1Z0-1127-25 OCI Generative AI Service Practice Question
A data scientist needs to generate embeddings for a collection of documents to be used for both clustering and semantic search. They want to use appropriate input types for each task. Which TWO input types should they use from the Cohere Embed API? (Choose two.)
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
✓
search_document
For clustering, the 'clustering' input type is appropriate. For semantic search, 'search_document' (for documents to be searched) and 'search_query' (for queries) are used. The question asks for two options that cover both tasks; 'clustering' and 'search_document' are correct. 'search_query' is for queries, not documents, and 'classification' is for classification tasks.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
search_query
Why it's wrong here
Used for queries, not documents.
- ✗
embedding
Why it's wrong here
Not a valid input type; the API uses specific types like search_document, search_query, classification, clustering.
- ✓
search_document
Why this is correct
Used for documents in a search corpus.
- ✗
classification
Why it's wrong here
Used for classification tasks, not clustering or search.
- ✓
clustering
Why this is correct
Used for clustering documents.
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Same concept, more angles
1 more way this is tested on 1Z0-1127-25
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Variation 1. Which input type should be used with the Cohere Embed API when generating embeddings for a query in a semantic search system?
easy- A.search_document
- B.clustering
- ✓ C.search_query
- D.classification
Why C: The Cohere Embed API uses the `search_query` input type specifically for embedding search queries in a semantic search system. This tells the model to optimize the embedding for matching against documents that were embedded with the `search_document` type, ensuring proper alignment in the vector space for retrieval tasks.
JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
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