Huawei H12-521_V1.0 - HCIP-Intelligent Vision V1.0

The correct answer is B . The material defines the strobe light as a lighting compensation device, stating that “Strobe light: provides light compensation at night to clearly capture license plates and pedestrians” . This directly shows that the purpose of the strobe light is to improve scene illumination so that important visual targets can be captured more clearly, especially in low-light conditions. That function most closely matches option B .

From an engineering standpoint, a strobe light is used to enhance the effective brightness of the monitored area and improve the visibility of vehicles, license plates, and other targets in both live viewing and snapshot capture. The installation notes further mention that the strobe light should be angled properly relative to the lane to avoid overexposure of license plates caused by direct illumination , which confirms that its role is optical illumination rather than sensing or speed detection. Option A is more consistent with a flash light , because the material states that “Flash light: provides strong light compensation to clearly capture targets and vehicle interiors” . Options C and D describe sensor and radar-related functions, not strobe light behavior.

In Huawei's intelligent video surveillance architecture, the IVS3800 and CloudIVS 3000 represent different tiers of the platform but share a compatible underlying software-defined framework. The IVS3800 is often deployed as a foundational or management node in smaller to medium-sized clusters, whereas the CloudIVS 3000 is designed for large-scale cloud-based environments. While the two systems are interoperable and can be integrated within a unified cluster environment to ensure resource sharing and centralized management, there are specific functional roles assigned to each.

Specifically, when these systems coexist in a hybrid cluster, the CloudIVS 3000 typically functions in a capacity-extension role. According to the technical deployment standards, the IVS3800 and CloudIVS 3000 can be deployed in the same cluster, but the CloudIVS 3000 can serve only as a service node . This means that while the IVS3800 can handle management tasks, database operations, and service logic, the CloudIVS 3000 nodes are primarily utilized for their heavy-duty media processing and storage capabilities (service tasks). This hierarchical arrangement allows for flexible scaling, where users can add CloudIVS nodes to increase the total channel count and storage capacity of an existing IVS3800 infrastructure without overcomplicating the management plane.

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.

The correct answer is B. FALSE . Behavior analysis, video synopsis, and video search are not isolated single-function outputs. They are built on a chain of capabilities that includes moving-object extraction, trajectory processing, segmentation, indexing, and structured retrieval. The material shows this dependency very clearly. For synopsis, it states that “Moving object detection involves algorithms such as background modeling, scene segmentation, and moving object extraction” , then “Object trajectory extraction involves object tracking algorithms” , and finally the system “Generate[s] a compact video” by fusing trajectories and background frames . For video search, the document further explains that indexed features and structured search conditions are required to browse and retrieve target information efficiently .

Because these services involve both intelligent analysis and media-content handling or structured-search capability, treating them as requiring only the VA algorithm is too narrow. In practical deployment, the MCS-related capability is also needed to support the complete algorithm service chain. Therefore, the statement that only the VA algorithm is required is false.

The correct answer is A because the SDC OS is designed with open northbound interconnection capability for platform integration. The material explains that “Standard interfaces such as ONVIF are used to connect cameras to the IVS platform” and further states that the open SDC OS supports camera connection through northbound access protocols such as “ONVIF, GB/T 28181, and GA/T 1400”

This directly confirms option A .

In practical intelligent vision deployment, these protocols are essential for interoperability between cameras, platforms, and third-party systems. ONVIF is widely used for standardized video surveillance interoperability. GB/T 28181 is important in many public-security and video networking scenarios. GA/T 1400 is commonly associated with structured data and public-security video information exchange. The other options list either general IT service protocols or physical interfaces, which are not the defined northbound camera access protocols in this context. Therefore, A is the correct answer because it precisely matches the supported protocol set described in the HCIP Intelligent Vision material.

The IVS3800 is a versatile intelligent video surveillance platform that can be configured for various roles, including storage, management, and intelligent analysis. To perform high-performance intelligent target or vehicle analysis—such as extracting features from video or images—the system requires dedicated computing resources beyond standard CPU capabilities. According to the technical specifications of the IVS3800 (specifically models like the IVS3800C), the hardware must be configured with an AI accelerator card , specifically the Ascend accelerator card.

For instance, a standard IVS3800C configuration for vehicle analysis in the Chinese mainland supports 360 channels in video mode when installed with six Ascend accelerator cards. These cards provide the "ultimate computing power" necessary for neural network-based processing, such as object classification and attribute recognition. While NVMe cards and SAS/SATA disks are used for high-speed cache and mass storage respectively, they do not provide the specialized AI inference logic required for data structuring. Therefore, the AI accelerator card is the mandatory component for enabling intelligent analysis functions on the IVS3800 platform.

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.

The correct answer is A. TRUE . The idea of an observation circle in video site planning is to form a perimeter-style observation system around a protected area so that targets can be detected, tracked, and controlled at key boundary points. This matches the planning logic used in intelligent vision deployments, where site selection focuses on strategically important locations. The material identifies priority planning positions such as “Road intersections and entrances and exits” and “Key areas in cities” . These are exactly the kinds of points used to form an observation circle.

From a professional planning perspective, the observation circle concept is not just about placing cameras randomly; it is about creating an enclosed prevention-and-control framework by covering the external boundary and critical transition points of an area. Inter-city boundaries and city entrances or exits are typical examples because they are natural choke points for observation, vehicle monitoring, and event response. By surrounding the target area through layered site deployment, the system improves awareness, traceability, and early warning capability. Therefore, the statement correctly describes the purpose and example of an observation circle.

The correct answers are A, C, and D because cloud-edge synergy is defined as collaboration between a central platform and lightweight edges or other upper- and lower-level cloud domains. The material states that “Cloud-edge synergy: collaboration between the central platform and lightweight edges or other upper- and lower-level domains deployed on clouds” while separately identifying “Multi-camera Collaboration: Multiple cameras collaborate to identify same image” . This distinction is decisive: option B belongs to multi-camera collaboration, not cloud-edge synergy.

In practical JVS deployment, cloud-edge synergy includes centralized task orchestration, algorithm lifecycle management, and cross-domain resource coordination. That is why delivering vehicle alert tasks downward to lower-level platforms fits the model, and why centrally loading, upgrading, or replacing algorithms on upper- and lower-level platforms also fits. Likewise, when the upper-level platform applies for edge-side resources to perform intelligent analysis on video or images, it is using distributed resource collaboration, which is a classic cloud-edge function. Option B is excluded because it describes camera-to-camera cooperative recognition rather than platform-to-edge coordination. Therefore, the valid cloud-edge synergy scenarios are A, C, and D .