100+ Free OCI GenAI Pro Practice Questions

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Question 1

Score: 0/0

Which neural-network architecture forms the foundation of every modern large language model offered through OCI Generative AI?

Convolutional neural network

Transformer

Long short-term memory recurrent network

Gradient-boosted decision tree

to track

2026 Statistics

Key Facts: OCI GenAI Pro Exam

Exam Questions

Oracle University

90 min

Exam Duration

Oracle University

68%

Passing Score

Oracle University

$245

Exam Fee (USD)

Oracle University

3 years

Certification Validity

Oracle Certification Program

40%

Largest Domain

Using OCI Generative AI Service

As of April 30, 2026, Oracle University lists exam 1Z0-1127-25 as a 50-question, 90-minute multiple-choice exam with a 68% passing score and a $245 USD exam fee. Oracle's published 2025 objectives weight Using OCI Generative AI Service at roughly 40% and split the remaining content across Fundamentals of Large Language Models, Implementing RAG, and Using OCI Generative AI RAG Agents at roughly 20% each. The credential is valid for three years.

Sample OCI GenAI Pro Practice Questions

Try these sample questions to test your OCI GenAI Pro exam readiness. Each question includes a detailed explanation. Start the interactive quiz above for the full 100+ question experience with AI tutoring.

1Which neural-network architecture forms the foundation of every modern large language model offered through OCI Generative AI?

A.Convolutional neural network

B.Transformer

C.Long short-term memory recurrent network

D.Gradient-boosted decision tree

Explanation: All current OCI Generative AI foundation models, including Cohere Command and Meta Llama, are decoder-only or encoder-decoder Transformer architectures. CNNs are designed for spatial data, LSTMs were the dominant sequence model before Transformers, and gradient-boosted trees are a classical tabular technique.

2What is the role of the self-attention mechanism inside a Transformer decoder?

A.Replace the need for positional information in the sequence

B.Compress the input into a fixed-length vector before any computation

C.Let each token weight every other token in the context to build a contextual representation

D.Apply convolutional filters across the embedding space

Explanation: Self-attention computes query, key, and value projections so each token can attend to every other token in the context, producing position-aware contextual embeddings. It does not compress to a fixed vector, it actually requires positional encodings, and it is unrelated to convolutional filtering.

3Which statement best describes the difference between encoder-only and decoder-only Transformer architectures?

A.Decoder-only models cannot be used for retrieval, only encoder-decoder models can

B.Encoder-only models are always larger than decoder-only models

C.Encoder-only models like BERT produce bidirectional embeddings; decoder-only models like Llama generate text autoregressively

D.Encoder-only and decoder-only models share identical training objectives

Explanation: Encoder-only Transformers like BERT use masked language modelling and produce bidirectional embeddings useful for classification and retrieval. Decoder-only Transformers like Llama use causal language modelling and generate text autoregressively. Size is independent of architecture, and decoder-only models can also produce embeddings.

4An LLM has a 128k token context window. Which statement is true?

A.Context window refers to the model's training corpus size

B.Only the user prompt is limited to 128k tokens; the response can be unlimited

C.The combined prompt plus generated response cannot exceed 128k tokens

D.Tokens are equivalent to characters in this calculation

Explanation: The context window is the total number of tokens the model can process at once, including system prompt, user prompt, and generated output. It is not a separate cap on prompt vs. response, it is unrelated to the training corpus, and a token is roughly four characters in English, not one character.

5Which tokenization scheme is most commonly used by modern LLMs such as the Cohere Command and Meta Llama families on OCI?

A.One-hot character encoding

B.Whole-word tokenization

C.Byte Pair Encoding (BPE) or a BPE-based variant

D.Phoneme-level segmentation

Explanation: BPE and BPE-derived schemes like SentencePiece and tiktoken iteratively merge frequent character pairs to build a sub-word vocabulary, which keeps the vocabulary small while still representing rare words. Whole-word tokenization explodes the vocabulary, character one-hot is too granular, and phoneme segmentation belongs to speech processing.

