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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics |
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Document Understanding - OCI Vision - OCI Language |
| Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Explain Responsible AI |
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
| Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
| Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning - Explain LLM Fine Tuning |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services |
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Embedding models
- B. Translation models
- C. Chat models
- D. Generation models
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How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They prioritize larger model sizes to achieve better performance.
- C. They ensure that the model size, training time, and data size are balanced for optimal results.
- D. They focus on increasing the number of tokens while keeping the model size constant.
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
- A. Speech
- B. Document Understanding
- C. Vision
- D. Language
Explanation: Only visible for DumpsTorrent members. You can sign-up / login (it's free).
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
- A. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
- B. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
- C. Both involve retraining the model, but Prompt Engineering does it more often.
- D. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
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What does " fine-tuning " refer to in the context of OCI Generative AI service?
- A. Upgrading the hardware of the AI clusters
- B. Doubling the neural network layers
- C. Encrypting the data for security reasons
- D. Adjusting the model parameters to improve accuracy
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