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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: Generative AI and Large Language Models15%- Generative AI concepts
  • 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
    • 2. Transformers, prompt engineering, and fine-tuning
      Topic 2: 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
          Topic 3: 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
              Topic 4: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
              • 1. Generative AI capabilities on OCI
                • 2. Oracle 23ai Vector Database integration
                  Topic 5: AI Foundations10%- Artificial Intelligence basics and terminology
                  • 1. AI, Machine Learning, and Deep Learning relationship
                    • 2. AI applications, use cases, and responsible AI principles
                      Topic 6: Machine Learning Foundations15%- Machine Learning fundamentals
                      • 1. Reinforcement learning basics and model evaluation concepts
                        • 2. Supervised learning: regression and classification
                          • 3. Unsupervised learning: clustering and dimensionality reduction
                            Topic 7: Deep Learning Foundations15%- Deep Learning and neural networks
                            • 1. Recurrent Neural Networks, LSTMs, and sequence models
                              • 2. Convolutional Neural Networks (CNN) architectures

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q45-Q50):

                                NEW QUESTION # 45
                                What is the key feature of Recurrent Neural Networks (RNNs)?

                                Answer: D

                                Explanation:
                                Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
                                RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
                                In contrast:
                                * Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
                                * Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
                                * Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
                                This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by " remembering " past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.


                                NEW QUESTION # 46
                                In machine learning, what does the term " model training " mean?

                                Answer: C

                                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 # 47
                                You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

                                Answer: A

                                Explanation:
                                For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.


                                NEW QUESTION # 48
                                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 # 49
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Answer: B


                                NEW QUESTION # 50
                                ......

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