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| Section | Weight | Objectives |
|---|
| Topic 1: 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
|
| Topic 2: 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
|
| Topic 3: OCI Generative AI and Oracle 23ai | 10% | - OCI Generative AI Service features
- 1. Oracle 23ai Vector Database integration
- 2. Generative AI capabilities on OCI
|
| Topic 4: Introduction to OCI AI Services | 20% | - OCI AI Service APIs
- 1. OCI Select AI and AI service use cases
- 2. OCI Language, Vision, Speech, and Document Understanding services
|
| Topic 5: Machine Learning Foundations | 15% | - Machine Learning fundamentals
- 1. Supervised learning: regression and classification
- 2. Unsupervised learning: clustering and dimensionality reduction
- 3. Reinforcement learning basics and model evaluation concepts
|
| Topic 6: OCI AI Portfolio | 15% | - Overview of OCI AI offerings
- 1. OCI Data Science and GPU-based compute infrastructure
- 2. AI Services, ML Services, and AI Infrastructure overview
|
| Topic 7: Deep Learning Foundations | 15% | - Deep Learning and neural networks
- 1. Recurrent Neural Networks, LSTMs, and sequence models
- 2. Convolutional Neural Networks (CNN) architectures
|
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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q30-Q35):
NEW QUESTION # 30
What can Oracle Cloud Infrastructure Document Understanding NOT do?
- A. Extract tables from documents
- B. Generate transcript from documents
- C. Classify documents into different types
- D. Extract text from documents
Answer: B
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 # 31
Which capability is supported by the Oracle Cloud Infrastructure Vision service?
- A. Analyzing historical data for unusual patterns
- B. Detecting and preventing fraud in financial transactions
- C. Generating realistic images from text
- D. Detecting vehicle number plates to issue speed citations
Answer: D
Explanation:
The Oracle Cloud Infrastructure (OCI) Vision service is designed for image analysis tasks, which includes the capability to detect and recognize objects, such as vehicle number plates. This functionality is particularly useful for applications such as automated enforcement of traffic laws, where the system can identify vehicles exceeding speed limits and issue citations based on the detected number plates. This capability leverages advanced computer vision techniques to process and analyze visual data, making it suitable for applications in public safety, transportation, and law enforcement.
NEW QUESTION # 32
What is the benefit of using embedding models in OCI Generative AI service?
- A. They facilitate semantic searches.
- B. They enable creating detailed graphics.
- C. They optimize the use of computational resources.
- D. They simplify managing databases.
Answer: A
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 # 33
What would you use Oracle AI Vector Search for?
- A. Manage database security protocols.
- B. Query data based on keywords.
- C. Query data based on semantics.
- D. Store business data in a cloud database.
Answer: C
Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .
NEW QUESTION # 34
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?
- A. To detect specific features in the input image.
- B. To reduce the spatial dimensions of the input image.
- C. To generate new images.
- D. To classify the input image.
Answer: A
Explanation:
A convolutional layer is designed to learn local visual features from an input image. During training, small learnable filters move across the image and respond to patterns such as edges, corners, textures, and progressively more complex structures. The resulting feature maps preserve useful spatial relationships while transforming raw pixels into representations that later layers can use. Oracle documentation recognizes convolutional neural networks as suitable neural-network architectures for visual data and identifies architectures such as ResNet for processing images. Oracle Docs The convolutional layer itself does not primarily generate images or make the final classification decision. Reducing spatial dimensions is normally performed through pooling or strided operations. Therefore, detecting specific features in the input image is the correct function.
NEW QUESTION # 35
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