1Z0-1122-24 POPULAR EXAMS - 1Z0-1122-24 EXAM DUMPS.ZIP

1z0-1122-24 Popular Exams - 1z0-1122-24 Exam Dumps.zip

1z0-1122-24 Popular Exams - 1z0-1122-24 Exam Dumps.zip

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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
  • Intro to DL Foundations: This section covers Deep Learning (DL) is a subset of ML that focuses on neural networks with many layers, and understanding its core concepts is vital for working with complex models.
Topic 3
  • OCI Generative AI and Oracle 23ai: This section covers CI Generative AI Services that are a key component of Oracle's AI offerings, and exploring these services provides a clear understanding of how Oracle supports generative AI applications.
Topic 4
  • Intro to Generative AI & LLMs: This section is about covering generative AI which represents a powerful area of AI that involves creating new content or data. Exploring the overview of Generative AI helps in understanding its potential and applications.
Topic 5
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q12-Q17):

NEW QUESTION # 12
In machine learning, what does the term "model training" mean?

  • A. Analyzing the accuracy of a trained model
  • B. Writing code for the entire program
  • C. Performing data analysis on collected and labeled data
  • D. Establishing a relationship between input features and output

Answer: D

Explanation:
In machine learning, "model training" refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.


NEW QUESTION # 13
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

  • A. Unsupervised learning
  • B. Supervised learning
  • C. Active learning
  • D. Reinforcement learning

Answer: A

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

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

Answer: D

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 # 15
Which feature is NOT supported as part of the OCI Language service's pretrained language processing capabilities?

  • A. Language Detection
  • B. Text Generation
  • C. Sentiment Analysis
  • D. Text Classification

Answer: B

Explanation:
The OCI Language service offers several pretrained language processing capabilities, including Text Classification, Sentiment Analysis, and Language Detection. However, it does not natively support Text Generation as a part of its core language processing capabilities. Text Generation typically involves creating new content based on input prompts, which is a feature more commonly associated with models specifically designed for natural language generation.


NEW QUESTION # 16
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. Prompt Engineering adjusts the model's parameters, while Fine-tuning crafts input prompts.
  • C. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.
  • D. Both involve retraining the model, but Prompt Engineering does it more often.

Answer: C

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 # 17
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