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About the test:

ETL評価 テストでは、データの抽出に使用されるツールを識別する候補者の能力、抽出されたデータを論理的または物理的にマージし、ソースデータに適用する変換を定義して、データのコンテキストと概要のメソッドを宛先システムにロードするためのアウトラインメソッドを作成します。

Covered skills:

  • ETLジョブを自動化します
  • データウェアハウスアーキテクチャ
  • データアクセスタイプ
  • スターとスノーフレークのスキーマ
  • ETL対ELT
  • データパイプライン
  • データウェアハウスレイヤー
  • データモデリング
  • データ変換

9 reasons why
9 reasons why

Adaface ETL Test is the most accurate way to shortlist ETL開発者s



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:

  • ETLジョブを設計および自動化して、データを効率的に抽出、変換、およびロードすることができます
  • データ間でデータを移動および変換するためのデータパイプラインの構築に習熟
  • データウェアハウスアーキテクチャとその主要なコンポーネントの理解
  • 生データ、ステージング領域、データマートなど、データウェアハウスシステムのさまざまなレイヤーの知識
  • バッチ処理、リアルタイムストリーミング、インクリメンタルロードなど、さまざまなデータアクセスタイプに精通しています
  • データモデリングの手法と実践の専門知識
  • 効率的なデータ表現のためにスターとスノーフレークスキーマを設計する能力
  • データの品質と一貫性を確保するためのデータ変換技術に熟練
  • データ統合におけるETLとELTアプローチの違いの理解
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

これらは、10,000以上の質問のライブラリからのわずかなサンプルです。これに関する実際の質問 ETL評価テスト グーグルできません.

🧐 Question

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

Medium

Multi Select
JOIN
GROUP BY
Solve
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
Solve
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
Solve
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
Solve
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
Solve
Consider the following SQL table:
 image
How many tuples does the following query return?
 image

Medium

Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Solve
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
Solve
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
Solve
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
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.
🧐 Question🔧 Skill

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

Medium

Multi Select
JOIN
GROUP BY

2 mins

SQL
Solve

Medium

nth highest sales
Nested queries
User Defined Functions

3 mins

SQL
Solve

Medium

Select & IN
Nested queries

3 mins

SQL
Solve

Medium

Sorting Ubers
Nested queries
Join
Comparison operators

3 mins

SQL
Solve

Hard

With, AVG & SUM
MAX() MIN()
Aggregate functions

2 mins

SQL
Solve

Medium

Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries

2 mins

Data Warehouse
Solve

Medium

Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design

2 mins

Data Warehouse
Solve

Medium

Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning

2 mins

Data Warehouse
Solve

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
🧐 Question🔧 Skill💪 Difficulty⌛ Time
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
Multi Select
JOIN
GROUP BY
SQL
Medium2 mins
Solve
nth highest sales
Nested queries
User Defined Functions
SQL
Medium3 mins
Solve
Select & IN
Nested queries
SQL
Medium3 mins
Solve
Sorting Ubers
Nested queries
Join
Comparison operators
SQL
Medium3 mins
Solve
With, AVG & SUM
MAX() MIN()
Aggregate functions
SQL
Hard2 mins
Solve
Marketing Database
Columnar Storage
Data Warehousing
Analytical Queries
Data Warehouse
Medium2 mins
Solve
Multidimensional Data Modeling
Multidimensional Modeling
OLAP Operations
Data Warehouse Design
Data Warehouse
Medium2 mins
Solve
Optimizing Query Performance
Query Optimization
Indexing Strategies
Data Partitioning
Data Warehouse
Medium2 mins
Solve
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
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

Adaface を使用することで、最初の選考プロセスを 75% 以上最適化することができ、採用担当マネージャーと人材獲得チームの両方にとって貴重な時間を同様に解放することができました。


Brandon Lee, 人々の責任者, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment ETL評価テスト 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評価テスト 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

サンプルスコアカードを表示します
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評価テスト 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:

  • ETLジョブの自動化
  • データパイプラインの設計と実装
  • データウェアハウスアーキテクチャの理解と最適化
  • データウェアハウスのさまざまなレイヤーを使用します
  • さまざまなデータアクセスタイプを利用します
  • 効果的なデータモデリング手法の実装
  • 星とスノーフレークのスキーマを作成します
  • データの変換とクレンジングデータ
  • ETLプロセスとELTプロセスを区別します
  • ETLワークフローのトラブルシューティングと処理の例外

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?

