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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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
Topic 2: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Convolutional Neural Networks (CNN) architectures 2. Recurrent Neural Networks, LSTMs, and sequence models
Topic 3: Generative AI and Large Language Models	15%	- Generative AI concepts 1. Embeddings, Retrieval-Augmented Generation (RAG), and LLMs 2. Transformers, prompt engineering, and fine-tuning
Topic 4: Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Reinforcement learning basics and model evaluation concepts 2. Unsupervised learning: clustering and dimensionality reduction 3. Supervised learning: regression and classification
Topic 5: AI Foundations	10%	- Artificial Intelligence basics and terminology 1. AI applications, use cases, and responsible AI principles 2. AI, Machine Learning, and Deep Learning relationship
Topic 6: OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration
Topic 7: 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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
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 focus on increasing the number of tokens while keeping the model size constant.
C. They prioritize larger model sizes to achieve better performance.
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 # 18
What does " fine-tuning " refer to in the context of OCI Generative AI service?

A. Adjusting the model parameters to improve accuracy
B. Encrypting the data for security reasons
C. Upgrading the hardware of the AI clusters
D. Doubling the neural network layers

Answer: A

Explanation:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

NEW QUESTION # 19
What is the purpose of Attention Mechanism in Transformer architecture?

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

Answer: B

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 # 20
What feature of OCI Data Science provides an interactive coding environment for building and training models?

A. Notebook sessions
B. Model catalog
C. Conda environment
D. Accelerated Data Science (ADS) SDK

Answer: A

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 # 21
In machine learning, what does the term " model training " mean?

A. Analyzing the accuracy of a trained model
B. Establishing a relationship between input features and output
C. Performing data analysis on collected and labeled data
D. Writing code for the entire program

Answer: B

Explanation:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

NEW QUESTION # 22
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