6Which decoding parameter would you lower first to reduce hallucinations and produce more deterministic answers from an LLM?

A.Frequency penalty

B.Maximum output tokens

C.Temperature

D.Stop sequences

Explanation: Lower temperature concentrates probability mass on the highest-likelihood tokens and produces more deterministic, factual output. Maximum tokens caps length, frequency penalty discourages repeats, and stop sequences only define where generation halts.

7Which prompt engineering technique adds a small number of input-output examples directly inside the prompt to steer behaviour without training?

A.Embedding-based retrieval

B.Full fine-tuning

C.Few-shot prompting

D.Reinforcement learning from human feedback

Explanation: Few-shot prompting places worked examples in the context so the model can pattern-match the desired format. Full fine-tuning updates weights, retrieval injects external documents, and RLHF is a costly training procedure, none of which happen inside a single prompt.

8An engineer needs the LLM to show its reasoning when solving multi-step word problems. Which prompting pattern should they apply?

A.Single-token completion

B.Zero-shot classification prompting

C.Negative prompting

D.Chain-of-thought prompting

Explanation: Chain-of-thought prompts ask the model to think step by step, which improves performance on arithmetic and multi-hop reasoning. Zero-shot classification gives no examples, negative prompting tells the model what to avoid, and single-token completion is a length constraint.

9What does "parameter count" describe in the context of an LLM?

A.The number of GPUs used to serve the model

B.The number of tokens in the training corpus

C.The maximum length of the input prompt

D.The number of learnable weights in the network

Explanation: Parameter count is the total number of trainable weights, often reported in billions, and is the primary indicator of model capacity. Training-corpus size, context length, and serving fleet size are independent dimensions.

10Which fine-tuning approach is highlighted by Oracle on OCI Generative AI as a low-cost, parameter-efficient method that updates only a small set of inserted parameters?

A.Reinforcement learning from human feedback

B.Full fine-tuning

C.Continued pre-training

D.T-Few

Explanation: Oracle exposes T-Few as the parameter-efficient fine-tuning option on OCI Generative AI. T-Few updates only a small set of injected parameters and keeps the base model frozen, drastically reducing compute and memory cost. Full fine-tuning, continued pre-training, and RLHF all touch many or all base weights.

About the OCI GenAI Pro Exam

The OCI 2025 Generative AI Professional exam (1Z0-1127-25) validates an engineer's ability to design, build, and operate generative AI workloads on Oracle Cloud Infrastructure. It covers large language model fundamentals, the OCI Generative AI service for chat, embedding, and fine-tuning, retrieval-augmented generation backed by Oracle Database 23ai AI Vector Search, and OCI Generative AI Agents.

Assessment

50 multiple-choice and scenario-based questions

Time Limit

90 minutes

Passing Score

68%

Exam Fee

$245 USD (Oracle University / Oracle MyLearn)

OCI GenAI Pro Exam Content Outline

20%

Fundamentals of Large Language Models

Transformer architectures, encoder-only vs decoder-only vs encoder-decoder models, attention, tokenization (BPE), context windows, parameters, prompt engineering, few-shot and chain-of-thought patterns, fine-tuning approaches including T-Few and LoRA, and decoding controls.

40%

Using OCI Generative AI Service

Cohere Command, Command R, and Command R+ models, Meta Llama 3 family, Cohere Embed and Rerank, on-demand vs dedicated AI clusters, hosting and fine-tuning cluster units, custom model endpoints, IAM and Resource Principals, KMS encryption, VCN private endpoints, monitoring, structured output, and pricing.

20%

Implement RAG using OCI Generative AI Service

RAG pipeline design, chunking strategies, hybrid retrieval (sparse plus dense), re-ranking with Cohere Rerank, contextual compression, query rewriting, Object Storage as a data source, and Oracle Database 23ai AI Vector Search with VECTOR_DISTANCE and approximate nearest-neighbour indexes.

