskjemaer
  • ETL vs Elt
  • Datapipelinjer
  • Datavarehuslag
  • Datamodellering
  • Datatransformasjon

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9 reasons why
9 reasons why

Adaface ETL Test is the most accurate way to shortlist ETL -utviklers



Reason #1

Tests for on-the-job skills

The ETL Assessment 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:

  • I stand til å designe og automatisere ETL -jobber for å trekke ut, transformere og laste inn data effektivt
  • Dyktig til å bygge datarørledninger for å flytte og transformere data mellom systemer
  • Forståelse av datavarehusarkitektur og dens viktige komponenter
  • Kunnskap om forskjellige lag i et datavarehussystem, for eksempel rå data, iscenesettelsesområde og datamarter
  • Kjennskap til forskjellige datatilgangstyper, for eksempel batchbehandling, streaming i sanntid og trinnvis belastning
  • Kompetanse innen datamodelleringsteknikker og praksis
  • Evne til å designe stjerne- og snøfnuggskjemaer for effektiv datarrepresentasjon
  • Dyktig i datatransformasjonsteknikker for å sikre datakvalitet og konsistens
  • Forståelse av forskjellene mellom ETL- og ELT -tilnærminger i dataintegrasjon
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 ETL vurderingstest vil være ikke-googlable.

🧐 Question

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:
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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

Medium

Multi Select
JOIN
GROUP BY
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Consider the following SQL table:
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How many rows does the following SQL query return?
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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?
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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.

Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity
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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
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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
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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
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 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.
🧐 Question🔧 Skill

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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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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Easy

Healthcare System
Data Integrity
Normalization
Referential Integrity

2 mins

Data Modeling
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Hard

ER Diagram and minimum tables
ER Diagram

2 mins

Data Modeling
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Medium

Normalization Process
Normalization
Database Design
Anomaly Elimination

3 mins

Data Modeling
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Medium

University Courses
ER Diagrams
Complex Relationships
Integrity Constraints

2 mins

Data Modeling
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🧐 Question🔧 Skill💪 Difficulty⌛ Time
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
Try practice test
Trade Index
Index
ETL
Medium3 mins
Try practice test
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
Try practice test
Healthcare System
Data Integrity
Normalization
Referential Integrity
Data Modeling
Easy2 mins
Try practice test
ER Diagram and minimum tables
ER Diagram
Data Modeling
Hard2 mins
Try practice test
Normalization Process
Normalization
Database Design
Anomaly Elimination
Data Modeling
Medium3 mins
Try practice test
University Courses
ER Diagrams
Complex Relationships
Integrity Constraints
Data Modeling
Medium2 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 oppover 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 ETL vurderingstest 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 ETL vurderingstest 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 ETL Online Test

Why you should use Pre-employment ETL Assessment Test?

The ETL vurderingstest 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:

  • Automatisere ETL -jobber
  • Designe og implementere datapipelinjer
  • Forstå og optimalisere datavarehusarkitektur
  • Arbeide med forskjellige lag med et datavarehus
  • Bruke forskjellige datatilgangstyper
  • Implementering av effektive datamodelleringsteknikker
  • Skape stjerne- og snøfnuggskjemaer
  • Transforming og rensende data
  • Skille mellom ETL- og ELT -prosesser
  • Feilsøking og håndtering av unntak i ETL -arbeidsflyter

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 ETL Assessment Test?

  • Automatiser ETL -jobber

    Automatisering av ETL (Extract, Transform, Load) jobber innebærer å lage arbeidsflyter eller skript for å effektivisere prosessen med å trekke ut data fra forskjellige kilder, transformere det til et passende format og laste den inn i et målsystem. Denne ferdigheten måles i testen for å vurdere kandidatens evne til å optimalisere databelastningsprosesser, redusere manuell innsats og forbedre den generelle effektiviteten i dataintegrasjon.

  • Datapipelines

    Datapipelinjer refererer til En serie prosesser og arbeidsflyter som samler inn, transformerer og flytter data fra et system til et annet. Det innebærer å trekke ut data fra flere kilder, utføre nødvendige transformasjoner og valideringer og levere dem til et målmål. Måling av denne ferdigheten hjelper til med å evaluere kandidatens ferdigheter i å utforme effektive og skalerbare datarørledninger for å sikre jevn dataflyt og integrasjon.

  • Datavarehusarkitektur

    Datavarehusarkitektur refererer til organisasjonen og strukturen til et datavarehussystem. Det omfatter forskjellige komponenter som datakilder, datainnsamling, lagring, datamodellering og tilgangslag. Å vurdere denne ferdigheten gjør det mulig for rekrutterere å måle kandidatens kunnskap om å utforme en effektiv arkitektur som oppfyller forretningskrav, muliggjør dataanalyse og støtter effektiv datainnhenting.

  • Datalagerlag

    Datavarehus lag Representere de forskjellige nivåene av data abstraksjon i et datavarehussystem. Disse lagene inkluderer iscenesettelsesområdet, datavarehuset og presentasjonslaget. Evaluering av denne ferdigheten hjelper til med å bestemme kandidatens forståelse av hvordan data er organisert og lagres i hvert lag, og hvordan disse lagene samhandler for å muliggjøre enkel datainnhenting og analyse.

  • Datatilgangstyper

    Data Tilgangstyper refererer til de forskjellige metodene og protokollene som brukes til å hente data fra et datavarehus. Disse inkluderer OLAP (online analytisk prosessering), OLTP (online transaksjonsbehandling) og rapporteringsverktøy. Måling av denne ferdigheten hjelper til med å vurdere kandidatens kjennskap til forskjellige datatilgangsmetoder og deres evne til å velge den aktuelle metoden basert på kravene i dataanalysen eller rapporteringsoppgaver.

