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Informatica Data Quality Online Test

The Informatica Data Quality online test evaluates a candidate's knowledge and skills in various aspects of data quality management. It covers topics such as data profiling, data cleansing, data standardization, duplicate detection, data monitoring, data validation, data enrichment, data governance, and data integration.

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Screen candidates with a 40 mins test

Test duration:  ~ 40 mins
Difficulty level:  Moderate
Availability:  Available as custom test
Questions:
  • 15 Informatica Data Quality MCQs
Covered skills:
Data Masking
Data Subset
Data Generation
Rule Simulator
Sequence Data Generation
Dictionary Data Generation
Data Quality
Data Profiling
Data Cleansing
Data Standardization
Duplicate Detection
Data Validation
Data Enrichment
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Use the Informatica Data Quality Test to shortlist qualified candidates

The Informatica Data Quality 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:

  • Able to perform Data Masking to protect sensitive information
  • Capable of generating realistic and representative Data Subsets
  • Proficient in generating large volumes of realistic test data using Data Generation techniques
  • Able to simulate and test complex data validation rules using Rule Simulator
  • Knowledge of generating sequential data using Sequence Data Generation
  • Familiarity with creating test data based on dictionary values using Dictionary Data Generation
  • Skilled in identifying and resolving data quality issues using Data Quality tools
  • Capable of profiling data to understand its structure, content, and completeness using Data Profiling techniques
  • Experienced in cleansing and transforming data to improve its quality
  • Knowledge of standardizing data to ensure consistency and compliance
  • Competent in detecting and handling duplicate records in data sets
  • Able to validate data against predefined rules and criteria
  • Proficient in enriching data with additional information or attributes
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Screen candidates with the highest quality 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

Test candidates on core Informatica Data Quality Hiring Test topics

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.

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Have questions about the Informatica Data Quality Hiring Test?

How does pricing work?

You can check out our pricing plans.

Can I customize the test?

Yes, absolutely. Custom assessments are set up within 48 hours based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test. You can also customize a test by uploading your own questions.

Can I combine multiple skills into one test?

Yes, absolutely. Custom assessments are set up based on your job description, and will include questions on all must-have skills you specify. Here's a quick guide on how you can request a custom test.

What roles can I use the Informatica Data Quality Test for?

Here are few roles for which we recommend this test:

  • 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
Can I see a sample test, or do you have a free trial?

Yes!

The free trial includes one sample technical test (Java/ JavaScript) and one sample aptitude test that you will find in your dashboard when you sign up. You can use it to review the quality of questions and the candidate experience of giving a test on Adaface.

You can preview any of the 500+ tests and see the sample questions to decide if it would be a good fit for your requirements.

How do I interpret test scores?

The primary thing to keep in mind is that an assessment is an elimination tool, not a selection tool. A skills assessment is optimized to help you eliminate candidates who are not technically qualified for the role, it is not optimized to help you find the best candidate for the role. So the ideal way to use an assessment is to decide a threshold score (typically 55%, we help you benchmark) and invite all candidates who score above the threshold for the next rounds of interview.

I'm a candidate. Can I try a practice test?

No. Unfortunately, we do not support practice tests at the moment. However, you can use our sample questions for practice.

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