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| Section | Weight | Objectives |
|---|
| Get started with OCI AI Portfolio | 15% | - Explain Responsible AI - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Discuss OCI AI Services Overview
|
| Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals
|
| Intro to OCI AI Services | 20% | - OCI Speech - OCI Language - OCI Vision - OCI Document Understanding
|
| Intro to Generative AI and LLMs | 15% | - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals
|
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- 1. Regression
- 2. Classification
- Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics
|
| Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL
|
| OCI Generative AI and Oracle 23ai | 10% | - Describe OCI Generative AI Services - Discuss Oracle Vector Search - Discuss Autonomous Database Select AI
|
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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q44-Q49):
NEW QUESTION # 44
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
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 requires extensive machine learning expertise to use.
- C. It allows for text analysis at scale without machine learning expertise.
- D. It provides image processing capabilities.
Answer: C
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
Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?
- A. Profanity filtering
- B. Confidence scoring
- C. Batch support
- D. Timestamping
Answer: C
Explanation:
OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.
NEW QUESTION # 47
What feature of OCI Data Science provides an interactive coding environment for building and training models?
- A. Conda environment
- B. Notebook sessions
- C. Accelerated Data Science (ADS) SDK
- D. Model catalog
Answer: B
Explanation:
In OCI Data Science, Notebook sessions provide an interactive coding environment that is essential for building, training, and deploying machine learning models. These sessions allow data scientists to write and execute code in real time, offering a flexible environment for data exploration, model experimentation, and iterative development. The integration with various OCI services and support for popular machine learning frameworks further enhances the utility of Notebook sessions, making them a crucial tool in the data science workflow.
NEW QUESTION # 48
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
- A. Language Detection
- B. Text Classification
- C. Text Generation
- D. Sentiment Analysis
Answer: C
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 # 49
......
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