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| Certification Vendor: | Huawei |
|---|---|
| Exam Name: | HCIP-Intelligent Vision V1.0 |
| Exam Number: | H12-521_V1.0 |
| Passing Score: | 60% |
| Certificate Validity Period: | 3 years |
| Related Certifications: | HCIP-Intelligent Vision |
| Exam Format: | True/False, Multiple Choice, Single Choice |
| Real Exam Qty: | 60-62 |
| Available Languages: | Chinese, English |
| Exam Price: | $300 USD |
| Exam Duration: | 90 minutes |
| Sample Questions: | Huawei H12-521_V1.0-ENU Sample Questions |
| Exam Way: | Pearson VUE test center or online proctored exam |
| Pre Condition: | Basic knowledge of video surveillance, networking, and intelligent vision technologies is recommended. |
| Official Syllabus URL: | https://e.huawei.com/en/talent/#/cert/product-details?certifiedProductId=347 |
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Huawei H12-521_V1.0 Exam is a challenging exam that requires candidates to have a solid understanding of intelligent vision technologies and their applications. Candidates who successfully pass H12-521_V1.0-ENU exam will have a competitive advantage in the IT industry and will be able to demonstrate their expertise in the area of intelligent vision technologies. HCIP-Intelligent Vision V1.0 certification earned from passing H12-521_V1.0-ENU Exam is recognized globally and can lead to better job opportunities and higher salaries.
NEW QUESTION # 29
What business attributes does the bayonet camera have? (Multiple choice)
Answer: A,B,C,D
NEW QUESTION # 30
Regardless of whether the product selected for the intelligent vision platform is IVS3800 or IVS1800, before performing platform intelligent behavior analysis. All need engineers to install and configure the corresponding algorithm plug-in.
Answer: B
NEW QUESTION # 31
When configuring vehicle lists, if you want certain vehicles to pass by without triggering alarms, you can add these vehicles to the ( ).
Answer: C
Explanation:
The correct answer is A. trustlist . In checkpoint and vehicle control logic, the system compares recognized license plates against configured vehicle lists and then determines whether an alarm or release action is required. The material explains that in a micro checkpoint scenario the system can "generate alarms after matching passing vehicles with vehicles in the list" . This means the behavior depends on how the vehicle list is defined.
From a professional intelligent-transportation and security-management perspective, a trustlist is the list used for vehicles that are allowed to pass normally without being treated as suspicious targets. By contrast, a blocklist or redlist is associated with vehicles that should trigger attention, control, interception, or alarm. A temporary list may be used for short-term management, but it does not inherently mean "pass without alarm." Therefore, if the intention is to let known authorized vehicles pass through the checkpoint without alarm generation, they should be added to the trustlist . That is the standard whitelist-style logic used in vehicle management systems.
NEW QUESTION # 32
For Huawei HoloSens IVS1800 multi-machine deployment network, what is the largest access scale of an iClent client camera?
Answer: B
NEW QUESTION # 33
Processors are important to the Intelligent Vision system, and different processors have different functions.
Link the following processor types to the corresponding description.
Answer:
Explanation:
Explanation:
* CPU # Central processing unit
* GPU # Graphics processing unit
* FPGA # Field-programmable gate array
* NPU # Neural processing unit
The correct matching is based on the standard definitions of the four processor types used in intelligent vision architecture. A CPU is the central processing unit , which handles general-purpose control, logic execution, and system coordination. A GPU is the graphics processing unit , originally designed for graphics rendering but now widely used for parallel computing tasks. An FPGA is a field-programmable gate array , which can be reconfigured for specific hardware logic functions and is valuable in acceleration and low-latency processing scenarios. An NPU is a neural processing unit , specialized for AI inference and deep-learning operations.
In intelligent vision systems, these processors serve different but complementary roles. The CPU manages operating control and service orchestration. The GPU accelerates highly parallel workloads. The FPGA supports custom hardware logic and fast pipeline processing. The NPU is especially important for AI-based vision services because it is optimized for neural-network computation, target recognition, and inference efficiency. This processor division is fundamental in intelligent camera, edge, and cloud architecture, where workload specialization improves overall system performance and intelligence capability.
NEW QUESTION # 34
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