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About the test:

Informatica -testen evaluerer en kandidats evne til at bruge PowerCenter til ETL. Den vurderer evnen til at udføre datasynkroniserings-/ replikationsopgaver, designdatatransformationer, administrere kilde/ måldefinitioner og datakrangling ved at anvende filter, sammenføje, samle, kategorisere, fusionere og ekspressionslogik uden at skrive SQL.

Covered skills:

  • Datalagring
  • Dataintegration
  • Database slutter sig til
  • Parameterisering
  • Sessioner og opgaver
  • Ekstraher transformbelastning (ETL)
  • Relationsdatabase CRUD -operationer
  • Mapplets
  • Arbejdsgange
  • Transformationer

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9 reasons why
9 reasons why

Adaface Informatica Test is the most accurate way to shortlist Informatica Developers



Reason #1

Tests for on-the-job skills

The Informatica 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 at designe og implementere datalagringsløsninger
  • Kapacitet til at udføre Extract Transform Load (ETL) -operationer på store datasæt
  • Færdigheder i at integrere forskellige datakilder i en samlet database
  • Dygtighed til at udføre relationelle database CRUD -operationer
  • Evne til at konstruere og optimere databaseforbindelser
  • Viden om at arbejde med Mapplets til datatransformation
  • Ekspertise i parameterisering af dataværende
  • Kompetence til styring af sessioner og opgaver i en dataintegrationsproces
  • Kendskab til at bruge forskellige datatransformationer
  • Kapacitet til at fejlfinde og håndtere fejl i databehandling
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 kun en lille prøve fra vores bibliotek med 10.000+ spørgsmål. De faktiske spørgsmål om dette Informatica online test vil være ikke-gåbart.

🧐 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

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:
 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
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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

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.
🧐 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

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

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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🧐 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
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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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Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Data Warehouse
Medium2 mins
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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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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 mod 75 %, hvilket frigjorde kostbar tid for både ansættelsesledere og vores talentanskaffelsesteam!


Brandon Lee, Leder af mennesker, Love, Bonito

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Reason #5

Designed for elimination, not selection

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

Reason #7

Detailed scorecards & benchmarks

Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

View sample 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 Informatica Assessment Test

Why you should use Pre-employment Informatica online test?

The Informatica 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:

  • Datalagringskoncepter og principper
  • Ekstraher Transform Load (ETL) -processen
  • Dataintegrationsteknikker og bedste praksis
  • Relationsdatabase CRUD -operationer
  • Databasemarkedstyper og optimering
  • Mapplets og deres brug i Informatica PowerCenter
  • Parameterisering for at forbedre fleksibiliteten i ETL -processer
  • Oprettelse og styring af arbejdsgang i Informatica PowerCenter
  • Session og opgavekonfigurationer i Informatica PowerCenter
  • Transformationstyper og brug i ETL -processer

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 Informatica online test?

  • udtræk transformationsbelastning (ETL) <//// H4> <p> ETL er processen med at udtrække data fra forskellige kilder, omdanne dem til et konsistent format og indlæse dem til et målsystem, typisk et datalager. Denne færdighed vurderes i testen for at evaluere kandidaternes evne til at håndtere komplekse dataintegrationsopgaver og sikre kvaliteten og pålideligheden af ​​data i målsystemet. </p> <h4> dataintegration

    Dataintegration involverer Kombination af data fra flere kilder, der kan være struktureret eller ustruktureret, for at give en samlet visning til analyse og rapportering. Kandidaters færdigheder i denne færdighed måles i testen for at måle deres evne til at integrere forskellige datakilder og sikre datakonsistens og nøjagtighed i hele organisationen.

  • Relationsdatabase CRUD -operationer

    CRUD -operationer Se Opret, læse, opdatere og slette handlinger, der udføres i en relationel database. Denne færdighed evalueres i testen for at vurdere kandidaternes forståelse af databasestyring og deres evne til at manipulere data ved hjælp af SQL -udsagn. Færdigheder i CRUD -operationer er vigtig for at vedligeholde og hente data effektivt fra relationelle databaser.

  • databaseforbindelser

    Databaseforhold bruges til at kombinere data fra flere tabeller baseret på fælles felter eller nøgler. Denne færdighed måles i testen for at bestemme kandidaternes ekspertise i konstruktion af komplekse SQL -forespørgsler, der involverer forskellige typer sammenføjninger, såsom indre sammenføjning, ydre sammenføjning og krydsmæssige sammenføjning. Færdigheder i databaseforhold er afgørende for at hente og analysere data fra relationelle databaser effektivt.

  • mapplets

    Mapplets er genanvendelige kortlægningskomponenter i Informatica PowerCenter, som giver udviklere mulighed for at definere og gemme almindelige transformationer, at det Kan kaldes fra flere kortlægninger. Denne færdighed vurderes i testen for at evaluere kandidaternes viden om oprettelse af mapplet, konfiguration og brug samt deres forståelse af datatransformationer og kortlægningsdesignprincipper.

