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>> 1Z0-1122-25 Fragen Und Antworten <<

Oracle 1Z0-1122-25 Prüfungsaufgaben - 1Z0-1122-25 Online Prüfungen

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Oracle 1Z0-1122-25 Prüfungsplan:

Thema
Einzelheiten

Thema 1

Intro to DL Foundations: This section assesses the expertise of Deep Learning Engineers in understanding deep learning frameworks and architectures. It covers fundamental concepts of deep learning, introduces convolutional neural networks (CNN) for image processing, and explores sequence models like recurrent neural networks (RNN) and long short-term memory (LSTM) networks for handling sequential data.

Thema 2

Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.

Thema 3

OCI Generative AI and Oracle 23ai: This section evaluates the skills of Cloud AI Architects in utilizing Oracle’s generative AI capabilities. It includes a deep dive into OCI Generative AI services, Autonomous Database Select AI for enhanced data intelligence and Oracle Vector Search for efficient information retrieval in AI-driven applications.

Thema 4

Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.

Oracle Cloud Infrastructure 2025 AI Foundations Associate 1Z0-1122-25 Prüfungsfragen mit Lösungen (Q11-Q16):

11. Frage
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By automating data extraction from documents
B. By generating lifelike speech from documents
C. By analyzing sentiment in text documents
D. By transcribing spoken language

Antwort: A

Begründung:
Explanation:

12. Frage
What role do Transformers perform in Large Language Models (LLMs)?

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

Antwort: B

Begründung:
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.

13. Frage
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They provide faster internet connection speeds.
B. They provide high performance compute resources for fine-tuning tasks.
C. They allow access to unlimited database resources.
D. They are free of charge for all users.

Antwort: B

Begründung:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

14. Frage
What are Convolutional Neural Networks (CNNs) primarily used for?

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

Antwort: C

Begründung:
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.

15. Frage
What is "in-context learning" in the realm of Large Language Models (LLMs)?

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

Antwort: A

Begründung:
"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.

16. Frage
......

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