Oracle - 1z0-1122-26 - High Hit-Rate Oracle Cloud Infrastructure 2026 AI Foundations Associate Mock Exams

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: 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 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 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 4: 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 5: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Topic 6: 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 7: 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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q14-Q19):

NEW QUESTION # 14
What are Convolutional Neural Networks (CNNs) primarily used for?

A. Time series prediction
B. Image generation
C. Text processing
D. Image classification

Answer: D

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 # 15
What is the purpose of Attention Mechanism in Transformer architecture?

A. Apply a specific function to each word individually.
B. Weigh the importance of different words within a sequence and understand the context.
C. Break down a sentence into smaller pieces called tokens.
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 # 16
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 disregard model size and prioritize high-quality data only.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: A

NEW QUESTION # 17
What is " in-context learning " in the realm of Large Language Models (LLMs)?

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

Answer: A

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 # 18
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Active learning
B. Unsupervised learning
C. Reinforcement learning
D. Supervised learning

Answer: B

Explanation:
Unsupervised learning is a type of machine learning that focuses on understanding relationships within data without the need for labeled outcomes. Unlike supervised learning, which requires labeled data to train models to make predictions or classifications, unsupervised learning works with unlabeled data and aims to discover hidden patterns, groupings, or structures within the data.
Common applications of unsupervised learning include clustering, where the algorithm groups data points into clusters based on similarities, and association, where it identifies relationships between variables in the dataset. Since unsupervised learning does not predict outcomes but rather uncovers inherent structures, it is ideal for exploratory data analysis and discovering previously unknown patterns in data .

NEW QUESTION # 19
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