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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
Topic 2: 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 3: 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 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: 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 6: 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 7: 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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q39-Q44):

NEW QUESTION # 39
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

A. They provide faster internet connection speeds.
B. They provide high performance compute resources for fine-tuning tasks.
C. They allow access to unlimited database resources.
D. They are free of charge for all users.

Answer: B

Explanation:
The primary advantage of using dedicated AI clusters in the Oracle Cloud Infrastructure (OCI) Generative AI service is the provision of high-performance compute resources that are specifically optimized for fine-tuning tasks. Fine-tuning is a critical step in the process of adapting pre-trained models to specific tasks, and it requires significant computational power. Dedicated AI clusters in OCI are designed to deliver the necessary performance and scalability to handle the intense workloads associated with fine-tuning large language models (LLMs) and other AI models, ensuring faster processing and more efficient training.

NEW QUESTION # 40
Which feature of OCI Speech helps make transcriptions easier to read and understand?

A. Text normalization
B. Profanity filtering
C. Audio tuning
D. Timestamping

Answer: A

Explanation:
The text normalization feature of OCI Speech helps make transcriptions easier to read and understand by converting spoken language into a more standardized and grammatically correct format. This process includes correcting grammar, punctuation, and formatting, ensuring that the transcribed text is clear, accurate, and suitable for various use cases. Text normalization enhances the usability of transcriptions, making them more accessible and easier to process in downstream applications.
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NEW QUESTION # 41
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 prioritize larger model sizes to achieve better performance.
C. They ensure that the model size, training time, and data size are balanced for optimal results.
D. They focus on increasing the number of tokens while keeping the model size constant.

Answer: C

NEW QUESTION # 42
What is " in-context learning " in the realm of Large Language Models (LLMs)?

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

Answer: B

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 # 43
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

A. Language Detection
B. Sentiment Analysis
C. Text Classification
D. Text Generation

Answer: D

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 # 44
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