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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: Artificial Intelligence and Machine Learning Fundamentals- Artificial Intelligence Concepts
  • 1. AI use cases and applications
    • 2. AI fundamentals and terminology
      - Machine Learning Fundamentals
      • 1. Supervised and unsupervised learning
        • 2. Machine learning models and architectures
          - Deep Learning Fundamentals
          • 1. Convolutional and sequence models
            • 2. Neural networks
              Topic 2: Oracle AI and Machine Learning Services- OCI AI Services
              • 1. Document Understanding
                • 2. Language
                  • 3. Vision
                    • 4. Speech
                      - Oracle AI Stack
                      • 1. AI data and machine learning services
                        • 2. AI infrastructure
                          - OCI Machine Learning Services
                          • 1. Machine learning capabilities in OCI
                            • 2. OCI Data Science and machine learning workflows
                              Topic 3: Generative AI and Large Language Models- Generative AI Fundamentals
                              • 1. Generative AI concepts and capabilities
                                • 2. Generative AI use cases
                                  - Large Language Models
                                  • 1. LLM fundamentals
                                    • 2. Language models and generative AI applications
                                      Topic 4: OCI Generative AI and Oracle Database AI Capabilities- AI Application Frameworks
                                      • 1. Retrieval and AI application concepts
                                        • 2. Language frameworks
                                          - OCI Generative AI
                                          • 1. Generative AI models and applications
                                            • 2. OCI Generative AI services
                                              - Oracle AI Database
                                              • 1. AI capabilities in Oracle Database
                                                • 2. Vector database concepts

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q13-Q18):

                                                  NEW QUESTION # 13
                                                  What is the primary benefit of using the OCI Language service for text analysis?

                                                  Answer: A

                                                  Explanation:
                                                  The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre- built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.


                                                  NEW QUESTION # 14
                                                  What does " fine-tuning " refer to in the context of OCI Generative AI service?

                                                  Answer: A

                                                  Explanation:
                                                  Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.


                                                  NEW QUESTION # 15
                                                  What is the purpose of Attention Mechanism in Transformer architecture?

                                                  Answer: D

                                                  Explanation:
                                                  The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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                                                  NEW QUESTION # 16
                                                  In machine learning, what does the term " model training " mean?

                                                  Answer: A

                                                  Explanation:
                                                  In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.


                                                  NEW QUESTION # 17
                                                  You are working on a multilingual public announcement system. Which AI task will you use to implement it?

                                                  Answer: B

                                                  Explanation:
                                                  For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
                                                  Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .


                                                  NEW QUESTION # 18
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