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Last Updated: Sep 20, 2026
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A certificate does more than decorate a resume — it widens your choices. Preparing for the 1z0-1127-24 exam with TopExamCollection's 66+ Q&As covering the Oracle Cloud Infrastructure 2024 Generative AI Professional objectives is a practical step toward more opportunities and a broader future.
Oracle 1z0-1127-24 Exam Overview:
| Certification Vendor: | Oracle |
|---|---|
| Exam Name: | Oracle Cloud Infrastructure 2024 Generative AI Professional |
| Exam Number: | 1Z0-1127-24 |
| Exam Format: | Multiple Choice, Multiple Select |
| Available Languages: | English |
| Exam Duration: | 90 minutes |
| Exam Price: | $245 USD |
| Passing Score: | 68% |
| Real Exam Qty: | 60 |
| Related Certifications: | Oracle Cloud Infrastructure Certified AI Professional |
| Sample Questions: | DOWNLOAD DEMO |
| Exam Way: | Online proctored or in-person testing center |
| Pre Condition: | Recommended: Basic knowledge of cloud computing concepts, familiarity with AI/ML fundamentals, and understanding of Oracle Cloud Infrastructure |
| Official Syllabus URL: | https://education.oracle.com/oracle-cloud-infrastructure-2024-generative-ai-professional |
Oracle 1z0-1127-24 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Introduction to Generative AI | 15% | - Key terminology and vocabulary - Business applications and use cases - Generative AI concepts and definitions - History and evolution of generative AI models - Key components of generative AI systems |
| Retrieval Augmented Generation (RAG) | 20% | - Evaluation metrics for RAG systems - RAG architecture and components - Document chunking strategies - Retrieval optimization techniques - Implementing RAG on OCI - Vector databases and embeddings |
| OCI AI Services Integration | 10% | - Multi-modal AI capabilities - End-to-end solution architecture - Integration with OCI Language - Integration with OCI Vision |
| Fine-tuning and Model Customization | 10% | - Fine-tuning concepts and approaches - Training data preparation - Model evaluation and validation - Fine-tuning with OCI tools - Hyperparameter tuning |
| Oracle Cloud Infrastructure (OCI) Generative AI Service | 25% | - API usage and integration patterns - Pricing and resource management - Available models and model selection - Security and access control in OCI Gen AI - Service deployment and configuration - OCI Generative AI service architecture |
| Prompt Engineering | 20% | - Prompt optimization for specific tasks - Prompt design principles and best practices - Prompt templates and patterns - Few-shot and zero-shot prompting techniques - Iterative prompt refinement - Chain of thought prompting |
Before You Book the 1z0-1127-24 Exam: Key Facts
Immediately after checkout, your download link appears on screen and an automatic email with the materials lands in your inbox in about a minute. You can install the software on as many devices as you need, and if the email has not arrived within two hours, our support team will step in. Every purchase includes 365 days of free updates — each revision is compiled in step with current exam trends and emailed to you automatically — followed by a 50% renewal discount when the period ends.
A professional team, for one thing. Our experts compile the 1z0-1127-24 bank in step with current exam trends, every answer is expert-verified, and there are no amateurs in the group — career-elite advisors also stand by with progress advice when you need direction. Just as important is the relationship: we stay in close contact with users, fold their feedback into every revision, and back each Oracle Cloud Infrastructure 2024 Generative AI Professional purchase with a free demo, 365 days of free updates, and a clearly stated refund policy.
Oracle sets the following prerequisites for the Oracle Cloud Infrastructure 2024 Generative AI Professional: Recommended: Basic knowledge of cloud computing concepts, familiarity with AI/ML fundamentals, and understanding of Oracle Cloud Infrastructure.
Confirm the current requirements on the official certification page before booking.
The Oracle Cloud Infrastructure 2024 Generative AI Professional blueprint is organized into these main domains:
- Retrieval Augmented Generation (RAG) (20%)
- Introduction to Generative AI (15%)
- Fine-tuning and Model Customization (10%)
Further domains complete the official outline, and our question bank spans all of them.
We take responsibility for our materials. If you fail the corresponding exam within 60 days of purchase, send us a scanned copy of your enrollment slip and your official Score Report PDF within two days of the exam date, and we will process a full refund within seven days of verification. Please note the exclusions: exams taken within three days of purchase are not eligible, the candidate name must match the payer, and free or expired products are not covered. If you would rather switch direction, we can exchange your product for two others of equal value instead.
According to the latest exam information, the 1z0-1127-24 exam contains 60 questions and runs for 90 minutes minutes. Timing yourself during practice sessions is a smart habit — pacing problems sink more candidates than knowledge gaps.
At present, the passing score for the 1z0-1127-24 exam is 68% and the registration fee is $245 USD. Both figures are set by Oracle and can change, so double-check them on the official site when you register.
Oracle Cloud Infrastructure 2024 Generative AI Professional Sample Questions:
Which is a characteristic of T-Few fine-tuning for Large Language Models (LLMs)?
- A. It does not update any weights but restructures the model architecture.
- B. It increases the training time as compared to Vanilla fine-tuning.
- C. It updates all the weights of the model uniformly.
- D. It selectively updates only a fraction of the model's weights.
Correct Answer: D 🗳️
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How does the architecture of dedicated Al clusters contribute to minimizing GPU memory overhead forT- Few fine-tuned model inference?
- A. By sharing base model weights across multiple fine-tuned model's on the same group of GPUs
- B. By optimizing GPIJ memory utilization for each model's unique para
- C. By loading the entire model into G PU memory for efficient processing
- D. By allocating separate GPUS for each model instance
Correct Answer: A 🗳️
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Which is NOT a built-in memory type in LangChain?
- A. Conversation Buffer Memory
- B. Conversation Summary Memory
- C. Conversation ImgeMemory
- D. Conversation Token Buffer Memory
Correct Answer: C 🗳️
Explanation: Only visible for TopExamCollection members. You can sign-up / login (it's free).
Which is a distinguishing feature of "Parameter-Efficient Fine-tuning (PEFT)" as opposed to classic Tine- tuning" in Large Language Model training?
- A. PEFT does not modify any parameters but uses soft prompting with unlabeled data. PEFT modifies
- B. PEFT involves only a few or new parameters and uses labeled, task-specific data.
- C. PEFT parameters and b typically used when no training data exists.
- D. PEFT modifies all parameters and uses unlabeled, task-agnostic data.
Correct Answer: B 🗳️
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Which statement best describes the role of encoder and decoder models in natural language processing?
- A. Encoder models are used only for numerical calculations, whereas decoder models are used to interpret the calculated numerical values back into text.
- B. Encoder models convert a sequence of words into a vector representation, and decoder models take this vector representation to sequence of words.
- C. Encoder models take a sequence of words and predict the next word in the sequence, whereas decoder models convert a sequence of words into a numerical representation.
- D. Encoder models and decoder models both convert sequence* of words into vector representations without generating new text.
Correct Answer: B 🗳️
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