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1Z0-1127-25 Practice Question: Building LLM Applications with RAG and Vector Search

Network Topology
oci generative-ai inference embedded-modelmodel-id cohere.embed-multilingual-light-v3.0input-text "Hello world"truncate ENDRefer to the exhibit.```"data": {"embeddings": [[0.023, -0.045, 0.012, ...]]

An OCI CLI command above returns embeddings for the phrase 'Hello world'. The developer notices that the embedding vector length is 384 dimensions. However, they expected 768 dimensions. What is the most likely cause?

⚠ Common exam trap

Oracle often tests the misconception that embedding dimension is dynamically determined by input length or CLI flags, when in fact it is a static property of the chosen model.

Answer choices

Why each option matters

Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.

Correct answer & explanation

The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.

The Cohere model 'cohere.embed-multilingual-light-v3.0' is specifically designed to output 384-dimensional embeddings. The developer's expectation of 768 dimensions likely stems from familiarity with larger models like 'cohere.embed-english-v3.0', which outputs 1024 dimensions, or other models that produce 768-dimensional vectors. The embedding dimension is a fixed property of the model, not influenced by input length or CLI display settings.

Answer analysis

Option-by-option breakdown

For each option: why learners choose it and why it is or isn't the right answer here.

  • The input text 'Hello world' is too short, causing dimension reduction.

    Why it's wrong here

    Input length does not affect vector dimension.

  • The CLI result is truncated in the display.

    Why it's wrong here

    The displayed embedding length is complete; the JSON would contain all dimensions.

  • The model 'cohere.embed-multilingual-light-v3.0' outputs 384-dimensional vectors.

    Why this is correct

    This specific model produces 384 dimensions; the 'light' version is smaller.

  • The --truncate END flag reduces the dimension.

    Why it's wrong here

    Truncate dictates how input text is handled, not output dimensions.

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