First-hand Answers 1z0-1122-26 Real Questions - Oracle Exam 1z0-1122-26 Training: Oracle Cloud Infrastructure 2026 AI Foundations Associate

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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q37-Q42):

                                NEW QUESTION # 37
                                What is the key feature of Recurrent Neural Networks (RNNs)?

                                Answer: A

                                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 # 38
                                What is the primary benefit of using Oracle Cloud Infrastructure Supercluster for AI workloads?

                                Answer: B

                                Explanation:
                                Oracle Cloud Infrastructure Supercluster is designed to deliver exceptional performance and scalability for complex AI tasks. The primary benefit of this infrastructure is its ability to handle demanding AI workloads, offering high-performance computing (HPC) capabilities that are crucial for training large-scale AI models and processing massive datasets. The architecture of the Supercluster ensures low-latency networking, efficient resource allocation, and high-throughput processing, making it ideal for AI tasks that require significant computational power, such as deep learning, data analytics, and large-scale simulations.


                                NEW QUESTION # 39
                                What is the benefit of using embedding models in OCI Generative AI service?

                                Answer: A

                                Explanation:
                                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 .


                                NEW QUESTION # 40
                                You are working on a multilingual public announcement system. Which AI task will you use to implement it?

                                Answer: D

                                Explanation:
                                For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
                                Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .


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

                                Answer: D

                                Explanation:
                                The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.


                                NEW QUESTION # 42
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