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| Certification Vendor: | NVIDIA |
|---|---|
| Exam Name: | NVIDIA-Certified Professional: OpenUSD Development (NCP-OUSD) |
| Exam Number: | NCP-OUSD |
| Available Languages: | English |
| Exam Duration: | 120 minutes |
| Exam Price: | $200 USD |
| Related Certifications: | NVIDIA-Certified Associate (NCA) Generative AI LLM NVIDIA-Certified Professional AI Infrastructure NVIDIA-Certified Professional Agentic AI |
| Certificate Validity Period: | 2 years |
| Real Exam Qty: | 60–70 |
| Exam Format: | Multiple choice, Online proctored exam, Scenario-based questions |
| Recommended Training: | OpenUSD Development Learning Modules Learn OpenUSD Training Path (NVIDIA) |
| Exam Registration: | NCP-OUSD Exam Registration Official NVIDIA Certification Page |
| 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 # 36
What is an instance proxy in USD?
Answer: D
Explanation:
An instance proxy represents a prim inside an instance, even though it does not actually exist in the scenegraph. It behaves like the prototype's prim but appears under the instance path.
NEW QUESTION # 37
Which API can move multiple prims into /World in one shot?
Answer: A
Explanation:
Batch namespace edits support safe repathing/reparenting.
NEW QUESTION # 38
Which Python method is used to add a payload to a prim?
Answer: C
Explanation:
The GetPayloads().AddPayload() method is used to add payloads in Python.
NEW QUESTION # 39
What does "sparse override" mean in USD?
Answer: B
Explanation:
Sparse overrides ensure only differences are written, keeping layers lightweight and non-destructive.
NEW QUESTION # 40
Why is extract, transform, load (ETL) a useful design pattern for USD data exchange? Choose two.
Answer: B,C
Explanation:
ETL is useful for USD data exchange because it separates the problem into disciplined phases instead of forcing extraction, interpretation, optimization, and client-specific restructuring into one converter step.
NVIDIA's Learn OpenUSD data exchange guidance describes a two-phase approach, extract and transform, inspired by ETL. The extract phase should translate source data to OpenUSD as directly as possible, mapping source concepts to USD concepts to preserve the integrity and structure of the original data. The transform phase then applies optional changes such as user export options, content-structure changes, and optimizations for workflow or client performance.
Option B is correct because this separation of concerns makes converters easier to maintain, test, and adapt.
Option D is correct because the pattern preserves source fidelity first, then permits controlled tailoring for specific downstream needs. Option A is incorrect because NVIDIA explicitly notes that data exchange is typically lossy and not every data model maps directly. Option C is incorrect because the guide states there is no single content structure suitable for every organization or workflow. This aligns with Data Exchange # Two-Phase Data Exchange, Data Extraction, Data Transformation, Export Options, and Pipeline Adaptability .
NEW QUESTION # 41
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