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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q41-Q46):

                                NEW QUESTION # 41
                                Which capability is supported by Oracle Cloud Infrastructure Language service?

                                Answer: D

                                Explanation:
                                Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


                                NEW QUESTION # 42
                                Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                Answer: D

                                Explanation:
                                The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


                                NEW QUESTION # 43
                                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 # 44
                                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 # 45
                                What is the primary purpose of reinforcement learning?

                                Answer: D

                                Explanation:
                                Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


                                NEW QUESTION # 46
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