ShikenPASSにあるソフトウェアバージョンは、1z0-1122-26実際の試験の3つのバージョンの1つであり、当社の専門家によって設計されています。 ソフトウェアバージョンの機能は非常に特殊です。 たとえば、ソフトウェアバージョンは実際の試験環境をシミュレートできます。 1z0-1122-26試験問題を購入すると、同様の実際の試験環境を楽しむことができます。 また、教材のソフトウェアバージョンは、コンピューターの数に限定されません。 1z0-1122-26準備試験をためらわずに購入してください。多くのメリットが得られ、1z0-1122-26試験に確実に合格します。
| Section | Weight | Objectives |
|---|---|---|
| Deep Learning Foundations | 15% | - Deep Learning and neural networks
|
| Introduction to OCI AI Services | 20% | - OCI AI Service APIs
|
| OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
|
| AI Foundations | 10% | - Artificial Intelligence basics and terminology
|
| OCI AI Portfolio | 15% | - Overview of OCI AI offerings
|
| Generative AI and Large Language Models | 15% | - Generative AI concepts
|
| Machine Learning Foundations | 15% | - Machine Learning fundamentals
|
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質問 # 18
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
正解：D
解説：
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.
質問 # 19
What can Oracle Cloud Infrastructure Document Understanding NOT do?
正解：A
解説：
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
質問 # 20
Which feature of OCI Speech helps make transcriptions easier to read and understand?
正解：D
解説：
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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質問 # 21
In machine learning, what does the term " model training " mean?
正解：D
解説：
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.
質問 # 22
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?
正解：B
解説：
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.
質問 # 23
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