  • データパイプライン

    データパイプラインを参照してくださいデータを収集、変換、およびあるシステムから別のシステムに移動する一連のプロセスとワークフロー。複数のソースからデータを抽出し、必要な変換と検証を実行し、ターゲットの宛先に配信することが含まれます。このスキルを測定することで、候補者が効率的でスケーラブルなデータパイプラインを設計する能力を評価して、スムーズなデータフローと統合を確保します。データウェアハウスシステム。データソース、データ収集、ストレージ、データモデリング、アクセスレイヤーなどのさまざまなコンポーネントが含まれます。このスキルを評価することで、採用担当者は、ビジネス要件を満たし、データ分析を可能にし、効率的なデータ検索をサポートする効果的なアーキテクチャを設計する候補者の知識を測定できます。データウェアハウスシステムのさまざまなレベルのデータ抽象化を表します。これらのレイヤーには、ステージング領域、データウェアハウス、およびプレゼンテーションレイヤーが含まれます。このスキルを評価することは、各レイヤーにデータが整理され保存される方法と、これらのレイヤーがどのように対話するかについての候補者の理解を判断して、簡単なデータの取得と分析を可能にします。アクセスタイプは、データウェアハウスからデータを取得するために使用されるさまざまな方法とプロトコルを指します。これらには、OLAP(オンライン分析処理)、OLTP(オンライントランザクション処理)、およびレポートツールが含まれます。このスキルの測定は、候補者のさまざまなデータアクセス方法に対する慣れを評価するのに役立ち、データ分析またはレポートタスクの要件に基づいて適切な方法を選択する能力を評価します。

  • データモデリング

    データデータモデリングとは、データベースの構造、関係、制約の概念的または論理的な表現を作成するプロセスです。データの保存方法と編成方法を定義するテーブル、列、関係の設計が含まれます。このスキルはテストで評価され、効率的なデータ検索、分析、レポートを促進する効果的なデータモデルを設計する候補者の能力を決定します。データウェアハウジングで使用される2つの一般的なデータモデリング手法です。 Star Schemaはデータを複数の寸法テーブルを持つ中央のファクトテーブルに編成し、Snowflakeスキーマは次元テーブルをさらに正規化することにより星スキーマを拡張します。このスキルの測定は、リクルーターがこれらのスキーマ設計の作成と作業における候補者の習熟度を評価するのに役立ちます。これらは、効率的なデータストレージと分析のためにデータウェアハウジングで一般的に使用されています。

  • データ変換

    データ変換ソース形式からターゲットシステムまたはデータウェアハウスに適した形式にデータを変更または変換することが含まれます。このプロセスには、データのクリーニング、集約、マージ、分割、またはデータの計算の実行が含まれる場合があります。このスキルを評価することで、データを正確かつ効率的に操作および変換する候補者の能力を判断し、ETL(抽出、変換、負荷)プロセス内のデータの整合性と品質を確保することができます。 <p> ETL(抽出、変換、負荷)およびELT(抽出、負荷、変換)は、データ統合プロセスで使用される2つのアプローチです。 ETLには、さまざまなソースからデータを抽出し、変換してからターゲットシステムにロードすることが含まれます。一方、ELTは、最初にターゲットシステムに生データをロードし、次に必要に応じて変換を実行することを伴います。このスキルを測定することで、採用担当者は、ETLとELTの重要な違いについての候補者の理解、および特定の要件と制約に基づいて適切なアプローチを選択および実装する能力を評価できます。

  • 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評価テスト to be based on.

