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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: OpenUSD Development (NCP-OUSD) |
| Exam Number: | NCP-OUSD |
| Exam Format: | Scenario-based questions, Multiple choice, Online proctored exam |
| Certificate Validity Period: | 2 years |
| Exam Duration: | 120 minutes |
| Real Exam Qty: | 60–70 |
| Related Certifications: | NVIDIA-Certified Associate (NCA) Generative AI LLM NVIDIA-Certified Professional Agentic AI NVIDIA-Certified Professional AI Infrastructure |
| Exam Price: | $200 USD |
| Available Languages: | English |
| Recommended Training: | OpenUSD Development Learning Modules Learn OpenUSD Training Path (NVIDIA) |
| Exam Registration: | Official NVIDIA Certification Page NCP-OUSD Exam Registration |
| Sample Questions: | NVIDIA NCP-OUSD Sample Questions |
| Exam Way: | Online proctored exam (remote delivery via Certiverse platform) |
| Pre Condition: | 2–3 years of experience with OpenUSD and Python or C++ programming, or completion of official NVIDIA study materials |
| Official Syllabus URL: | https://www.nvidia.com/en-eu/learn/certification/openusd-development-professional/ |
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NEW QUESTION # 32
When USD computes the position of a PointInstancer instance, what additional position-related information is used beyond the position information for a given instance?
Answer: A
Explanation:
A PointInstancer instance position is not computed from the per-instance positions array alone. NVIDIA's Learn OpenUSD point-instancing lesson explains that authored positions are in the local coordinate space of the PointInstancer. For the final world-space transform, USD combines multiple transforms: first the prototype root transform , then the instance-specific transform components such as scales[i], orientations[i]
, and positions[i], and finally the transform authored on the PointInstancer prim itself.
Option B is correct because it includes both additional transform sources that affect the resulting placement:
the prototype root's transform and the PointInstancer prim's transform. This matters in production asset aggregation because prototypes may carry their own local placement or orientation, while the instancer prim may be moved, rotated, or scaled as a whole. Option A is incomplete because it incorrectly discards the prototype root transform. Option C is incorrect because it treats instance positions as isolated data and ignores the composed transform stack. This aligns with Content Aggregation # Asset Modularity and Instancing # PointInstancer, Prototype Roots, Instance Transforms, and Transform Composition .
NEW QUESTION # 33
Which of the following statements about OpenUSD plugin development are true? Choose two.
Answer: A,D
Explanation:
OpenUSD's plugin architecture is a major customization mechanism. Option A is correct because NVIDIA's Learn OpenUSD Data Exchange material states that file format plugins allow OpenUSD to compose with additional file formats and non-file-based sources, translating the source format on the fly as a USD layer. It also notes that file format plugins may be bidirectional, supporting both reading and writing. ( docs.nvidia.
com )
Option B is also correct because custom plugins can extend OpenUSD beyond built-in behavior. NVIDIA's OpenUSD plugin samples include schemas, both codeful and codeless, file format plugins, dynamic payloads, and Hydra scene indices, demonstrating plugin-based extension of data models and runtime behavior. ( github.
com )
Option C is not the best general statement for OpenUSD development certification in this context. Some tooling-level plugin systems can use Python, but core extensibility such as schema libraries and file format plugins commonly depends on plugin metadata and compiled libraries or generated schema artifacts. Option D is false because plugins are discovered through plugin registration metadata, such as plugInfo.json, and do not require recompiling USD itself. This aligns with Customizing USD # Plugins, Schemas, File Format Plugins, plugInfo.json, and Extensibility .
NEW QUESTION # 34
Why might you choose a payload instead of a reference in rare cases?
Answer: C
Explanation:
Payloads are weaker than references and support deferred loading, which can be beneficial in specific workflows.
NEW QUESTION # 35
Why should custom internal taxonomies rarely be exposed directly as kinds?
Answer: D
Explanation:
Model hierarchy rules are fundamental to USD composition, and mixing internal taxonomies can create invalid hierarchies.
NEW QUESTION # 36
You are setting up an outdoor scene with realistic lighting and want to simulate the effect of the sun. Which UsdLux light type is most appropriate for this purpose, providing a directional light source with parallel rays?
Answer: D
Explanation:
The correct UsdLux light type is DistantLight . NVIDIA's Learn OpenUSD lighting guide identifies UsdLux.
DistantLight as a schema where light is emitted from a distant source along the negative Z axis, commonly known as a directional light. This makes it the most appropriate light type for representing sunlight in an outdoor scene.
Option C is correct because sunlight is effectively modeled as coming from an extremely distant source, so its rays are treated as broadly parallel across the scene. The OpenUSD DistantLight schema documentation explicitly describes it as typically used for distant, broad light sources like sunlight, where rays affect the entire scene and are roughly parallel. It also notes that the inputs:angle attribute controls angular diameter; the fallback value approximates the Sun's apparent angular size from Earth.
Option A , RectLight, is better suited for rectangular area emitters such as panels or windows. Option B , DomeLight, is commonly used for environment or HDRI lighting. Option D , SphereLight, represents local emission from a spherical source, such as a bulb. This aligns with Visualization # UsdLux, Lighting Schemas, DistantLight, Directional Lighting, and Outdoor Illumination .
NEW QUESTION # 37
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