  • Datamodellering

    Data Modellering er prosessen med å skape en konseptuell eller logisk representasjon av strukturen, forholdene og begrensningene i en database. Det innebærer å designe tabellene, kolonnene og forholdene som definerer hvordan data lagres og organiseres. Denne ferdigheten blir evaluert i testen for å bestemme kandidatens evne til å designe effektive datamodeller som letter effektiv datainnhenting, analyse og rapportering.

  • Stjerne- og snøfnuggskjemaer

    Stjerne- og snøfnuggskjemaer er to populære datamodelleringsteknikker som brukes i datavarehus. Stjernerskjemaet organiserer data i en sentral faktabord med flere dimensjonstabeller, mens snøfnuggskjemaet utvider stjerneskjemaet ved å normalisere dimensjonstabeller ytterligere. Måling av denne ferdigheten hjelper rekrutterere med å vurdere kandidatens ferdigheter i å skape og jobbe med disse skjemadesignene, som ofte brukes i datavarehus for effektiv datalagring og analyse.

  • Datatransformasjon

    Datatransformasjon innebærer å endre eller konvertere data fra kildeformatet til et format som er egnet for målsystemet eller datavarehuset. Denne prosessen kan omfatte rengjøringsdata, aggregering, sammenslåing, splitting eller utføre beregninger på dataene. Evaluering av denne ferdigheten hjelper til med å bestemme kandidatens evne til å manipulere og transformere data nøyaktig og effektivt, og sikre integriteten og kvaliteten på data i ETL (Extract, Transform, Load) -prosessen.

  • ETL vs Elt

    ETL (Extract, Transform, Load) og ELT (Extract, Load, Transform) er to tilnærminger som brukes i dataintegrasjonsprosesser. ETL innebærer å trekke ut data fra forskjellige kilder, transformere dem og deretter laste dem inn i et målsystem. ELT derimot innebærer å laste inn rå data inn i et målsystem først og deretter utføre transformasjoner etter behov. Måling av denne ferdigheten gjør det mulig for rekrutterere å vurdere kandidatens forståelse av de viktigste forskjellene mellom ETL og ELT, så vel som deres evne til å velge og implementere passende tilnærming basert på spesifikke krav og begrensninger.

  • 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 ETL vurderingstest to be based on.

    ETL -grunnleggende
    Datautvinning
    Datatransformasjon
    Databelastning
    Datapipelinjer
    ETL -rammer
    ETL -optimalisering
    ETL -verktøy
    Datavarehuskonsepter
    Datavarehusdesign
    Datavarehusarkitektur
    Rådata
    Scene område
    Data MARTS
    Datatilgangstyper
    Batchbehandling
    Sanntids streaming
    Inkrementell belastning
    Datamodelleringsteknikker
    Enhet-forhold modellering
    Dimensjonell modellering
    Stjernerskjema
    Snøfnuggskjema
    Datatransformasjonsteknikker
    Datakartlegging
    Data rensing
    Dataintegrasjon
    Datakonsistens
    ETL vs Elt
    Datavarehusytelse
    Datavarehussikkerhet
    Datavarehusverktøy
    ETL -testing
    ETL -dokumentasjon
    Endre datafangst
    Dataintegrasjonsmønstre
    ETL beste praksis
    Dataprofilering
    ETL feilhåndtering
    Metadata Management
    Parallell prosessering
    Datakvalitetsstyring
    ETL -overvåking
    Data avstamning
    ETL -ytelse
    Datavarehusskjemaer
    Master Data Management
    Sakte skiftende dimensjoner
    Data Mart Design
    Fakta- og dimensjonstabeller
    Datavarehusstyring
    ELT -verktøy og teknikker
    Datamodelleringsverktøy
    Datavarehus i skyen
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What roles can I use the ETL Assessment Test for?

  • ETL -utvikler
  • ETL -analytiker
  • Senior ETL -utvikler
  • ETL -bly
  • Senioringeniør (ETL)
  • Data Stage Developer
  • Informatica ETL -utvikler
  • Dataingeniør - ETL
  • BI -utvikler

How is the ETL Assessment 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

  • Utvikling og vedlikehold av datavarehusdokumentasjon
  • Optimalisering av ETL -ytelse og skalerbarhet
  • Implementere datainntak og replikasjonsteknikker
  • Forstå og anvende ekstrakt-transform-belastningsteknikker
  • Utføre dataprofilering og kvalitetssikring
  • Implementering av dimensjonell modellering for datavarehus
  • Å bygge og vedlikeholde dataintegrasjonsrørledninger
  • Designe effektive datatransformasjonsprosesser
  • Arbeide med datavisualiseringsverktøy og teknikker
  • Implementering av endring av datafangst og dataintegrasjon i sanntid
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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

ETL Hiring Test Vanlige spørsmål

Kan jeg evaluere andre relevante ferdigheter som SQL i samme test?

Ja. Vi støtter screening av flere ferdigheter i en enkelt test. Du kan gjennomgå Standard SQL Test for å forstå hvilken type spørsmål vi bruker for å evaluere SQL-ferdigheter. Når du har registrert deg for en plan, kan du be om en tilpasset vurdering som vil bli tilpasset stillingsbeskrivelsen din. Den tilpassede vurderingen vil inneholde spørsmål for alle må-ha-ferdigheter som kreves for ETL-rollen din.

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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