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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q15-Q20):
NEW QUESTION # 15
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?
- A. Translation models
- B. Generation models
- C. Embedding models
- D. Chat models
Answer: A
Explanation:
The OCI Generative AI service offers various categories of pretrained foundational models, including Embedding models, Chat models, and Generation models. These models are designed to perform a wide range of tasks, such as generating text, answering questions, and providing contextual embeddings. However, Translation models, which are typically used for converting text from one language to another, are not a category available in the OCI Generative AI service's current offerings. The focus of the OCI Generative AI service is more aligned with tasks related to text generation, chat interactions, and embedding generation rather than direct language translation.
NEW QUESTION # 16
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Active learning
- B. Unsupervised learning
- C. Reinforcement learning
- D. Supervised learning
Answer: B
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 # 17
What is "in-context learning" in the realm of Large Language Models (LLMs)?
- A. Teaching a model through zero-shot learning
- B. Training a model on a diverse range of tasks
- C. Providing a few examples of a target task via the input prompt
- D. Modifying the behavior of a pretrained LLM permanently
Answer: C
Explanation:
"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.
NEW QUESTION # 18
What is the purpose of Attention Mechanism in Transformer architecture?
- A. Convert tokens into numerical forms (vectors) that the model can understand.
- B. Weigh the importance of different words within a sequence and understand the context.
- C. Break down a sentence into smaller pieces called tokens.
- D. Apply a specific function to each word individually.
Answer: B
Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 19
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?
- A. Language
- B. Document Understanding
- C. Vision
- D. Speech
Answer: D
Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the "Speech" service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.
NEW QUESTION # 20
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