ORACLE - 1Z0-1122-24 - ORACLE CLOUD INFRASTRUCTURE 2024 AI FOUNDATIONS ASSOCIATE–RELIABLE VALID TEST BRAINDUMPS

Oracle - 1z0-1122-24 - Oracle Cloud Infrastructure 2024 AI Foundations Associate–Reliable Valid Test Braindumps

Oracle - 1z0-1122-24 - Oracle Cloud Infrastructure 2024 AI Foundations Associate–Reliable Valid Test Braindumps

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q37-Q42):

NEW QUESTION # 37
What role do Transformers perform in Large Language Models (LLMs)?

  • A. Provide a mechanism to process sequential data in parallel and capture long-range dependencies
  • B. Limit the ability of LLMs to handle large datasets by imposing strict memory constraints
  • C. Image recognition tasks in LLMs
  • D. Manually engineer features in the data before training the model

Answer: A

Explanation:
Transformers play a critical role in Large Language Models (LLMs), like GPT-4, by providing an efficient and effective mechanism to process sequential data in parallel while capturing long-range dependencies. This capability is essential for understanding and generating coherent and contextually appropriate text over extended sequences of input.
Sequential Data Processing in Parallel:
Traditional models, like Recurrent Neural Networks (RNNs), process sequences of data one step at a time, which can be slow and difficult to scale. In contrast, Transformers allow for the parallel processing of sequences, significantly speeding up the computation and making it feasible to train on large datasets.
This parallelism is achieved through the self-attention mechanism, which enables the model to consider all parts of the input data simultaneously, rather than sequentially. Each token (word, punctuation, etc.) in the sequence is compared with every other token, allowing the model to weigh the importance of each part of the input relative to every other part.
Capturing Long-Range Dependencies:
Transformers excel at capturing long-range dependencies within data, which is crucial for understanding context in natural language processing tasks. For example, in a long sentence or paragraph, the meaning of a word can depend on other words that are far apart in the sequence. The self-attention mechanism in Transformers allows the model to capture these dependencies effectively by focusing on relevant parts of the text regardless of their position in the sequence.
This ability to capture long-range dependencies enhances the model's understanding of context, leading to more coherent and accurate text generation.
Applications in LLMs:
In the context of GPT-4 and similar models, the Transformer architecture allows these models to generate text that is not only contextually appropriate but also maintains coherence across long passages, which is a significant improvement over earlier models. This is why the Transformer is the foundational architecture behind the success of GPT models.
Reference:
Transformers are a foundational architecture in LLMs, particularly because they enable parallel processing and capture long-range dependencies, which are essential for effective language understanding and generation.


NEW QUESTION # 38
What distinguishes Generative AI from other types of AI?

  • A. Generative AI uses algorithms to predict outcomes based on past data.
  • B. Generative AI involves training models to perform tasks without human intervention.
  • C. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
  • D. Generative AI focuses on making decisions based on user interactions.

Answer: C

Explanation:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.


NEW QUESTION # 39
What are Convolutional Neural Networks (CNNs) primarily used for?

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

Answer: A

Explanation:
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.


NEW QUESTION # 40
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?

  • A. Computer Vision
  • B. Natural Language Processing
  • C. Anomaly Detection
  • D. Natural Language Processing

Answer: D

Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.


NEW QUESTION # 41
What is the key feature of Recurrent Neural Networks (RNNs)?

  • A. They do not have an internal state.
  • B. They have a feedback loop that allows information to persist across different time steps.
  • C. They are primarily used for image recognition tasks.
  • D. They process data in parallel.

Answer: B

Explanation:
Recurrent Neural Networks (RNNs) are a class of neural networks where connections between nodes can form cycles. This cycle creates a feedback loop that allows the network to maintain an internal state or memory, which persists across different time steps. This is the key feature of RNNs that distinguishes them from other neural networks, such as feedforward neural networks that process inputs in one direction only and do not have internal states.
RNNs are particularly useful for tasks where context or sequential information is important, such as in language modeling, time-series prediction, and speech recognition. The ability to retain information from previous inputs enables RNNs to make more informed predictions based on the entire sequence of data, not just the current input.
In contrast:
Option A (They process data in parallel) is incorrect because RNNs typically process data sequentially, not in parallel.
Option B (They are primarily used for image recognition tasks) is incorrect because image recognition is more commonly associated with Convolutional Neural Networks (CNNs), not RNNs.
Option D (They do not have an internal state) is incorrect because having an internal state is a defining characteristic of RNNs.
This feedback loop is fundamental to the operation of RNNs and allows them to handle sequences of data effectively by "remembering" past inputs to influence future outputs. This memory capability is what makes RNNs powerful for applications that involve sequential or time-dependent data.


NEW QUESTION # 42
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