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Oracle 1z0-1122-24 시험요강:

주제	소개
주제 1	OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
주제 2	Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
주제 3	Intro to AI Foundations: This section covers the fundamentals of AI are essential for understanding its wide-ranging impact and applications.

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최신 Oracle Cloud 1z0-1122-24 무료샘플문제 (Q21-Q26):

질문 # 21
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Speech recognition
B. Text to speech
C. Text summarization
D. Audio recording

정답：B

설명：
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 .

질문 # 22
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Reinforcement learning
B. Supervised learning
C. Active learning
D. Unsupervised learning

정답：D

설명：
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

질문 # 23
Which AI Ethics principle leads to the Responsible AI requirement of transparency?

A. Respect for human autonomy
B. Fairness
C. Explicability
D. Prevention of harm

정답：C

설명：
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.
Top of Form
Bottom of Form

질문 # 24
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Time series prediction
B. Image classification
C. Image generation
D. Text processing

정답：B

설명：
Convolutional Neural Networks (CNNs) are primarily used for image classification and other tasks involving spatial data. CNNs are particularly effective at recognizing patterns in images due to their ability to detect features such as edges, textures, and shapes across multiple layers of convolutional filters. This makes them the model of choice for tasks such as object recognition, image segmentation, and facial recognition.
CNNs are also used in other domains like video analysis and medical image processing, but their primary application remains in image classification.

질문 # 25
What is "in-context learning" in the realm of Large Language Models (LLMs)?

A. Training a model on a diverse range of tasks
B. Teaching a model through zero-shot learning
C. Modifying the behavior of a pretrained LLM permanently
D. Providing a few examples of a target task via the input prompt

정답：D

설명：
"In-context learning" in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt. This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

질문 # 26
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1z0-1122-24최신업데이트버전공부문제 & 1z0-1122-24최고품질덤프자료

Oracle 1z0-1122-24 시험요강:

1z0-1122-24최신 업데이트버전 공부문제 인증시험공부

최신 Oracle Cloud 1z0-1122-24 무료샘플문제 (Q21-Q26):

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