    ETLの基本
    データ抽出
    データ変換
    データの読み込み
    データパイプライン
    ETLフレームワーク
    ETL最適化
    ETLツール
    データウェアハウスの概念
    データウェアハウスの設計
    データウェアハウスアーキテクチャ
    生データ
    ステージング領域
    データマート
    データアクセスタイプ
    バッチ処理
    リアルタイムストリーミング
    増分荷重
    データモデリング手法
    エンティティ関連モデリング
    寸法モデリング
    スタースキーマ
    スノーフレークスキーマ
    データ変換手法
    データマッピング
    データクレンジング
    データ統合
    データの一貫性
    ETL対ELT
    データウェアハウスのパフォーマンス
    データウェアハウスのセキュリティ
    データウェアハウジングツール
    ETLテスト
    ETLドキュメント
    データキャプチャを変更します
    データ統合パターン
    ETLベストプラクティス
    データプロファイリング
    ETLエラー処理
    メタデータ管理
    並列処理
    データ品質管理
    ETL監視
    データ系統
    ETLパフォーマンスチューニング
    データウェアハウススキーマ
    マスターデータ管理
    ゆっくりと変化する寸法
    データマートデザイン
    事実と寸法テーブル
    データウェアハウスガバナンス
    ELTツールとテクニック
    データモデリングツール
    クラウド内のデータウェアハウジング

What roles can I use the ETL Assessment Test for?

  • ETL開発者
  • ETLアナリスト
  • シニアETL開発者
  • ETLリード
  • シニアエンジニア(ETL)
  • データステージ開発者
  • Informatica ETL開発者
  • データエンジニア-ETL
  • BI開発者

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

  • データウェアハウスのドキュメントの開発と維持
  • ETLパフォーマンスとスケーラビリティを最適化します
  • データの摂取と複製技術の実装
  • 抽出物変換 - 荷重技術の理解と適用
  • データプロファイリングと品質保証の実行
  • データウェアハウスの次元モデリングの実装
  • データ統合パイプラインの構築と維持
  • 効果的なデータ変換プロセスの設計
  • データの視覚化ツールとテクニックの操作
  • 変更データキャプチャとリアルタイムデータ統合の実装
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採用担当者は、パネル面接中に尋ねる専門的な質問を通じて、どの候補者がより良いスコアを持っているかを判断し、スコアが低い候補者と区別できると感じました。彼らです 非常に満足 Adaface のスクリーニングで最終候補者に選ばれた候補者の質を重視します。


85%
スクリーニング時間の短縮

ETL Hiring Test よくある質問

複数のスキルを1つのカスタム評価に組み合わせることはできますか?

そのとおり。カスタム評価は、職務内容に基づいて設定され、指定したすべての必須スキルに関する質問が含まれます。

アンチチートまたは監督の機能はありますか?

次のアンチチート機能があります。

  • グーグル不可能な質問
  • IP監督
  • Webの提案
  • ウェブカメラの監督
  • 盗作の検出
  • 安全なブラウザ

[プロクチャリング機能](https://www.adaface.com/proctoring)の詳細をご覧ください。

テストスコアを解釈するにはどうすればよいですか?

留意すべき主なことは、評価が選択ツールではなく排除ツールであることです。スキル評価が最適化され、技術的にその役割の資格がない候補者を排除するのに役立ちます。これは、役割の最良の候補者を見つけるのに役立つために最適化されていません。したがって、評価を使用する理想的な方法は、しきい値スコア(通常は55%、ベンチマークを支援します)を決定し、インタビューの次のラウンドのしきい値を超えてスコアを上回るすべての候補者を招待することです。

このテストを使用できますか?

各ADAFACE評価は、職務記述書/理想的な候補者のペルソナにカスタマイズされます(当社の主題の専門家は、10000以上の質問のライブラリからあなたの評価に適切な質問を選択します)。この評価は、あらゆる経験レベルでカスタマイズできます。

すべての候補者は同じ質問を受け取りますか?

私は候補者です。練習テストを試すことはできますか?

いいえ。残念ながら、現時点では練習テストをサポートしていません。ただし、[サンプルの質問](https://www.adaface.com/questions)を使用するには、練習できます。

このテストを使用するコストはいくらですか?

無料トライアルを受けることはできますか?

私はちょうど有料プランに移りました。カスタム評価をリクエストするにはどうすればよいですか?

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今日、最も候補者のフレンドリーなスキル評価ツールをお試しください。
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