Informatica Data Engineering Foundation study guide
Informatica Data Engineering Foundation · Work through your exam, one topic at a time.
The Informatica Data Engineering Foundation credential is an entry-level certification for data professionals who build and maintain data pipelines using Informatica's platform. It suits ETL developers, data engineers, analytics engineers, and integration specialists who need a vendor-neutral foundation in data integration, data quality, master data management, governance, and cataloging. The Informatica Data Engineering Foundation study guide has 5 sections and about 1,764 words.
01Data Integration
The Data Integration domain checks whether you can read a mapping's logic and predict its runtime behaviour: which transformations change row counts, how parameters resolve, and where pushdown or partitioning alters the result. Expect mapping-design questions, session/task configuration, and error-handling scenarios framed in both PowerCenter and Cloud Data Integration terms.
384 words, about 2 min02Data Quality & Observability
This domain tests your ability to pick the right quality operation for a symptom, read a scorecard correctly, and keep pipelines observable after go-live. Questions mix Cloud Data Quality assets (rules, scorecards, cleansing and matching transformations) with Data Observability signals such as freshness, volume, schema drift, and anomalous values.
355 words, about 2 min03Master Data Management (MDM)
This domain tests the Informatica MDM hub lifecycle: how records move from landing through staging to the base object, how match and merge produce a trusted record, and how survivorship and trust resolve conflicting values across source systems. Flow: landing tables receive source data, staging tables hold cleansed and standardized rows, the base object is the master table, and the hub store serves consolidated records.
369 words, about 2 min04AI & Data Governance
This domain covers Informatica's governance stack - business glossary, policies, stewardship, privacy classification and masking - plus how CLAIRE-based AI features (metadata-driven recommendations and generative assistance) support them. Expect questions on classifying sensitive data, applying policy outcomes, and governing AI and analytics use of data.
322 words, about 2 min05Enterprise Catalog
This domain tests metadata discovery and cataloging: registering resources, scanning them, reading technical and business lineage, and making assets findable and trustworthy through glossary links, certification, ownership, and search facets. A catalog is populated by scanners running against registered resources such as databases, files, BI tools, and cloud stores.
334 words, about 2 min