Oracle 1z0-1122-26 Testing Engine - 1z0-1122-26 Deutsch Prüfung

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
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
      AI Foundations10%- Artificial Intelligence basics and terminology
      • 1. AI, Machine Learning, and Deep Learning relationship
        • 2. AI applications, use cases, and responsible AI principles
          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
                    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
                            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 1z0-1122-26 Prüfungsfragen mit Lösungen (Q37-Q42):

                                37. Frage
                                A customer feedback analysis team needs to extract insights from thousands of online product reviews. They want to determine whether customers express positive or negative opinions about different aspects of a product or service. Which feature of OCI Language is helpful?

                                Antwort: B

                                Begründung:
                                OCI Language provides pretrained natural language processing capabilities for analyzing unstructured text, including sentiment analysis. In this scenario, the requirement is specifically to determine whether customers express positive or negative opinions concerning individual aspects of a product or service. Oracle ' s OCI Language customer-feedback architecture describes extracting aspects from reviews and associating sentiments such as positive, negative, or neutral with those aspects. Oracle Docs Key phrase extraction identifies important topics or phrases, named entity recognition identifies entities such as organizations, people, or locations, and language detection determines the language in which content is written. None directly evaluates customer opinion. Sentiment analysis therefore precisely matches the business requirement because it transforms subjective review content into structured sentiment information that can be analyzed at scale.


                                38. Frage
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Antwort: B

                                Begründung:
                                Explicability is the AI Ethics principle that leads to the Responsible AI requirement of transparency. This principle emphasizes the importance of making AI systems understandable and interpretable to humans.
                                Transparency is a key aspect of explicability, as it ensures that the decision-making processes of AI systems are clear and comprehensible, allowing users to understand how and why a particular decision or output was generated. This is critical for building trust in AI systems and ensuring that they are used responsibly and ethically.
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                                39. Frage
                                Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?

                                Antwort: B

                                Begründung:
                                OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
                                Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
                                Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.


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

                                Antwort: C

                                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.


                                41. Frage
                                Which AI Ethics principle leads to the Responsible AI requirement of transparency?

                                Antwort: B


                                42. Frage
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