Informatica Data Quality Online Test

The Informatica Data Quality 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:

• Data masking techniques to protect sensitive information
• Creating smaller, representative subsets of larger datasets
• Ability to create realistic test data for various scenarios
• Proficiency in simulating and testing data quality rules to ensure accuracy
• Skill in generating sequential or ordered test data
• Ability to generate test data based on a predefined dictionary
• Understanding of data quality concepts and techniques
• Skill in identifying and correcting errors or inconsistencies in data
• Proficiency in applying standardization rules to ensure consistent data

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.

• Data Masking

  Data masking is the process of replacing sensitive information with fictitious data, while preserving the overall structure, usability, and functionality of the original data. This skill should be measured in the test to assess the candidate's ability to protect sensitive data and ensure compliance with data security regulations.

• Data Subset

  Data subset refers to the process of creating a smaller, representative sample of a larger dataset. This skill should be measured in the test to evaluate the candidate's proficiency in extracting relevant subsets of data for analysis or testing purposes, which can help optimize performance and reduce resource requirements.

• Data Generation

  Data generation involves creating synthetic data that mimics real-world data. This skill should be measured in the test to assess the candidate's capability to generate large volumes of test data for various scenarios, such as performance testing, without relying on production data.

• Rule Simulator

  A rule simulator is a tool used to test and validate data quality rules and their impact on datasets. This skill should be measured in the test to evaluate the candidate's proficiency in simulating and verifying data quality rules, ensuring the accuracy, completeness, and validity of data.

• Sequence Data Generation

  Sequence data generation involves generating sequential values, such as unique identifiers or timestamps, in a specific order. This skill should be measured in the test to assess the candidate's ability to generate ordered sequences of data, which can be useful for maintaining data integrity and consistency in various applications.

• Dictionary Data Generation

  Dictionary data generation is the process of creating data based on predefined lists or dictionaries containing specific values. This skill should be measured in the test to evaluate the candidate's proficiency in generating data that adheres to predefined standards or criteria, facilitating data analysis and comparisons.

• Data Quality

  Data quality refers to the level of accuracy, completeness, consistency, and reliability of data. This skill should be measured in the test to assess the candidate's understanding of data quality concepts and their ability to identify and address data quality issues, ensuring the reliability and usability of data within an organization.

• Data Profiling

  Data profiling involves analyzing and understanding the structure, content, and quality of data. This skill should be measured in the test to evaluate the candidate's expertise in accurately assessing data quality, identifying data anomalies, and providing insight into data patterns, which is crucial for making informed data-driven decisions.

• Data Cleansing

  Data cleansing is the process of identifying and correcting erroneous, incomplete, or irrelevant data. This skill should be measured in the test to assess the candidate's ability to identify and rectify data quality issues, ensuring data accuracy, consistency, and integrity prior to analysis or integration with other systems.

• Data Standardization

  Data standardization involves transforming data into a consistent and predefined format, typically conforming to specific rules or guidelines. This skill should be measured in the test to evaluate the candidate's proficiency in converting data into a uniform format, facilitating data integration, comparability, and efficient data management.

• Duplicate Detection

  Duplicate detection is the process of identifying and removing duplicate records within a dataset. This skill should be measured in the test to assess the candidate's ability to develop algorithms or techniques for detecting and eliminating duplicate data, enhancing data accuracy, and reducing redundancy.

• Data Validation

  Data validation is the process of ensuring that data adheres to predefined rules, constraints, or requirements. This skill should be measured in the test to evaluate the candidate's expertise in validating data against specified criteria, detecting errors or inconsistencies, and ensuring data reliability, integrity, and adherence to business rules.

• Data Enrichment

  Data enrichment involves enhancing existing data by adding additional information, such as demographics, geolocation, or external datasets. This skill should be measured in the test to assess the candidate's proficiency in augmenting data with valuable insights, improving data completeness, accuracy, and overall quality for better decision-making and analysis.

• 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 Data Quality Test to be based on.

  Data Masking

  Sensitive Data Protection

  Data Subset Generation

  Test Data Generation

  Data Validation Rules

  Complex Rule Simulation

  Sequential Data Generation

  Dictionary Data Generation

  Data Quality

  Data Profiling

  Data Cleansing

  Data Standardization

  Duplicate Detection

  Data Validation

  Data Enrichment

  Data Quality Assessment

  Data Quality Metrics

  Data Profiling Methods

  Data Cleansing Techniques

  Data Lineage Analysis

  Data Anomaly Detection

  Data Quality Dashboards

  Data Quality Dimensions

  Data Matching Algorithms

  Data Standardization Rules

  Data Validation Techniques

  Data Enrichment Methods

  Data Masking Strategies

  Data Subset Techniques

  Data Generation Algorithms

  Rule Simulation Scenarios

  Sequence Data Generation Patterns

  Dictionary Data Generation Sources

  Duplicate Detection Algorithms

  Data Quality Frameworks

  Data Profiling Tools

  Data Cleansing Software

  Data Standardization Libraries

  Data Validation Tools

  Data Enrichment APIs

  Data Masking Best Practices

  Data Subset Optimization

  Data Generation Performance

  Rule Simulation Accuracy

  Sequence Data Generation Efficiency

  Dictionary Data Generation Customization

  Duplicate Detection Precision

  Data Validation Robustness

  Data Enrichment Integration

  Data Masking Regulations

  Data Profiling Automation

  Data Cleansing Databases

  Data Standardization Models

  Data Validation Frameworks

  Data Enrichment Strategies

• Informatica Data Quality Developer
• ETL Developer - Informatica/Data Quality
• Informatica Data Quality Senior Analyst
• Informatica Cloud Data Quality (CDQ) Consultant
• Informatica Data Quality Application Developer

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

• Identify and handle duplicate records in a dataset
• Validating data against predefined rules or criteria
• Enhance existing data with additional information
• Data governance principles and practices
• Integrating data from multiple sources
• Design and maintain data warehousing solutions
• Understanding of ETL processes and tools
• Skill in writing complex database queries and scripts