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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionObjectives
Topic 1: 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 use cases
        • 2. Generative AI concepts and capabilities
          Topic 2: 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. Language frameworks
                    • 2. Retrieval and AI application concepts
                      Topic 3: Oracle AI and Machine Learning Services- OCI AI Services
                      • 1. Language
                        • 2. Vision
                          • 3. Document Understanding
                            • 4. Speech
                              - OCI Machine Learning Services
                              • 1. OCI Data Science and machine learning workflows
                                • 2. Machine learning capabilities in OCI
                                  - Oracle AI Stack
                                  • 1. AI infrastructure
                                    • 2. AI data and machine learning services
                                      Topic 4: Artificial Intelligence and Machine Learning Fundamentals- Deep Learning Fundamentals
                                      • 1. Convolutional and sequence models
                                        • 2. Neural networks
                                          - Machine Learning Fundamentals
                                          • 1. Supervised and unsupervised learning
                                            • 2. Machine learning models and architectures
                                              - Artificial Intelligence Concepts
                                              • 1. AI fundamentals and terminology
                                                • 2. AI use cases and applications

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                                                  Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q22-Q27):

                                                  22. Frage
                                                  What key objective does machine learning strive to achieve?

                                                  Antwort: B

                                                  Begründung:
                                                  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.


                                                  23. Frage
                                                  What would you use Oracle AI Vector Search for?

                                                  Antwort: C

                                                  Begründung:
                                                  Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .


                                                  24. Frage
                                                  What is the benefit of using embedding models in OCI Generative AI service?

                                                  Antwort: D

                                                  Begründung:
                                                  Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
                                                  The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .


                                                  25. Frage
                                                  What is the key feature of Recurrent Neural Networks (RNNs)?

                                                  Antwort: D

                                                  Begründung:
                                                  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.


                                                  26. Frage
                                                  Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

                                                  Antwort: B

                                                  Begründung:
                                                  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.


                                                  27. Frage
                                                  ......

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