Reliable 1z0-1122-26 Dumps Pdf & Latest 1z0-1122-26 Dumps Questions

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Topic 1: 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
      Topic 2: OCI Generative AI and Oracle 23ai10%- OCI Generative AI Service features
      • 1. Oracle 23ai Vector Database integration
        • 2. Generative AI capabilities on OCI
          Topic 3: Deep Learning Foundations15%- Deep Learning and neural networks
          • 1. Convolutional Neural Networks (CNN) architectures
            • 2. Recurrent Neural Networks, LSTMs, and sequence models
              Topic 4: 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
                  Topic 5: 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 6: 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 7: 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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q48-Q53):

                                NEW QUESTION # 48
                                Which capability is supported by Oracle Cloud Infrastructure Language service?

                                Answer: A

                                Explanation:
                                Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


                                NEW QUESTION # 49
                                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?

                                Answer: B

                                Explanation:
                                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.


                                NEW QUESTION # 50
                                Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

                                Answer: A

                                Explanation:
                                The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


                                NEW QUESTION # 51
                                How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

                                Answer: B

                                Explanation:
                                In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
                                * Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
                                * Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
                                Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


                                NEW QUESTION # 52
                                Which feature of OCI Speech helps make transcriptions easier to read and understand?

                                Answer: B

                                Explanation:
                                The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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                                NEW QUESTION # 53
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