O R A C L E C E R T I F I C A T I O N
1Z0-1127 Oracle Cloud Infrastructure Generative AI 2025 Professional Practice Exam
Exam Number: 4828 | Last updated April 19, 2026 | 875+ questions across 5 vendor-aligned objectives
The 1Z0-1127 Oracle Cloud Infrastructure Generative AI 2025 Professional exam is aimed at AI engineers and application developers who build retrieval-augmented and agentic applications on OCI Generative AI. Candidates validate mastery of prompt design, fine-tuning on dedicated AI clusters, embeddings and vector search, and the LangChain and agent patterns that wire large language models to enterprise data.
The heaviest content is LLM Fundamentals and Prompt Engineering (roughly 30%), covering model families, tokenization, temperature and top-p sampling, system prompts, and few-shot patterns. Fine-Tuning and Dedicated AI Clusters contributes another 25% with T-Few and Vanilla fine-tuning, cluster sizing, and endpoint creation.
Retrieval-Augmented Generation and Vector Search sits near 25% and drills into embeddings, chunking strategies, Oracle 23ai AI Vector Search, OCI Generative AI Agents, and the LangChain integration. Observability, Safety, and Integration rounds out the remaining weight with content moderation, logging, cost control, and integration with OCI Data Science and Autonomous Database.
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Question #1 - LLM Fundamentals and Prompt Engineering
An AI engineer wants an LLM to classify support tickets into five categories consistently, with near-deterministic output for the same input. She is currently seeing varied responses for identical prompts.
Which generation parameter should she adjust to reduce output variability?
A) Lower temperature (and narrow top-p) toward 0 for near-deterministic outputs.
B) Increase temperature toward 1 for more variety.
C) Remove the system prompt entirely.
D) Increase the maximum output length.
Show solution
Correct answers: A – Explanation:
Temperature controls sampling randomness; near-zero temperature produces near-deterministic outputs — ideal for classification. Option B increases variability. Option D affects length, not stability. Option C removes guidance and typically harms classification. Source: Check Source
Question #2 - LLM Fundamentals and Prompt Engineering
A developer is building a few-shot prompt for an LLM to format customer data into JSON. The model sometimes adds explanatory text outside the JSON block.
Which prompting technique most directly reduces extraneous text?
A) Increasing temperature.
B) System prompt instructions plus few-shot examples that show JSON-only output with no prose.
C) Using the smallest available model without adjusting the prompt.
D) Ignoring the issue; the downstream parser will handle it.
Show solution
Correct answers: B – Explanation:
Combining explicit system instructions (“Return JSON only. No prose.”) with few-shot examples that demonstrate the exact format is the most effective steering technique. Option D passes the problem downstream. Option A increases variability. Option C loses capability without fixing the prompt. Source: Check Source
Question #3 - Fine-Tuning and Dedicated AI Clusters
A bank has 3,000 labeled examples of customer complaint categorization and wants to fine-tune a base LLM. The team prioritizes low cost and faster training over the absolute best customization.
Which OCI Generative AI fine-tuning method fits?
A) Vanilla full fine-tuning requiring dedicated capacity.
B) Training a model from scratch.
C) T-Few fine-tuning, which is parameter-efficient and shares a cluster across customers.
D) No fine-tuning — always use prompting only.
Show solution
Correct answers: C – Explanation:
T-Few is a parameter-efficient fine-tuning method that is faster and cheaper, and T-Few endpoints share a cluster — matching cost preference. Option A uses more capacity and cost. Option D may underperform at this data volume. Option B is infeasible. Source: Check Source
Question #4 - Fine-Tuning and Dedicated AI Clusters
An enterprise architect is sizing an OCI Generative AI dedicated AI cluster for hosting a fine-tuned model endpoint. The workload is predictable at 50 concurrent requests and will run 24/7.
Which cluster type is most appropriate?
A) No cluster — use on-demand shared endpoints.
B) A training dedicated AI cluster only, running permanently.
C) A random compute instance running the model.
D) A hosting (inference) dedicated AI cluster sized for the expected concurrency.
Show solution
Correct answers: D – Explanation:
Dedicated AI clusters come in training and hosting types; hosting clusters are the right choice for persistent inference workloads with predictable concurrency. Option B is wrong type. Option A may not meet SLA. Option C is unsupported. Source: Check Source
Question #5 - Retrieval-Augmented Generation and Vector Search
A product team wants a chatbot that answers questions grounded in the company’s 5,000-page product manual, updated weekly. Hallucination must be minimized.
Which architectural pattern fits?
A) Retrieval-augmented generation: chunk and embed the manual, retrieve relevant chunks per query, and generate from the retrieved context.
B) Fine-tune a model once and never update.
C) Paste the full 5,000 pages into every prompt.
D) Rely entirely on LLM general knowledge with no retrieval.
Show solution
Correct answers: A – Explanation:
RAG grounds the LLM’s answers in retrieved, up-to-date chunks of the manual, minimizing hallucination while keeping content fresh. Option B cannot absorb weekly updates. Option D is the cause of hallucinations. Option C exceeds context windows. Source: Check Source
Question #6 - Retrieval-Augmented Generation and Vector Search
A RAG architect picks the chunk size for documents and embeddings. The team observes that answers are too generic and miss specific details.