  • parameterisering

    parameterisering er processen med at gøre kortlægningskomponenter dynamisk og konfigurerbar ved hjælp af parametre. Denne færdighed måles i testen for at vurdere kandidaternes evne til at designe kortlægninger, der kan tilpasse sig forskellige runtime -scenarier ved at parameterisere forskellige egenskaber og værdier. Færdigheder i parameterisering hjælper med at skabe fleksible og genanvendelige kortlægninger i Informatica PowerCenter.

  • arbejdsgange, sessioner og opgaver </H4> <p> Arbejdsgange, sessioner og opgaver er byggesten Opret og administrer komplekse dataintegrationsprocesser. Denne færdighed vurderes i testen for at evaluere kandidaternes forståelse af workflow -design, sessionskonfiguration og opgaveafhængigheder. Færdigheder i at arbejde med arbejdsgange, sessioner og opgaver er vigtig for effektivt at orkestrere dataintegrationsprocesser i Informatica PowerCenter. </p> <h4> transformationer

    Transformationer i Informatica PowerCenter bruges til at manipulere, validere og aggregere Data under ETL -processen. Denne færdighed måles i testen for at bestemme kandidaternes viden og ekspertise inden for forskellige typer transformationer, såsom aggregator, udtryk, opslag og filter. Færdigheder i transformationer er afgørende for rensning af data, berigelse og integration i datalagringsprojekter.

  • 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 Informatica online test to be based on.

    Datamodellering
    Dimensionel modellering
    Stjerneskema
    Snowflake -skema
    Faktabord
    Dimensionstabel
    ETL -proces
    Kildesystemanalyse
    Dataprofilering
    Datarensning
    Datatransformation
    Dataintegration
    Dataindlæsning
    Surrogatnøgler
    Trinvise belastninger
    Skift datafangst
    Langsomt skiftende dimensioner
    Metadata -styring
    Relationsdatabase
    SQL -operationer
    CRUD -operationer
    Database slutter sig til
    Inner Deltag
    Ydre sammenføjning
    Venstre slutter sig
    Højre deltagelse
    Fuld ydre sammenføjning
    Kryds sammenføjning
    Selvforbindelse
    Samlede transformationer
    Joiner -transformation
    Filtertransformation
    Ekspressionstransformation
    Routertransformation
    Opslagstransformation
    Merge transformation
    Normalizer -transformation
    Rang transformation
    Sekvensgeneratortransformation
    Aggregator -transformation
    Unionens transformation
    Sorteringstransformation
    Routertransformation
    Betingede transformationer
    Genanvendelige transformationer
    Udtryksprog
    Workflow Design
    Opgaveafhængigheder
    Sessionegenskaber
    Parameterfil
    Sessioner og overvågning af opgaver
    Fejlhåndtering
    Arbejdsgangsplanlægning
    Data Warehouse Architecture
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What roles can I use the Informatica online test for?

  • Informatica Developer
  • Senior Informatica Developer
  • Informatica Architect
  • Data Integration Developer (Informatica)
  • Software Engineer (Informatica)
  • Dataingeniør (Informatica)
  • Informatica ETL -udvikler
  • Informatica BI -konsulent

How is the Informatica 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

  • Datakvalitetsstyring og rensningsteknikker
  • Fejlhåndtering og undtagelsesstyring i ETL -processer
  • Ydelsesoptimering og indstilling til ETL -processer
  • Dataintegration og streaming i realtid
  • Ændring af datafangst (CDC) teknikker
  • Datavalidering og teststrategier
  • Metadata styring og konsekvensanalyse
  • Dimensionelle modelleringskoncepter til datalagring
  • Databaseindekseringsstrategier og forespørgseloptimering
  • Scripting og automatisering i Informatica PowerCenter
Singapore government logo

Ansættelseslederne mente, at de gennem de tekniske spørgsmål, som de stillede under panelinterviewene, var i stand til at fortælle, hvilke kandidater der havde bedre score, og differentieret med dem, der ikke scorede så godt. De er meget tilfreds med kvaliteten af ​​de kandidater, der er nomineret med Adaface-screeningen.


85%
Reduktion i screeningstid

Informatica Hiring Test Ofte stillede spørgsmål

Kan jeg evaluere andre relevante færdigheder som ETL, SQL i den samme test?

Ja. Vi understøtter screening af flere færdigheder i en enkelt test. Du kan gennemgå vores Standard SQL-test og [Standard ETL-test](https://www.adaface.com/assessment-test /ETL-Online-test) For at forstå, hvilken type spørgsmål vi bruger til at evaluere SQL- og ETL-færdigheder. Når du tilmelder dig enhver plan, kan du anmode om en brugerdefineret vurdering, der tilpasses til din jobbeskrivelse. Den tilpassede vurdering vil omfatte spørgsmål til alle de must-have-færdigheder, der kræves til din Informatica-rolle.

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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