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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| Topic 2: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| Topic 3: Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Topic 4: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| Topic 5: Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
| Topic 6: Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Topic 7: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
Oracle 1z0-1122-26인증시험도 어려울 뿐만 아니라 신청 또한 어렵습니다.Oracle 1z0-1122-26시험은 IT업계에서도 권위가 있고 직위가 있으신 분들이 응시할 수 있는 시험이라고 알고 있습니다. 우리 KoreaDumps에서는Oracle 1z0-1122-26관련 학습가이드를 제동합니다. KoreaDumps 는 우리만의IT전문가들이 만들어낸Oracle 1z0-1122-26관련 최신, 최고의 자료와 학습가이드를 준비하고 있습니다. 여러분의 편리하게Oracle 1z0-1122-26응시하는데 많은 도움이 될 것입니다.
질문 # 26
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
정답：B
설명：
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.
질문 # 27
Emma is developing a customer support chatbot for an e-commerce website. The chatbot needs to provide accurate and up-to-date return policies, which change frequently. She initially tries fine-tuning but finds that the model still uses outdated information. Which approach should Emma use instead?
정답：A
설명：
Retrieval-Augmented Generation is the appropriate approach when a chatbot must answer using information that changes frequently. Oracle defines RAG as a technique that retrieves information from specific external data sources and augments an LLM ' s response with that retrieved context, producing grounded answers.
Oracle Docs Oracle further explains that RAG can incorporate information that is more current than the model
' s original training data and that knowledge repositories can be continually updated without retraining the underlying LLM. Oracle Docs Fine-tuning is better suited to adapting model behavior or specialization, not continuously changing factual information. Prompt engineering and zero-shot prompting control how instructions are presented but do not independently supply current return-policy data. Therefore, RAG is the correct solution for providing accurate, current, organization-specific policy responses.
질문 # 28
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
정답：D
설명：
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
질문 # 29
Which AI domain can be employed for identifying patterns in images and extract relevant features?
정답：A
설명：
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.
질문 # 30
What would you use Oracle AI Vector Search for?
정답：B
설명：
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 .
질문 # 31
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