Huawei H19-372 (HCS-Pre-Sale-Big Data) certification exam is an essential step for professionals who want to become certified pre-sales specialists in big data solutions. It is a comprehensive exam that covers a wide range of topics related to big data technologies, and it is recognized worldwide as a valuable asset for professionals in the industry. With the right preparation and study, candidates can successfully pass the exam and advance their careers in big data.
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Huawei H19-372: HCS-Pre-Sale-Big Data is an exam designed for individuals who are passionate about big data and possess skills and knowledge that can help organizations make informed decisions. HCS-Pre-Sale-Big Data (Huawei Certified Pre-sales Specialist Big Data) certification validates the expertise of the pre-sales specialist who possesses the skills and knowledge to help clients derive maximum value from their big data solutions. HCS-Pre-Sale-Big Data (Huawei Certified Pre-sales Specialist Big Data) certification aims to make individuals reliable advisors to their business clients, able to guide them on the best course of action for their big data projects.
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Huawei H19-372 (HCS-Pre-Sale-Big Data) exam is a certification test that validates the knowledge and skills of professionals who are looking to work as pre-sales specialists in the field of big data. HCS-Pre-Sale-Big Data (Huawei Certified Pre-sales Specialist Big Data) certification is offered by Huawei Technologies Co. Ltd, a leading global provider of information and communications technology (ICT) infrastructure and smart devices.
Huawei H19-372 Exam Syllabus Topics:
| Section | Weight | Objectives |
| Huawei Big Data Platform and Products | 30% | - Product positioning and core features
- Technical specifications and advantages
- FusionInsight platform overview
|
| Big Data Solution Design & Pre-sales | 15% | - Solution architecture design
- Cost estimation and competitiveness analysis
- Customer requirement analysis
|
| Big Data Ecosystem Architecture | 25% | - Distributed computing framework
- Distributed storage framework
- Data management and scheduling systems
|
| Typical Industry Application Cases | 10% | - Case analysis and solution matching
- Application scenarios in government, finance, and energy
|
| Big Data Fundamentals | 20% | - Basic concepts and characteristics of big data
- Big data industry trends and application scenarios
|