1Z0-1127-25
Free Oracle Cloud Infrastructure 2025 Generative AI Professional Practice Test
12 exam-style questions with full explanations — no sign-up. Score yourself, then close your gaps with the full course.
Exam fee ~$2454 exam domainsLevel Advanced2 timed practice tests in the course
✅ Free practice — no sign-up📝 Real exam-style questions💡 Detailed explanations💸 30-day money-back via Udemy
A developer is building an application on OCI Generative AI service that must return highly consistent, repeatable answers to factual questions, with minimal variation in wording across identical prompts. Which decoding parameter adjustment BEST achieves this?
Temperature controls randomness in token selection by scaling the probability distribution before sampling. A value near 0 makes the model almost always pick the highest-probability token, producing deterministic, repeatable output.
A machine learning team wants to fine-tune a Cohere Command foundation model on their own labeled dataset using OCI Generative AI Service and then serve it for production inference. Which hosting option MUST they provision to run the fine-tuning job?
Fine-tuning a pretrained foundation model in OCI Generative AI Service is only supported on a dedicated AI cluster provisioned in fine-tuning mode; on-demand mode only supports inference against base pretrained models.
A retail company wants a chatbot that answers customer questions using only the content of its internal product manuals, and it must minimize hallucinated answers without retraining the base model. Which approach BEST meets these requirements?
RAG retrieves relevant, up-to-date passages from the manuals at query time and injects them into the prompt, grounding the answer in actual content and reducing hallucination without any retraining.
An enterprise support team wants a conversational assistant that maintains multi-turn context, retrieves answers from an internal document repository, AND calls an internal REST API to check live order status before responding. Which OCI Generative AI implementation pattern BEST fits this requirement?
The Agents service is designed for exactly this pattern: it manages conversational session state and can orchestrate multiple tools (RAG over a knowledge base plus an API/action tool) within one agent, which a plain RAG pipeline or a fine-tuned model cannot do on its own.
A financial services company needs its generative AI assistant to answer questions using the company's internal policy documents, which are updated weekly. The assistant must cite the specific document a fact came from, and the team cannot retrain a model every week. Which approach BEST meets these requirements?
RAG retrieves current, relevant document chunks from a vector store at query time and grounds the response in them, allowing citation of source documents without retraining, and naturally handles frequently changing content.
An architect provisions a dedicated AI cluster in OCI Generative AI Service to host a custom fine-tuned model for a production workload. What is the minimum billing commitment applied to that cluster?
OCI bills dedicated AI clusters with a minimum commitment of 744 unit-hours per cluster, equivalent to running continuously for 31 days, regardless of actual usage time.
A team building a RAG pipeline on OCI wants to store document embeddings alongside existing relational business data and perform semantic similarity search using SQL. Which Oracle capability should they use?
Oracle Database 23ai introduces the AI Vector Search capability, which adds a native VECTOR data type and similarity search operators so embeddings can be stored and queried with SQL directly alongside relational data.
Which two data source types does the OCI Generative AI Agents service natively support when creating a knowledge base for RAG-based grounding?
OCI Generative AI Agents knowledge bases are created from either an OCI Object Storage bucket containing supported document formats or an OCI Search with OpenSearch index, which the service uses for retrieval during RAG.
OCI Generative AI service's Cohere Command models generate text token-by-token, predicting each next token based only on previously generated tokens, without a separate input-encoding stage. Which transformer architecture type is this?
Decoder-only architectures (like GPT-style and Cohere Command models) autoregressively generate output tokens one at a time, conditioning each prediction on prior tokens, without a separate encoder stage — ideal for open-ended text generation.
A team has only about 100 labeled examples and needs to customize a pretrained chat model quickly and at low compute cost using OCI Generative AI Service fine-tuning. Which fine-tuning method BEST fits this requirement?
T-Few (Few-shot Parameter Efficient Fine-Tuning) is designed for smaller training datasets and updates a small subset of transformer layers, making it fast and inexpensive compared to full fine-tuning.
While designing document chunking for a RAG knowledge base, an engineer sets chunk size extremely large (e.g., entire chapters per chunk) with minimal overlap. What is the most likely consequence at retrieval time?
Oversized chunks mix multiple topics together, so the embedding represents a blended meaning; even when retrieved correctly, the chunk carries excess irrelevant text that dilutes the prompt and can push the assembled context past the model's context window.
You are configuring an OCI Generative AI Agent endpoint that customers will call repeatedly to have a back-and-forth conversation. Which endpoint setting must you enable so the agent retains conversational context across multiple calls?
Enabling sessions on an agent endpoint is what allows the service to persist and reuse prior turns of the conversation, giving the agent multi-turn context awareness for subsequent requests on the same session ID.
Ready for the real thing?
The full course has two full-length practice tests, video lessons for every exam domain, hands-on labs and detailed answer explanations.
Start my full course on Udemy →1Z0-1127-25 exam — quick answers
How much does the 1Z0-1127-25 exam cost?
The exam fee is approximately $245 and varies by region — confirm current pricing with the certification vendor before you book.
What topics are on the exam?
It covers 4 domains: Fundamentals of Large Language Models (25%), Using the OCI Generative AI Service (25%), Implement RAG using the OCI Generative AI Service (25%), Using the OCI Generative AI Agents Service (25%). The full course has a dedicated chapter, lab and practice-test coverage for each.
Is this practice test really free?
Yes — all questions on this page are free with explanations and no sign-up. The paid Udemy course adds two full-length timed exams, video lessons and hands-on labs.
Will this prepare me for the real exam?
The questions mirror the real exam's style and are mapped to the official domains. This is exam-focused preparation — combine the free test with the full course's timed simulations to gauge your readiness.