TEST 1Z0-1122-24 PREP & NEW APP 1Z0-1122-24 SIMULATIONS

Test 1z0-1122-24 Prep & New APP 1z0-1122-24 Simulations

Test 1z0-1122-24 Prep & New APP 1z0-1122-24 Simulations

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to OCI AI Services: This section is about exploring OCI AI Services and their related APIs, such as those for Language, Vision, Document Understanding, and Speech, which are essential for developers and businesses looking to integrate AI into their operations.
Topic 2
  • Get Started with OCI AI Portfolio: This section is about the OCI AI Portfolio which offers a comprehensive suite of services and infrastructure for developing and deploying AI models. Exploring the overview of OCI AI Services provides insight into the tools available for AI development.
Topic 3
  • Intro to ML Foundations: This section covers Machine Learning (ML) which is a critical area within AI, and understanding its fundamentals is crucial for anyone interested in this field. The section covers delving into the basics of ML allowing for a better grasp of how machines learn from data.

Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q41-Q46):

NEW QUESTION # 41
What distinguishes Generative AI from other types of AI?

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

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

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

Answer: D

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
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

  • A. They prioritize larger model sizes to achieve better performance.
  • B. They disregard model size and prioritize high-quality data only.
  • C. They focus on increasing the number of tokens while keeping the model size constant.
  • D. They ensure that the model size, training time, and data size are balanced for optimal results.

Answer: D

Explanation:
Large Language Models (LLMs) handle the trade-off between model size, data quality, data size, and performance by balancing these factors to achieve optimal results. Larger models typically provide better performance due to their increased capacity to learn from data; however, this comes with higher computational costs and longer training times. To manage this trade-off effectively, LLMs are designed to balance the size of the model with the quality and quantity of data used during training, and the amount of time dedicated to training. This balanced approach ensures that the models achieve high performance without unnecessary resource expenditure.


NEW QUESTION # 44
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. Active learning
  • D. Unsupervised learning

Answer: D

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 # 45
What is the primary benefit of using the OCI Language service for text analysis?

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

Answer: B

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 # 46
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