20%

Using OCI Generative AI RAG Agents Service

Agent fundamentals, knowledge bases backed by Object Storage and 23ai, agent endpoints, citations, tool and function calling, ReAct and planner-executor patterns, multi-agent orchestration, guardrails, evaluation, and responsible-AI controls including PII redaction and prompt-injection defence.

How to Pass the OCI GenAI Pro Exam

What You Need to Know

Passing score: 68%
Assessment: 50 multiple-choice and scenario-based questions
Time limit: 90 minutes
Exam fee: $245 USD

Keys to Passing

Complete 500+ practice questions
Score 80%+ consistently before scheduling
Focus on highest-weighted sections
Use our AI tutor for tough concepts

OCI GenAI Pro Study Tips from Top Performers

1Spend the most time on the OCI Generative AI service domain because it carries roughly 40% of the exam weight and rewards hands-on familiarity with on-demand versus dedicated cluster serving.

2Build a small RAG lab on Oracle Database 23ai AI Vector Search: ingest a few PDFs, embed with Cohere Embed, store as VECTOR, and query with VECTOR_DISTANCE.

3Memorize the Cohere portfolio (Command, Command R, Command R+, Embed English V3, Embed Multilingual V3, Rerank) and the Meta Llama family Oracle exposes on OCI Generative AI.

4Practice mapping fine-tuning options on OCI: T-Few for parameter-efficient updates and Vanilla / LoRA-style fine-tuning for deeper adaptation, and remember custom models must be served on a hosting dedicated AI cluster.

5Understand the difference between Object Storage knowledge bases and 23ai-backed knowledge bases for OCI Generative AI Agents, including when each is the right choice.

6Drill prompt engineering, chain-of-thought, query rewriting, hybrid retrieval, re-ranking, and contextual compression so you can recognize them in scenario questions.

7Lock down IAM patterns: Resource Principals, dynamic groups, and the use generative-ai-family verb for least-privilege inference access.

8Prepare for responsible-AI questions on PII redaction, content moderation, prompt-injection defence, and citation-grounded answers.

Frequently Asked Questions

What is the OCI 2025 Generative AI Professional exam?

Exam 1Z0-1127-25 is Oracle's professional-level credential for engineers who design, build, and operate generative AI workloads on Oracle Cloud Infrastructure. It tests four domains: large language model fundamentals, the OCI Generative AI service, retrieval-augmented generation on OCI, and OCI Generative AI RAG Agents.

How many questions are on 1Z0-1127-25 and how long do you get?

Oracle University lists 50 multiple-choice and scenario-based questions with a 90-minute time limit. The published passing score is 68%, and a single exam attempt is currently $245 USD.

Which domain matters most on the OCI Generative AI Professional exam?

Using OCI Generative AI Service is the largest domain at roughly 40% of the exam. Make sure you understand on-demand versus dedicated AI cluster serving, custom model endpoints, the Cohere Command and Meta Llama families, Cohere Embed and Rerank, IAM and Resource Principals, KMS encryption, and private VCN access.

How is this exam different from OCI AI Foundations?

OCI AI Foundations (1Z0-1122-25) is an associate-level overview covering AI, ML, deep learning, and the OCI AI portfolio. OCI Generative AI Professional (1Z0-1127-25) is the next step up: it focuses specifically on building production generative AI systems with OCI Generative AI, RAG on Oracle Database 23ai, and Generative AI Agents.

How long is the certification valid?

Oracle states the OCI Generative AI Professional credential is valid for three years from the date earned, after which holders must recertify by passing the latest version of the exam or by satisfying Oracle's recertification options.

What experience should you have before taking the exam?

Oracle targets the exam at software developers, machine learning and AI engineers, and generative AI professionals who already understand machine learning and deep learning basics, can write Python, and have hands-on time with OCI. Real lab work in OCI Generative AI, Oracle Database 23ai AI Vector Search, and OCI Generative AI Agents is strongly recommended.