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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Oracle AI and Machine Learning Services	- Oracle AI Stack 1. AI data and machine learning services 2. AI infrastructure - OCI AI Services 1. Document Understanding 2. Vision 3. Language 4. Speech - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI
Topic 2: Artificial Intelligence and Machine Learning Fundamentals	- Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks - Artificial Intelligence Concepts 1. AI use cases and applications 2. AI fundamentals and terminology - Machine Learning Fundamentals 1. Machine learning models and architectures 2. Supervised and unsupervised learning
Topic 3: Generative AI and Large Language Models	- Large Language Models 1. LLM fundamentals 2. Language models and generative AI applications - Generative AI Fundamentals 1. Generative AI concepts and capabilities 2. Generative AI use cases
Topic 4: OCI Generative AI and Oracle Database AI Capabilities	- AI Application Frameworks 1. Language frameworks 2. Retrieval and AI application concepts - Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database - OCI Generative AI 1. Generative AI models and applications 2. OCI Generative AI services

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q27-Q32):

NEW QUESTION # 27
Which feature is NOT available as part of OCI Speech capabilities?

A. Provides timestamped, grammatically accurate transcriptions
B. Uses extensive data science experience to operate
C. Supports multiple languages including English, Spanish, and Portuguese
D. Transcribes audio and video files into text

Answer: B

Explanation:
OCI Speech capabilities are designed to be user-friendly and do not require extensive data science experience to operate. The service provides features such as transcribing audio and video files into text, offering grammatically accurate transcriptions, supporting multiple languages, and providing timestamped outputs.
These capabilities are built to be accessible to a broad range of users, making speech-to-text conversion seamless and straightforward without the need for deep technical expertise.

NEW QUESTION # 28
You are training a deep learning model to classify images. What is the primary function of the convolutional layer?

A. To generate new images.
B. To reduce the spatial dimensions of the input image.
C. To classify the input image.
D. To detect specific features in the input image.

Answer: D

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 # 29
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They focus on increasing the number of tokens while keeping the model size constant.
B. They prioritize larger model sizes to achieve better performance.
C. They disregard model size and prioritize high-quality data only.
D. They ensure that the model size, training time, and data size are balanced for optimal results.

Answer: D

NEW QUESTION # 30
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. ML is a subset of AI, and DL is a subset of ML.
B. AI is a subset of DL, which is a subset of ML.
C. AI, ML, and DL are entirely separate fields with no overlap.
D. DL is a subset of AI, and ML is a subset of DL.

Answer: A

Explanation:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.

NEW QUESTION # 31
What is a key advantage of using dedicated AI clusters in the OCI Generative AI service?

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

Answer: A

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 # 32
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