Oracle - 1z0-1122-26 - Fantastic Latest Oracle Cloud Infrastructure 2026 AI Foundations Associate Test Pdf

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Oracle 1z0-1122-26 Exam Syllabus Topics:

SectionWeightObjectives
Generative AI and Large Language Models15%- Generative AI concepts
  • 1. Transformers, prompt engineering, and fine-tuning
    • 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
      AI Foundations10%- Artificial Intelligence basics and terminology
      • 1. AI applications, use cases, and responsible AI principles
        • 2. AI, Machine Learning, and Deep Learning relationship
          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
                  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
                        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
                            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

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                                Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q22-Q27):

                                NEW QUESTION # 22
                                What key objective does machine learning strive to achieve?

                                Answer: C

                                Explanation:
                                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.


                                NEW QUESTION # 23
                                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 # 24
                                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 # 25
                                You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?

                                Answer: D

                                Explanation:
                                In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
                                Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.


                                NEW QUESTION # 26
                                You are working on a multilingual public announcement system. Which AI task will you use to implement it?

                                Answer: C

                                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 # 27
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