Which adjustment is most likely to help?
A) Stop using embeddings.
B) Reduce chunk size and/or add a reranking step to surface the most relevant passages.
C) Remove chunks entirely and paste full documents.
D) Increase chunk size to 10x so every passage is longer.
Show solution
Correct answers: B – Explanation:
Smaller, well-scoped chunks plus a reranking stage typically surface the most relevant specific passages, improving answer specificity. Option D makes retrieval fuzzier. Option C breaks context windows. Option A removes retrieval entirely. Source: Check Source
Question #7 - LLM Fundamentals and Prompt Engineering
A developer wants to embed a corpus of 200,000 documents in OCI Generative AI, store the vectors in Oracle Database, and perform similarity search with a user query at runtime.
Which Oracle product supports the vector storage and similarity search?
A) Autonomous Database without the vector type.
B) An Excel spreadsheet of vectors.
C) Oracle Database 23ai AI Vector Search.
D) Object Storage with a manual cosine calculation in Python.
Show solution
Correct answers: C – Explanation:
Oracle Database 23ai’s AI Vector Search provides native vector types and similarity operators — the designed pattern for enterprise vector storage. Option D is unscalable. Option B is absurd at 200K docs. Option A lacks the vector type. Source: Check Source
Question #8 - Observability, Safety, and Integration
A compliance officer needs every prompt sent to and response received from a production LLM endpoint logged, with ability to search logs for policy violations.
Which OCI combination supports this?
A) No logging; trust the model.
B) A printed log on paper.
C) A spreadsheet updated manually per request.
D) Enable request/response logging on the model endpoint, writing to OCI Logging, with Logging Analytics for search.
Show solution
Correct answers: D – Explanation:
Writing endpoint request/response to OCI Logging and searching via Logging Analytics is the native observability pattern for LLM endpoints. Option A has no audit trail. Options B and C do not scale. Source: Check Source
Question #9 - Observability, Safety, and Integration
An AI safety lead wants to prevent the LLM from producing harmful content and block prompt-injection attempts from users where possible.
Which approach combines content moderation and prompt-injection defense?
A) Content moderation guardrails on input and output plus input sanitization and system-prompt isolation.
B) Disabling the model entirely.
C) Publishing a user policy document only.
D) Relying on the base model to refuse harmful requests.
Show solution
Correct answers: A – Explanation:
Layered safety — content moderation plus input sanitization and protected system prompts — is the intended defense stack against harmful outputs and prompt-injection. Option D is incomplete. Option B defeats the purpose. Option C is not a technical control. Source: Check Source
Question #10 - Retrieval-Augmented Generation and Vector Search
A developer wants an agent that calls external tools (a price lookup API and a CRM search) when the question requires current enterprise data, rather than answering from the model alone.
Which OCI Generative AI capability supports tool-using agents?
A) An embedding lookup only.
B) A plain compute instance running a bash script.
C) OCI Generative AI Agents with custom tool definitions for API and CRM calls.
D) A static few-shot prompt with no tool access.
Show solution
Correct answers: C – Explanation:
OCI Generative AI Agents support tool-using patterns where the agent invokes external APIs/services to supplement model knowledge — the designed agentic framework. Option D has no tools. Option A is retrieval, not action. Option B is outside the agent framework. Source: Check Source
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What the 1Z0 1127 OCI GenAI Pro exam measures
- LLM fundamentals and prompt engineering (30%) — pick model families, tune tokenization, temperature, and top-p, and author system prompts with few-shot examples that control output.
- Fine-tuning and dedicated AI clusters (25%) — apply T-Few or Vanilla fine-tuning, size clusters correctly, and publish endpoints for inference.
- Retrieval-augmented generation and vector search (25%) — design embeddings, chunking, and rerank flows with Oracle AI Vector Search, LangChain, and OCI Generative AI Agents.
- Observability, safety, and integration (20%) — enable content moderation, logging, cost control, and integration with OCI Data Science and Autonomous Database.
How to prepare for this exam
- Review the official 1Z0-1127 exam page to confirm the current 2025 objectives and weights.
- Complete the Oracle University OCI Generative AI Professional 2025 learning path on MyLearn.
- In an OCI tenancy, run a prompt against the Generative AI playground, build a RAG pipeline with AI Vector Search, and T-Few fine-tune a small model on a labeled dataset.
- Apply the skills at work: deploy an internal knowledge-search assistant, build a customer-support agent with LangChain, or compare a fine-tuned endpoint against a prompt-only baseline.
- Master one objective at a time, starting with LLM fundamentals and prompt engineering since it carries the most weight.
- Run PowerKram learn mode to see feedback after every question with sourced links back to Oracle documentation.
- Finish with PowerKram exam mode across all objectives until you pass three back-to-back full-length attempts.
Career paths and salary outlook
Generative AI skills command premium rates across AI and engineering roles:
- Generative AI Engineer — $140,000–$200,000 (Levels.fyi).
- Applied AI Scientist — $135,000–$190,000 (U.S. Bureau of Labor Statistics).
- AI Solutions Architect — $145,000–$195,000 (Glassdoor).
Official resources
Work through the OCI Generative AI Professional Learning Path on Oracle MyLearn. Reinforce with the OCI Generative AI documentation and the Oracle AI Vector Search User’s Guide.