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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
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
              OCI Generative AI and Oracle Database AI Capabilities- Oracle AI Database
              • 1. AI capabilities in Oracle Database
                • 2. Vector database concepts
                  - OCI Generative AI
                  • 1. OCI Generative AI services
                    • 2. Generative AI models and applications
                      - AI Application Frameworks
                      • 1. Retrieval and AI application concepts
                        • 2. Language frameworks
                          Generative AI and Large Language Models- Large Language Models
                          • 1. LLM fundamentals
                            • 2. Language models and generative AI applications
                              - Generative AI Fundamentals
                              • 1. Generative AI concepts and capabilities
                                • 2. Generative AI use cases
                                  Oracle AI and Machine Learning Services- OCI AI Services
                                  • 1. Vision
                                    • 2. Language
                                      • 3. Speech
                                        • 4. Document Understanding
                                          - OCI Machine Learning Services
                                          • 1. Machine learning capabilities in OCI
                                            • 2. OCI Data Science and machine learning workflows
                                              - Oracle AI Stack
                                              • 1. AI infrastructure
                                                • 2. AI data and machine learning services

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q15-Q20):

                                                  NEW QUESTION # 15
                                                  What key objective does machine learning strive to achieve?

                                                  Answer: D

                                                  Explanation:
                                                  The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
                                                  As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


                                                  NEW QUESTION # 16
                                                  What is " in-context learning " in the realm of Large Language Models (LLMs)?

                                                  Answer: A

                                                  Explanation:
                                                  " In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
                                                  This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.


                                                  NEW QUESTION # 17
                                                  What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

                                                  Answer: A

                                                  Explanation:
                                                  The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.


                                                  NEW QUESTION # 18
                                                  You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

                                                  Answer: A

                                                  Explanation:
                                                  In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
                                                  Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.


                                                  NEW QUESTION # 19
                                                  Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                                  Answer: A

                                                  Explanation:
                                                  Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
                                                  Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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                                                  NEW QUESTION # 20
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