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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                1z0-1122-26 Valid Test Question | 1z0-1122-26 Simulation Questions

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q24-Q29):

                                NEW QUESTION # 24
                                What is the primary benefit of using the OCI Language service for text analysis?

                                Answer: C

                                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 # 25
                                Which feature is NOT available as part of OCI Speech capabilities?

                                Answer: C

                                Explanation:
                                OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
                                These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.


                                NEW QUESTION # 26
                                Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

                                Answer: C

                                Explanation:
                                Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.


                                NEW QUESTION # 27
                                Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

                                Answer: D

                                Explanation:
                                Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
                                * Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.


                                NEW QUESTION # 28
                                What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

                                Answer: A

                                Explanation:
                                In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.


                                NEW QUESTION # 29
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