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- Exam Code: 1z0-1122-26
- Exam Name: Oracle Cloud Infrastructure 2026 AI Foundations Associate
- Q & A: 49 Questions and Answers
- PDF Price: $59.99
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- Exam Name: Oracle Cloud Infrastructure 2026 AI Foundations Associate
- Q & A: 49 Questions and Answers
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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Topic 1: OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI |
| Topic 2: Intro to Generative AI and LLMs | 15% | - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain LLM Fine Tuning - Discuss Large Language Models Fundamentals - Explain Prompt Engineering and Instruction Tuning |
| Topic 3: Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
| Topic 4: Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) |
| Topic 5: Intro to OCI AI Services | 20% | - OCI Speech - OCI Document Understanding - OCI Vision - OCI Language |
| Topic 6: Intro to ML Foundations | 15% | - Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Topic 7: Get started with OCI AI Portfolio | 15% | - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI AI Services Overview - Explain Responsible AI |
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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Which feature of OCI Speech helps make transcriptions easier to read and understand?
- A. Audio tuning
- B. Text normalization
- C. Timestamping
- D. Profanity filtering
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
What role do Transformers perform in Large Language Models (LLMs)?
- A. Manually engineer features in the data before training the model
- B. Image recognition tasks in LLMs
- C. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
- D. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They ensure that the model size, training time, and data size are balanced for optimal results.
- B. They prioritize larger model sizes to achieve better performance.
- C. They disregard model size and prioritize high-quality data only.
- D. They focus on increasing the number of tokens while keeping the model size constant.
Explanation: Only visible for ValidBraindumps members. You can sign-up / login (it's free).
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They ensure that the model size, training time, and data size are balanced for optimal results.
- B. They prioritize larger model sizes to achieve better performance.
- C. They disregard model size and prioritize high-quality data only.
- D. They focus on increasing the number of tokens while keeping the model size constant.
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