NEW ORACLE 1Z0-1122-24 EXAM LABS & 1Z0-1122-24 VALID EXAM BRAINDUMPS

New Oracle 1z0-1122-24 Exam Labs & 1z0-1122-24 Valid Exam Braindumps

New Oracle 1z0-1122-24 Exam Labs & 1z0-1122-24 Valid Exam Braindumps

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q40-Q45):

NEW QUESTION # 40
Which feature of OCI Speech helps make transcriptions easier to read and understand?

  • A. Audio tuning
  • B. Text normalization
  • C. Timestamping
  • D. Profanity filtering

Answer: B

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 41
What is the primary purpose of reinforcement learning?

  • A. Making predictions from labeled data
  • B. Finding relationships within data sets
  • C. Identifying patterns in data
  • D. Learning from outcomes to make decisions

Answer: D

Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.


NEW QUESTION # 42
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

  • A. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
  • B. Both involve retraining the model, but Prompt Engineering does it more often.
  • C. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
  • D. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.

Answer: A

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model's internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
Fine-tuning, on the other hand, refers to the process of retraining a pretrained model on a smaller, task-specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively "specializing" the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.


NEW QUESTION # 43
Which AI domain can be employed for identifying patterns in images and extract relevant features?

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

Answer: C

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.


NEW QUESTION # 44
Which is NOT a category of pretrained foundational models available in the OCI Generative AI service?

  • A. Translation models
  • B. Chat models
  • C. Embedding models
  • D. Generation 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 # 45
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