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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Machine Learning Foundations15%- Machine Learning fundamentals
  • 1. Supervised learning: regression and classification
    • 2. Reinforcement learning basics and model evaluation concepts
      • 3. Unsupervised learning: clustering and dimensionality reduction
        Generative AI and Large Language Models15%- Generative AI concepts
        • 1. Transformers, prompt engineering, and fine-tuning
          • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
            Introduction to OCI AI Services20%- OCI AI Service APIs
            • 1. OCI Select AI and AI service use cases
              • 2. OCI Language, Vision, Speech, and Document Understanding services
                Deep Learning Foundations15%- Deep Learning and neural networks
                • 1. Convolutional Neural Networks (CNN) architectures
                  • 2. Recurrent Neural Networks, LSTMs, and sequence models
                    OCI AI Portfolio15%- Overview of OCI AI offerings
                    • 1. OCI Data Science and GPU-based compute infrastructure
                      • 2. AI Services, ML Services, and AI Infrastructure overview
                        OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
                        • 1. Generative AI capabilities on OCI
                          • 2. Oracle 23ai Vector Database integration
                            AI Foundations10%- Artificial Intelligence basics and terminology
                            • 1. AI, Machine Learning, and Deep Learning relationship
                              • 2. AI applications, use cases, and responsible AI principles

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q26-Q31):

                                NEW QUESTION # 26
                                What feature of OCI Data Science provides an interactive coding environment for building and training models?

                                Answer: A

                                Explanation:
                                In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.


                                NEW QUESTION # 27
                                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?

                                Answer: B

                                Explanation:
                                OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.


                                NEW QUESTION # 28
                                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 # 29
                                How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

                                Answer: B

                                Explanation:
                                Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


                                NEW QUESTION # 30
                                How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

                                Answer: A

                                Explanation:
                                In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
                                * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
                                * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
                                Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


                                NEW QUESTION # 31
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