Pass the Huawei HCIP Intelligent Vision H12-521_V1.0 Questions and answers with CertsForce

The correct answer is B because the 1 + N function is designed to extend intelligent analysis capability to existing non-intelligent cameras without replacing them. This is one of Huawei’s practical upgrade approaches for legacy surveillance systems. The material states that “With the proprietary 1 + N technology, a single intelligent camera can connect to N generic cameras on a live network, request streams from these cameras, and perform intelligent analysis on the streams”

This directly matches option B .

From a solution-design standpoint, this function is highly valuable in reconstruction projects where the existing camera network is still usable but lacks intelligent features such as target capture or vehicle recognition. Instead of replacing all cameras, one intelligent camera can act as the analysis point for several generic cameras, reducing upgrade cost and deployment complexity. Option A is wrong because the function is not for storage expansion. Option C incorrectly changes the relationship into intelligent-camera-to-PTZ cooperation. Option D is also wrong because the whole purpose of 1 + N is to analyze additional external streams, not just the local one. Therefore, B is the correct answer.

The correct answers are A, C, and D because the documents prepared before IVS low-level design are the input materials used for design, deployment generation, and parameter definition, while the LLD report is an output of the LLD activity itself. The material emphasizes that network planning must be completed in advance, and the deployment process requires predefined addressing and parameter information. This is consistent with statements such as determining the network segment and IP address range for each network plane before deployment, and with the repeated use of fixed external service and management IP planning in domain and cluster configuration workflows .

From an engineering workflow perspective, the network plan file is required because network segments, IP ranges, and live-network planning must be decided in advance. A project configuration file is also needed because it carries deployment-specific parameters for the project. A JSON file is typically used as a structured import or export artifact generated by design tools and then consumed during planning or deployment preparation. However, the LLD report is not a prerequisite input document; it is the design deliverable produced after engineers complete the LLD work. Therefore, the required pre-LLD preparation documents are A, C, and D .

The correct answer is C because the Kunpeng processor is characterized by ARM-based RISC design, multi-core capability, low power consumption, and high integration, but not by a closed architecture . The material describes Kunpeng’s strengths by stating that “The ARM architecture is used so that the same function performance occupies a smaller area of the chip, consumes less power, and are highly integrated. More hardware CPU cores are provided for better concurrent processing” and further notes that “The RISC instruction set is used. The instruction length is fixed, the addressing mode is flexible and simple, and the execution efficiency is high” . These statements directly support options A, B, and D .

From an engineering perspective, ARM-based processors are known for compact instruction design, strong concurrency through many CPU cores, and high performance per watt. Those are exactly the attributes highlighted here. By contrast, “closed architecture” is not presented as a Kunpeng feature in the training material and does not align with Huawei’s ecosystem positioning, which emphasizes compatibility across multiple operating systems and application scenarios from IoT to cloud. Therefore, C is the only option that does not describe the Kunpeng processor.

High-density target capture is a specialized capability designed for scenarios with high pedestrian traffic, such as urban squares or station exits, where crowd density can exceed 100 person-times in a single frame. This feature ensures accurate trajectory generation and optimal snapshots without missed or false captures in challenging environments. The technical architecture of this feature is built upon three fundamental pillars, as the system requires that high-density target capture enabled by: object detection algorithm and target trajectory generation algorithm + chip computing power + large-capacity memory .

Hardware requirements are particularly stringent, necessitating a professional AI chip, such as a 4 TOPS NPU, to manage the processing load of tracking up to 200 targets per frame. Large- capacity memory, typically 4 GB of DDR, is also vital to ensure an ultra-low snapshot repetition rate of less than 8%. While high-frequency exposure—specifically T-Shot double-exposure technology—is an advanced imaging technique used to capture clear snapshots of vehicles and targets simultaneously at night, it is a distinct imaging enhancement rather than a prerequisite for the high-density capture logic itself. High-frequency exposure focuses on resolving illumination and motion blur, whereas high-density capture focuses on the computational capacity to detect and track a massive volume of objects.

The correct answer is B because Lite Edge is designed as a container-based edge computing architecture that emphasizes service deployment, compute scheduling, and lightweight resource orchestration, rather than a storage-only deployment. The material explains that “The CSP and CSP Edge adopt a unified architecture to support the Central Cloud and Lite Cloud respectively” and further states that “The Lite Cloud integrates with the HASEN to implement container resource management and deployment” and “The container-based Lite Edge can schedule only CPU resources of single-node systems” . This makes it clear that compute capability is fundamental to Lite Edge scenarios.

From a solution-design perspective, Lite Edge can be deployed in integrated or combined modes, such as a fusion server or combinations involving compute and search , because intelligent services require application execution and scheduling resources. A storage-only server does not fit that functional model, since storage by itself cannot deliver edge-side intelligent service processing, search handling, or containerized application deployment. Therefore, while integrated and multi-role server combinations are valid Lite Edge configuration ideas, a standalone storage server is not considered a Lite Edge configuration scenario.

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 .

The correct answer is A because WDR is the standard method for handling scenes with strong contrast between bright background light and a darker foreground target. In a backlit scenario, the camera must balance very bright and very dark regions at the same time. If this is not handled correctly, the foreground subject appears too dark. WDR addresses exactly this problem by improving detail retention in both bright and dark parts of the scene.

This is consistent with the exposure principle emphasized in the material, which states that “T-Shot, Huawei’s patented exposure technology, enables optimal imaging of targets and vehicles at the same time” and explains that “Traditional exposure technology cannot correctly expose targets, motor vehicles, and non-motorized vehicles in one image” . That same engineering logic is what makes WDR the right correction for backlit darkness. By contrast, HLC is mainly used to suppress overly bright highlights such as headlights, not to brighten a dark foreground in a backlit scene. FoV and focal length affect framing and magnification, not dynamic-range correction. Therefore, A is the correct answer.

The correct answer is D because the statement itself is inconsistent with the standard IVS3800 plane naming logic. In IVS deployment, network design is performed per network plane , and the material distinguishes service and management connectivity, for example by referencing service TOR and management TOR , and by using terms such as external service IP address and external management floating IP address for upper-layer connection planning . This clearly supports standard plane naming such as Outer_Service , Inner_Service , and Outer_OM .

The option D uses “InnerJDM” , which does not match the naming pattern established by the rest of the network design terminology. In professional IVS planning, this is most likely a typographical corruption of Inner_OM . Since the question asks which statement is false, the incorrect plane name makes D the false statement as written. The other options are structurally consistent with IVS network-plane design principles, where core predefined service and OM address ranges are typically kept unchanged to avoid deployment and expansion issues. Therefore, D is the false statement.