Exam facts (full table)
| Attribute | Details |
|---|---|
| Code | ICP-DQ |
| Name | Informatica Certified Professional – Cloud Data Quality |
| Vendor | Informatica |
| Level | Professional |
| Duration | 90 minutes |
| Question Count | 70 questions |
| Question Types | Multiple choice, multiple response, true/false |
| Pass Score | 70% |
| Delivery | Online, proctored |
| Cost | $340 USD (4 units × $85) |
| Validity | 2 years |
| Prerequisites | Data Quality fundamentals recommended |
| Exam Resources | Limited (no reference materials) |
Vendor source — Informatica Certified Professional – Cloud Data Quality ↗
About
The Informatica Certified Professional – Cloud Data Quality certification validates expertise in designing and implementing data quality solutions on Informatica's cloud platform. This certification is ideal for data quality professionals, data governance specialists, and data stewards implementing comprehensive quality assurance programs across cloud data ecosystems.
Certified professionals demonstrate proficiency in data profiling, standardization, matching, consolidation, and governance—critical capabilities for organizations managing complex, distributed data environments and ensuring data reliability for analytics and operations.
Domain context
Data quality has evolved from a technical discipline to a strategic business imperative:
Data Profiling & Assessment: Analyzing data to understand structure, completeness, accuracy, and patterns. Identifying data quality issues at their source. Using profiling insights to guide remediation strategies.
Data Standardization: Applying consistent formats, business rules, and naming conventions. Normalizing data across diverse sources. Preparing data for matching and consolidation.
Data Matching: Identifying duplicate and similar records across datasets using advanced algorithms. Managing false positives and false negatives. Building entity resolution models.
Data Consolidation: Creating unified master records from fragmented sources. Managing survivorship rules and conflict resolution. Building single customer views.
Data Governance Integration: Embedding data quality into broader governance frameworks. Managing stewardship workflows. Implementing compliance and privacy controls.
Topics covered
Data Quality Fundamentals (15% of exam weight)
- Data quality dimensions (accuracy, completeness, consistency, timeliness)
- Data quality frameworks and maturity models
- Quality metrics and KPIs
- Profiling overview and strategies
- Root cause analysis
- Quality improvement methodologies
Data Profiling (20% of exam weight)
- Column profiling and statistics
- Pattern discovery and anomaly detection
- Cardinality and distribution analysis
- Cross-column analysis and dependencies
- Data quality scorecards
- Profiling in cloud environments
Data Standardization (18% of exam weight)
- Standardization rules and algorithms
- Address standardization and validation
- Phone, email, and identifier standardization
- Custom standardization rules
- Reference data management
- Parsing and segmentation
Data Matching (20% of exam weight)
- Matching algorithms and scoring
- Training and tuning match rules
- Entity resolution techniques
- Weighted comparison functions
- Phonetic and fuzzy matching
- Batch and incremental matching
Data Consolidation (15% of exam weight)
- Consolidation logic and survivorship rules
- Master record creation and management
- Record linkage and hierarchy
- Conflict resolution strategies
- Golden record maintenance
- Data lineage and tracking
Governance and Integration (12% of exam weight)
- Data stewardship workflows
- Quality controls in data pipelines
- Compliance and regulatory requirements
- Privacy and data protection (GDPR, CCPA)
- Integration with data catalogs
- Monitoring and alerting
Common job-ready skills
- Assess and profile data quality using cloud tools
- Design data standardization and cleansing rules
- Implement sophisticated matching and entity resolution
- Build consolidated master data repositories
- Monitor data quality in production pipelines
- Design data quality governance frameworks
- Create quality scorecards and reports
- Integrate quality checks into ETL processes
- Manage stewardship and remediation workflows
- Communicate quality metrics to stakeholders
Recommended courses
- Informatica University: Cloud Data Quality Professional Certification
- Informatica University: Data Profiling and Standardization
- Informatica University: Master Data Management essentials
- Udemy: Informatica Data Quality courses
- Pluralsight: Data Quality and Governance fundamentals
Practice exams
- Informatica: Official practice exams and labs
- ExamsBoost: Data Quality practice questions
- CertKillers: DQ exam preparation materials
- CloudFoundation: IDQ certification guide and practice
Books
- Informatica Cloud Data Quality documentation (official)
- "Data Quality: The Field Guide" by David Loshin
- "The Data Governance Imperative" by John Ladley
- Informatica whitepapers and best practices guides
Job titles
- Data Quality Analyst
- Data Quality Engineer
- Data Steward
- Data Governance Specialist
- Master Data Manager
- Data Quality Architect
- Information Architect
- Data Analyst
- Business Analyst (Data focus)
- Quality Assurance Engineer (Data)
Salary (USD / ZAR×18 / GBP / EUR / AUD)
| Region | Low | Mid | High |
|---|---|---|---|
| USD | $90,000 | $130,000 | $180,000 |
| ZAR×18 | R1,620,000 | R2,340,000 | R3,240,000 |
| GBP | £70,000 | £100,000 | £140,000 |
| EUR | €78,000 | €112,000 | €157,000 |
| AUD | A$135,000 | A$195,000 | A$270,000 |
Note: Data quality specialists with governance focus command 10-25% premium. Combined with MDM certification increases earning potential by 15-30%.
Skills validated
- Data quality assessment and profiling
- Standardization algorithm design
- Matching and entity resolution
- Data consolidation and governance
- Cloud data quality tools
- Quality metrics and reporting
- Compliance and privacy implementation
- Governance framework design
- Stakeholder communication
- Quality improvement methodologies
Related certs
- Informatica ICP-IDMC: Cloud Data Integration (complementary)
- Informatica ICP-MDM: Master Data Management (highly complementary)
- Informatica ICS-DQ: Specialist-level (foundational)
- DAMA CDMP: Certified Data Management Professional (general governance)
- Gartner Data & Analytics: Governance focus