1Z0-1122-24 ACTUAL QUESTIONS - VALID 1Z0-1122-24 TEST BOOK

1z0-1122-24 Actual Questions - Valid 1z0-1122-24 Test Book

1z0-1122-24 Actual Questions - Valid 1z0-1122-24 Test Book

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Oracle 1z0-1122-24 Exam Syllabus Topics:

TopicDetails
Topic 1
  • 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 2
  • 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 3
  • 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.

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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q28-Q33):

NEW QUESTION # 28
Which capability is supported by Oracle Cloud Infrastructure Language service?

  • A. Detecting objects and scenes in images
  • B. Analyzing text to extract structured information like sentiment or entities
  • C. Converting text into images
  • D. Translating text into speech

Answer: B

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.


NEW QUESTION # 29
What key objective does machine learning strive to achieve?

  • A. Creating algorithms to solve complex problems
  • B. Explicitly programming computers
  • C. Improving computer hardware
  • D. Enabling computers to learn and improve from experience

Answer: D

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task. As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.


NEW QUESTION # 30
How is "Prompt Engineering" different from "Fine-tuning" in the context of Large Language Models (LLMs)?

  • A. Both involve retraining the model, but Prompt Engineering does it more often.
  • B. Prompt Engineering modifies training data, while Fine-tuning alters the model's structure.
  • 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 # 31
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. Establishing a relationship between input features and output
  • D. Performing data analysis on collected and labeled data

Answer: C

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 # 32
What are Convolutional Neural Networks (CNNs) primarily used for?

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

Answer: C

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 # 33
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