Pass Guaranteed Quiz 2025 Oracle Fantastic 1z0-1122-24 Dumps Vce
Pass Guaranteed Quiz 2025 Oracle Fantastic 1z0-1122-24 Dumps Vce
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Oracle Cloud Infrastructure 2024 AI Foundations Associate Sample Questions (Q15-Q20):
NEW QUESTION # 15
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?
- A. Active learning
- B. Unsupervised learning
- C. Supervised learning
- D. Reinforcement learning
Answer: B
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 # 16
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?
- A. They allow access to unlimited database resources.
- B. They are free of charge for all users.
- C. They provide high performance compute resources for fine-tuning tasks.
- D. They provide faster internet connection speeds.
Answer: C
Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.
NEW QUESTION # 17
You are working on a multilingual public announcement system. Which AI task will you use to implement it?
- A. Audio recording
- B. Speech recognition
- C. Text summarization
- D. Text to speech
Answer: D
Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is "Text to Speech" (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .
NEW QUESTION # 18
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?
- A. They disregard model size and prioritize high-quality data only.
- B. They prioritize larger model sizes to achieve better performance.
- 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 # 19
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract text from documents
- B. Classify documents into different types
- C. Generate transcript from documents
- D. Extract tables from documents
Answer: C
Explanation:
Oracle Cloud Infrastructure (OCI) Document Understanding service offers several capabilities, including extracting tables, classifying documents, and extracting text. However, it does not generate transcripts from documents. Transcription typically refers to converting spoken language into written text, which is a function associated with speech-to-text services, not document understanding services. Therefore, generating a transcript is outside the scope of what OCI Document Understanding is designed to do .
NEW QUESTION # 20
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