Hitachi HQT-2900 Q&A - in .pdf

  • Exam Code: HQT-2900
  • Exam Name: Hitachi Vantara Qualified Professional - Pre-sales Data Analytics Foundation
  • Q & A: 0 Questions and Answers
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Hitachi HQT-2900 Q&A - Testing Engine

  • Exam Code: HQT-2900
  • Exam Name: Hitachi Vantara Qualified Professional - Pre-sales Data Analytics Foundation
  • Q & A: 0 Questions and Answers
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Hitachi HQT-2900 is an exam designed to evaluate the knowledge and skills of a pre-sales data analytics professional. HQT-2900 exam is conducted by Hitachi Vantara, a global leader in data storage, analytics, and infrastructure solutions. The HQT-2900 exam is aimed at testing the fundamental knowledge of a pre-sales data analytics professional in areas such as data analysis, visualization, architecture, and deployment.

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Hitachi HQT-2900 exam is a challenging yet rewarding certification exam for professionals in the pre-sales data analytics field. It represents a significant milestone in their career and can help them stand out in a competitive job market. By passing the exam and obtaining the certification, professionals can demonstrate their expertise in pre-sales data analytics and their ability to help customers solve complex data analytics challenges.

Hitachi HQT-2900 certification exam is administered by Hitachi Vantara, a subsidiary of Hitachi Ltd. Hitachi Vantara is a global provider of data management and analytics solutions, and the certification exam is designed to ensure that professionals who work with Hitachi Vantara solutions have the knowledge and skills necessary to provide effective pre-sales support.

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Hitachi HQT-2900 Exam Syllabus Topics:

SectionObjectives
Topic 1: Data Catalog and Governance- Metadata management
  • 1. Data dictionary generation
    • 2. Automated data discovery
      - Lineage and compliance
      • 1. Data lineage visualization
        • 2. Impact analysis for changes
          Topic 2: Pentaho Business Analytics- Self-service analytics
          • 1. Drill-down and exploration
            • 2. Interactive visualization (Analyzer)
              - Reporting and dashboards
              • 1. Embedded analytics
                • 2. Pixel-perfect reporting
                  Topic 3: Data Integration (PDI)- Transformation Techniques
                  • 1. Joins and merge operations
                    • 2. Data standardization and scripting
                      - ETL Concepts
                      • 1. Incremental load and CDC
                        • 2. Extract, Transform, Load fundamentals
                          Topic 4: Data Optimization (PDO)- Storage optimization
                          • 1. Cold data identification
                            • 2. Data tiering to lower-cost storage
                              Topic 5: Data Analytics Foundations- Enterprise Data Challenges
                              • 1. Data governance fundamentals
                                • 2. Data silos and AI readiness
                                  - Data Quality Concepts
                                  • 1. Data profiling and cleansing concepts
                                    • 2. Impact of data quality on analytics and AI
                                      Topic 6: Pentaho+ Platform Overview- Core platform capabilities
                                      • 1. Data ingestion and integration (PDI)
                                        • 2. Data quality management (PDQ)
                                          • 3. Data optimization and tiering (PDO)
                                            • 4. Catalog and metadata management (PDC)

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