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| Section | Weight | Objectives |
|---|
| Deep Learning Foundations | 15% | - Deep Learning and neural networks
- 1. Convolutional Neural Networks (CNN) architectures
- 2. Recurrent Neural Networks, LSTMs, and sequence models
|
| Machine Learning Foundations | 15% | - Machine Learning fundamentals
- 1. Reinforcement learning basics and model evaluation concepts
- 2. Supervised learning: regression and classification
- 3. Unsupervised learning: clustering and dimensionality reduction
|
| Generative AI and Large Language Models | 15% | - Generative AI concepts
- 1. Transformers, prompt engineering, and fine-tuning
- 2. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs
|
| AI Foundations | 10% | - Artificial Intelligence basics and terminology
- 1. AI, Machine Learning, and Deep Learning relationship
- 2. AI applications, use cases, and responsible AI principles
|
| OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
- 1. Generative AI capabilities on OCI
- 2. Oracle 23ai Vector Database integration
|
| Introduction to OCI AI Services | 20% | - OCI AI Service APIs
- 1. OCI Language, Vision, Speech, and Document Understanding services
- 2. OCI Select AI and AI service use cases
|
| OCI AI Portfolio | 15% | - Overview of OCI AI offerings
- 1. AI Services, ML Services, and AI Infrastructure overview
- 2. OCI Data Science and GPU-based compute infrastructure
|
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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q20-Q25):
NEW QUESTION # 20
What is the benefit of using embedding models in OCI Generative AI service?
- A. They enable creating detailed graphics.
- B. They optimize the use of computational resources.
- C. They facilitate semantic searches.
- D. They simplify managing databases.
Answer: C
Explanation:
Embedding models in the OCI Generative AI service are designed to represent text, phrases, or other data types in a dense vector space, where semantically similar items are located closer to each other. This representation enables more effective semantic searches, where the goal is to retrieve information based on the meaning and context of the query, rather than just exact keyword matches.
The benefit of using embedding models is that they allow for more nuanced and contextually relevant searches. For example, if a user searches for " financial reports, " an embedding model can understand that " quarterly earnings " is semantically related, even if the exact phrase does not appear in the document. This capability greatly enhances the accuracy and relevance of search results, making it a powerful tool for handling large and diverse datasets .
NEW QUESTION # 21
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?
- A. Sentiment Analysis
- B. Text Classification
- C. Text Generation
- D. Language Detection
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 # 22
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Classify documents into different types
- B. Extract tables from documents
- C. Extract text from documents
- D. Generate transcript from documents
Answer: D
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 # 23
Which feature is NOT available as part of OCI Speech capabilities?
- A. Provides timestamped, grammatically accurate transcriptions
- B. Uses extensive data science experience to operate
- C. Transcribes audio and video files into text
- D. Supports multiple languages including English, Spanish, and Portuguese
Answer: B
Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.
NEW QUESTION # 24
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 # 25
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