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Oracle 1z0-1122-26 Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions:
Which feature is NOT available as part of OCI Speech capabilities?
- A. Transcribes audio and video files into text
- B. Provides timestamped, grammatically accurate transcriptions
- C. Supports multiple languages including English, Spanish, and Portuguese
- D. Uses extensive data science experience to operate
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What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?
- A. It offers seamless integration with social media platforms.
- B. It provides a cost-effective solution for simple AI tasks.
- C. It is ideal for tasks such as text-to-speech conversion.
- D. It delivers exceptional performance and scalability for complex AI tasks.
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A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?
- A. Sentiment analysis
- B. Language detection
- C. Named entity recognition
- D. Key phrase extraction
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Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Supervised learning
- B. Reinforcement learning
- C. Unsupervised learning
- D. Active learning
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What is the primary purpose of reinforcement learning?
- A. Identifying patterns in data
- B. Learning from outcomes to make decisions
- C. Finding relationships within data sets
- D. Making predictions from labeled data
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