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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Generative AI and Oracle 23ai	10%	- Discuss Oracle Vector Search - Discuss Autonomous Database Select AI - Describe OCI Generative AI Services
Topic 2: Intro to AI Foundations	10%	- Discuss AI Applications and Types of Data - Discuss AI Basics - Explain AI vs ML vs DL
Topic 3: Intro to DL Foundations	15%	- Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals
Topic 4: Intro to ML Foundations	15%	- Discuss Supervised Learning Fundamentals 1. Regression 2. Classification - Explain Machine Learning Basics - Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals
Topic 5: Intro to Generative AI and LLMs	15%	- Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Explain Transformers Fundamentals
Topic 6: Get started with OCI AI Portfolio	15%	- Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview - Discuss OCI ML Services Overview
Topic 7: Intro to OCI AI Services	20%	- OCI Language - OCI Speech - OCI Document Understanding - OCI Vision

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q19-Q24):

NEW QUESTION # 19
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 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 disregard model size and prioritize high-quality data only.

Answer: A

NEW QUESTION # 20
What key objective does machine learning strive to achieve?

A. Enabling computers to learn and improve from experience
B. Improving computer hardware
C. Creating algorithms to solve complex problems
D. Explicitly programming computers

Answer: A

Explanation:
The key objective of machine learning is to enable computers to learn from experience and improve their performance on specific tasks over time. This is achieved through the development of algorithms that can learn patterns from data and make decisions or predictions without being explicitly programmed for each task.
As the model processes more data, it becomes better at understanding the underlying patterns and relationships, leading to more accurate and efficient outcomes.

NEW QUESTION # 21
What is the purpose of Attention Mechanism in Transformer architecture?

A. Convert tokens into numerical forms (vectors) that the model can understand.
B. Apply a specific function to each word individually.
C. Weigh the importance of different words within a sequence and understand the context.
D. Break down a sentence into smaller pieces called tokens.

Answer: C

Explanation:
The purpose of the Attention Mechanism in Transformer architecture is to weigh the importance of different words within a sequence and understand the context. In essence, the attention mechanism allows the model to focus on specific parts of the input sequence when producing an output, which is crucial for understanding context and maintaining coherence over long sequences. It does this by assigning different weights to different words in the sequence, enabling the model to capture relationships between words that are far apart and to emphasize relevant parts of the input when generating predictions.
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NEW QUESTION # 22
What can Oracle Cloud Infrastructure Document Understanding NOT do?

A. Classify documents into different types
B. Generate transcript from documents
C. Extract text from documents
D. Extract tables 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 # 23
What is " in-context learning " in the realm of Large Language Models (LLMs)?

A. Training a model on a diverse range of tasks
B. Teaching a model through zero-shot learning
C. Modifying the behavior of a pretrained LLM permanently
D. Providing a few examples of a target task via the input prompt

Answer: D

Explanation:
" In-context learning " in the realm of Large Language Models (LLMs) refers to the ability of these models to learn and adapt to a specific task by being provided with a few examples of that task within the input prompt.
This approach allows the model to understand the desired pattern or structure from the given examples and apply it to generate the correct outputs for new, similar inputs. In-context learning is powerful because it does not require retraining the model; instead, it uses the examples provided within the context of the interaction to guide its behavior.

NEW QUESTION # 24
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