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NVIDIA NCP-OUSD - OpenUSD Development Certification Exam

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Question #1 (Topic: Demo Questions)

Which of these are valid types for custom attributes in OpenUSD? (Choose two.) 

A.
structs
B.
assset paths
C.
string arrays
D.
dictionaries 
Correct Answer: B, C
Explanation:
Custom attributes in OpenUSD are user-defined properties authored on prims to store additional typed data beyond predefined schema attributes. NVIDIA’s Learn OpenUSD material states that custom attributes can hold “numeric values, strings, or arrays,” and that custom attributes are created with UsdPrim::CreateAttribute(), where Sdf.ValueTypeNamesrepresentsthe attribute type. NVIDIA’s OpenUSD data type reference lists Asset with the USDA value type token asset, and StringArray with the USDA token string[], making asset paths and string arrays valid attribute value types. Option B is correct because asset path values are represented through the asset value type and are commonly used for resource references such as textures or external asset identifiers. Option C is correct because string[] is a valid array value type. Option A is incorrect because USD does not have a native struct attribute type; NVIDIA recommends namespace-prefixed attributes for mapping grouped or compound fields. Option D is incorrect in this context because dictionaries are associated with metadata/custom Data patterns, not ordinary typed custom attributes. This aligns with Customizing USD → Custom Properties, Sdf.ValueType Names, Name spaced Attributes.
Question #2 (Topic: Demo Questions)

In OpenUSD, which USDA snippet correctly uses a payload to reference an external asset while allowing deferred loading? 

A.
def "Character" (prepend payload = @character.usda@) { }
B.
def "Character" { prepend payloads = @character.usd@ }
C.
def Xform "Character" (reference = @character.usda@) { }
D.
def Xform "Character" (payload = @character.usd@) { }
Correct Answer: A
Explanation:
Option A is the correct USDA pattern because a payload is authored as prim metadata using the singular payload keyword with a list-editing operation such as prepend. NVIDIA’s Learn OpenUSD payload exercise shows the canonical USDA form: prepend payload = @./red_cube.usd@, authored on the prim declaration. It further explains that payloads are similar to references in USDA, with the important distinction that the keyword is payload rather than references. The key technical purpose of a payload is deferred loading. NVIDIA explains that payloads can compose scene description when loaded, or unload the targeted scene description beneath the payloaded prim. It also contrasts this with references, which are always composed and present on the stage, while payloads can be opened unloaded and selectively loaded later. Option B is incorrect because payloads is not the USDA keyword and it is not authored as a property inside the prim body. Option C uses a reference, not a payload, so it does not provide payload load/unload behavior. Option D is not the canonical list-op form expected here. This aligns with Composition → Reference and Payloads → Working With Payloads.
Question #3 (Topic: Demo Questions)

Which of the following statements best describes the purpose of OpenUSD file format plugins?

A.
They extend OpenUSD's functionality by allowing it to read and write from various file formats.
B.
They are only used for visualizing OpenUSD data and geometry in 3D applications.
C.
They convert OpenUSD files to other formats without any loss of data or information.
D.
They are designed to compress OpenUSD asset files for faster loading times.
Correct Answer: A
Explanation:
OpenUSD file format plugins belong under the Data Exchange topic because they expand how USD participates in interchange workflows. Their purpose is to allow OpenUSD to read, interpret, and in some cases write data using formats beyond the native USD file types such as .usd, .usda, .usdc, and .usdz. Through these plugins, external file formats can be exposed to USD as layers and can participate in composition arcs such as references, payloads, and sublayers. This allows pipelines to integrate heterogeneous asset sources while still using USD’s scene description model, composition engine, and layer-based workflows.
Option A is correct because it directly identifies the function of file format plugins: extending OpenUSD functionality for reading and writing various file formats. Option B is incorrect because visualization is only one possible downstream use of exchanged data, not the purpose of the plugin system. Option C is incorrect because translation between formats does not inherently guarantee lossless preservation of every schema, opinion, or semantic construct. Option D is incorrect because compression is not the primary role of file format plugins. This aligns with the NVIDIA OpenUSD Development Study Guide topic Data Exchange , especially file formats, connectors, and plugin-based interoperability.
Question #4 (Topic: Demo Questions)

Which of these are valid types for custom attributes in OpenUSD? (Choose two.)

A.
structs
B.
assset paths
C.
string arrays
D.
dictionaries
Correct Answer: B, C
Explanation:
Custom attributes in OpenUSD are user-defined properties authored on prims to store additional typed data beyond predefined schema attributes. NVIDIA’s Learn OpenUSD material states that custom attributes can hold “numeric values, strings, or arrays,” and that custom attributes are created with UsdPrim::CreateAttribute(), where Sdf.ValueTypeNames represents the attribute type. NVIDIA’s OpenUSD data type reference lists Asset with the USDA value type token asset, and String Array with the USDA token string[], making asset paths and string arrays valid attribute value types.
Option B is correct because asset path values are represented through the asset value type and are commonly used for resource references such as textures or external asset identifiers. Option C is correct because string[] is a valid array value type. Option A is incorrect because USD does not have a native struct attribute type; NVIDIA recommends namespace-prefixed attributes for mapping grouped or compound fields. Option D is incorrect in this context because dictionaries are associated with metadata/customData patterns, not ordinary typed custom attributes. This aligns with Customizing USD → Custom Properties, Sdf.ValueTypeNames, Name spaced Attributes.
Question #5 (Topic: Demo Questions)

When prioritizing ease of interchange and reducing dependencies between different applications in a pipeline, why might codeless schemas, schemas defined purely in .usda files, be preferred over codeful schemas, schemas with generated classes using usdGenSchema?

A.
Codeless schemas enable interchange by loading .usda definitions directly, avoiding the need to compile or link against custom schema code libraries.
B.
Codeless schemas provide more convenient, strongly-typed C++ and Python APIs for property access compared to code generated via usdGenSchema.
C.
Generated schemas cannot define fallback attribute values within their .usda definition, making interchange less consistent across applications.
D.
Only codeless schemas are added to the schema registry, allowing them to participate in property value resolution correctly.
Next Question
Correct Answer: A
Explanation:
Codeless schemas are preferred when the pipeline goal is portability, easy distribution, and reduced application coupling. NVIDIA’s Learn OpenUSD schema guidance states that schemas define data models and optional APIs for encoding and interchanging 3D and non-3D concepts, and notes a trend toward codeless schemas for easier distribution, with schemas becoming more focused on data modeling rather than behavior implementation.
Option A is correct because a codeless schema can be distributed as schema data and plugin metadata without requiring every consuming application to compile, link, or ship custom generated C++/Python schema classes. OpenUSD’s schema-generation documentation identifies codeless schemas as schemas produced without corresponding C++ classes, where only generatedSchema.usda and plugInfo.json are essential for runtime registration.
Option B is incorrect because strongly typed convenience APIs are the advantage of codeful generated schemas. Option C is incorrect because fallback values are authored in schema definitions. Option D is incorrect because codeful and codeless schemas can both participate in schema registration and value interpretation. This aligns with Customizing USD → Schemas, API Schemas, Codeless Schemas, Schema Registry, Data Modeling for Interchange .