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