Oracle 1z0-1122-24 Hot Questions, 1z0-1122-24 Real Brain Dumps
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Oracle 1z0-1122-24 Exam Syllabus Topics:
Topic
Details
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.
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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q29-Q34):
NEW QUESTION # 29
Which is NOT a capability of OCI Vision's image analysis?
- A. Translating text in images to another language
- B. Object detection with bounding boxes
- C. Assigning classification labels to images
- D. Locating and extracting text in images
Answer: A
Explanation:
OCI Vision's image analysis capabilities include locating and extracting text from images, assigning classification labels to images, and detecting objects with bounding boxes. However, translating text in images to another language is not a capability of OCI Vision's image analysis. This functionality typically requires an additional layer of processing, such as integration with a language translation service, which is beyond the scope of OCI Vision's core image analysis features.
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NEW QUESTION # 30
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They ensure that the model size, training time, and data size are balanced for optimal results.
- B. They focus on increasing the number of tokens while keeping the model size constant.
- C. They disregard model size and prioritize high-quality data only.
- D. They prioritize larger model sizes to achieve better performance.
Answer: A
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 # 31
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?
- A. Directly predicting the final output
- B. Capturing the internal representation of the raw image data
- C. Storing the input pixel values
- D. Providing labels for the output neurons
Answer: B
Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.
NEW QUESTION # 32
Which AI Ethics principle leads to the Responsible AI requirement of transparency?
- A. Explicability
- B. Prevention of harm
- C. Respect for human autonomy
- D. Fairness
Answer: A
NEW QUESTION # 33
Which capability is supported by Oracle Cloud Infrastructure Language service?
- A. Analyzing text to extract structured information like sentiment or entities
- B. Detecting objects and scenes in images
- C. Converting text into images
- D. Translating text into speech
Answer: A
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 # 34
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