Informatica PR000005 Exam Topics
Informatica PR000005 Exam Overview :
| Exam Name: | Data Quality 9.x Developer Specialist |
| Exam Code: | PR000005 |
| Certifications: | Informatica ICP Certification |
| Actual Exam Duration: | 90 minutes |
| Expected no. of Questions in Actual Exam: | 60 |
| See Expected Questions: | Informatica PR000005 Expected Questions in Actual Exam |
Informatica PR000005 Exam Objectives :
| Section | Objectives |
|---|---|
| Informatica Data Quality (IDQ) Overview | The Informatica Data Quality (IDQ) Overview section provides a comprehensive introduction to the IDQ platform. This includes understanding the core components of the Data Quality solution, such as the Data Quality Console, Designer, and Processor. You’ll need to familiarize yourself with the architecture and functionality of these components, including how they interact to perform data quality tasks. The section covers the general features of IDQ, including its role in data integration and data governance. |
| Data Quality Concepts | In the Data Quality Concepts section, you will be tested for the fundamental principles of data quality. This includes understanding key concepts such as data profiling, data cleansing, and data matching. You'll need to grasp how these concepts are applied within Informatica Data Quality to improve the accuracy, consistency, and completeness of data. The section also covers the importance of data quality in the context of overall data management and governance strategies. |
| Designing and Implementing Data Quality Solutions | The Designing and Implementing Data Quality Solutions section focuses on creating and deploying data quality solutions using Informatica. This involves designing data quality rules and data transformation workflows to address specific data quality issues. You'll learn how to use the IDQ Designer to develop and test data quality mappings and transformations. This section also covers implementing data quality solutions within an existing data environment and integrating them with other data management processes. |
| Data Profiling and Analysis | Data Profiling and Analysis is a crucial section that involves examining data to understand its structure, content, and quality. You'll need to show how to use Informatica’s data profiling tools to analyze data sources and identify data quality issues. This includes generating data profiles, interpreting profiling results, and using these insights to guide data cleansing and improvement efforts. The section emphasizes the importance of profiling in setting up effective data quality processes. |
| Data Cleansing and Standardization | In the Data Cleansing and Standardization section, you’ll focus on techniques for improving data accuracy and consistency. This includes using Informatica tools to perform data cleansing operations such as removing duplicates, correcting errors, and standardizing data formats. You'll need to learn how to develop and apply cleansing rules and transformations to ensure that data adheres to established standards and requirements. The section covers best practices for maintaining high-quality data through ongoing cleansing efforts. |
| Data Matching and Deduplication | Data Matching and Deduplication deals with techniques for identifying and resolving duplicate records within datasets. This section covers the use of Informatica Data Quality tools to perform data matching, including setting up matching rules and algorithms. You’ll learn how to configure and execute matching processes to merge or eliminate duplicate records, improving the overall quality and reliability of the data. The section also addresses strategies for handling partial matches and ensuring data accuracy. |
| Official Information | https://www.informatica.com/services-and-training/certification/data-quality-certification.html#fbid=M68tJ9Yt3El |
Updates in the Informatica PR000005 Exam Topics:
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