
Oracle Cloud Infrastructure 2026 Generative AI Professional
Master generative AI implementation on Oracle Cloud Infrastructure, focusing on Large Language Models, RAG architectures, and building advanced AI-driven applications.
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Sample Oracle Cloud Infrastructure 2026 Generative AI Professional exam question
Answer this question the same way you would on exam day. No sign-up needed — one random question, three tries to swap it out.
Evaluate the ingestion snippet used on OCI to build a RAG index. It indexes a whole policy manual as one embedding, and downstream prompts receive only coarse context. Which missing operation explains the poor granularity of model answers?
from langchain_community.embeddings import OCIGenAIEmbeddings
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_core.documents import Document
embedding_model = OCIGenAIEmbeddings(
model_id='cohere.embed-english-v3.0',
service_endpoint='https://inference.generativeai.uk-london-1.oci.oraclecloud.com',
compartment_id='ocid1.compartment.oc1..example'
)
pdf_full_text = 'Long policy manual text...'
doc = Document(page_content=pdf_full_text, metadata={'source': 'policy-v1.pdf'})
vector_store = OpenSearchVectorSearch.from_documents(
documents=[doc],
embedding=embedding_model,
opensearch_url='https://search-example.oci.oraclecloud.com',
index_name='policy_kb'
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