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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q23-Q28):

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

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

Answer: A

Explanation:
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.
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NEW QUESTION # 24
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

A. Sentiment Analysis
B. Text Generation
C. Text Classification
D. Language Detection

Answer: B

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
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Accelerated Data Science (ADS) SDK
B. Notebook sessions
C. Conda environment
D. Model catalog

Answer: B

Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.

NEW QUESTION # 26
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. Unsupervised learning
D. Active learning

Answer: C

Explanation:
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 .

NEW QUESTION # 27
What is the primary benefit of using the OCI Language service for text analysis?

A. It allows for text analysis at scale without machine learning expertise.
B. It requires extensive machine learning expertise to use.
C. It provides image processing capabilities.
D. It only works with structured data.

Answer: A

Explanation:
The primary benefit of using the OCI Language service for text analysis is its ability to scale text analysis without requiring users to have extensive machine learning expertise. The service abstracts the complexities of machine learning, allowing businesses to easily process and analyze large amounts of text data through pre-built models. This accessibility makes it possible for a broader range of users to leverage advanced text analysis capabilities, facilitating insights from textual data without needing to develop and train models from scratch.

NEW QUESTION # 28
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