Informatica Data Engineering Foundation study plan

Map the end-to-end data integration lifecycle and label where mapping, session, and workflow objects each live

Configure a reusable relational source and target definition and document every connection attribute you set

Build a pass-through mapping with one expression transformation that derives a new column, then run it in a session

Explain pushdown optimization versus the Data Integration Service engine and state when each is appropriate

Implement slowly changing dimension type 1 and type 2 logic with a lookup and update strategy

Configure a session for incremental load using a mapping parameter and a high-water-mark filter

Add a router transformation to split one source into insert, update, and reject paths

Diagnose and fix at least two common session failures from the log, such as a datatype mismatch or a lookup cache overflow

Design a reusable transformation that validates nulls, ranges, and referential integrity before load

Route invalid rows to a reject table with an error code and a source-row identifier

Explain how a Data Quality rule differs from an in-mapping validation and when to use each

Draft three data quality dimensions with a concrete measurable threshold for each

Define a scorecard of completeness, uniqueness, and timeliness metrics for one subject area

Write exception-report queries that surface duplicate and orphaned records

Set up a monitoring step that compares row counts and control totals between source and target

Describe how observability differs from testing and where alerting fits in the pipeline

Distinguish master data, reference data, and transactional data with an example of each in one business domain

Explain the match and merge process, including match rules, trust scores, and survivorship

Compare consolidation, registry, and coexistence MDM styles and select one for a stated scenario

Model a party entity with an identifier cross-reference table for multiple source systems

Trace how a golden record is created from two conflicting source records step by step

Draft data governance roles for an MDM program, naming who owns, stewards, and approves changes

List the policy controls needed for AI or analytics consumption of master data, such as consent and retention

Identify three governance risks of feeding ungoverned master data into a model

Classify data by sensitivity and map each class to a handling rule

Describe how lineage supports audit and impact analysis for a governed dataset

Explain the role of a data steward in resolving quality exceptions raised by governance policy

Outline an AI data-readiness checklist covering provenance, quality, and permitted use

Create catalog assets with descriptions, owners, tags, and a business glossary term attached

Link a glossary term to a physical column and verify the lineage view shows the path

Run a full review pass across all five domains and list weak spots by domain weight

Complete timed practice sets and log every missed question with the underlying concept