Informatica DVO Test

Focused on evaluating how well candidates can navigate the DVO interface and execute simple operations, this topic includes tasks like setting up source-to-target mappings, running basic data validation tests, and understanding different types of validations (e.g., row count, column comparison). It also tests understanding of tool configurations and key UI elements, ensuring candidates can confidently handle day-to-day data validation tasks.

This topic assesses candidates’ proficiency in writing SQL queries within DVO for validation tasks. Questions cover essential SQL concepts such as SELECT statements, filtering, joins, aggregate functions, and basic data transformations. This topic ensures candidates understand how SQL is used to create and execute validation rules in DVO, including row-level validations and summary-level validations for data consistency.

This in-depth topic explores the core of data validation processes within DVO. It covers setting up validation tests, defining validation rules, configuring source-target comparisons, and mapping fields. Candidates are expected to understand the end-to-end process of validating data from ingestion to output, ensuring data accuracy and quality across multiple environments. Topics like row-level, field-level, and cross-database validation are tested.

Data quality is critical in any ETL or migration process. This topic focuses on identifying common data quality issues such as duplicates, null values, incorrect data types, and missing records. It also evaluates how candidates troubleshoot and resolve data validation errors using DVO's built-in functionalities. Error reporting, logging, and best practices for ensuring data accuracy are included to assess candidates' knowledge in resolving real-world data quality issues.

Informatica DVO often works in conjunction with other Informatica products. This topic covers how to integrate DVO with tools like Informatica PowerCenter (for ETL) and Informatica Data Quality (for cleansing and profiling). Candidates will be tested on their ability to use DVO in multi-tool environments, set up automated workflows, and create synergies between DVO and other products like MDM (Master Data Management) to ensure seamless data validation and quality control.

Candidates are expected to create complex validation scenarios using custom SQL queries. This topic covers advanced SQL techniques such as window functions, subqueries, and advanced joins, applied to solve complex validation needs like multi-source validation, complex transformation validation, and summary-level aggregation checks. It also includes scenarios where custom SQL queries are combined with standard DVO validations to handle unique data challenges.

Reporting is crucial for communicating validation results and discrepancies to stakeholders. This topic evaluates the candidate’s ability to generate, customize, and export validation reports. Advanced questions test how candidates automate report generation, customize reports to meet specific business needs, and integrate validation results into reporting tools like Tableau, Power BI, or Excel. Understanding report formats and the significance of validation results is also tested.

Automation is vital for reducing manual effort and ensuring consistent data validation workflows. This topic evaluates the ability to automate DVO tasks using scripting languages (e.g., Python, Shell scripting). Candidates will be tested on how they integrate scripts to schedule and run validation tests automatically, including error handling automation and integrating validation automation into broader data pipelines (e.g., using Cron jobs or PowerCenter workflows).

As enterprises face increasing regulatory requirements (e.g., GDPR, HIPAA), data governance and compliance play a vital role in data validation. This topic covers how DVO supports data governance frameworks by ensuring that validation processes meet regulatory standards. It also tests the candidate’s ability to configure validation workflows to generate compliance reports and set up audit trails. Advanced scenarios include integrating DVO validation with enterprise data governance tools and processes.