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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q46-Q51):

NEW QUESTION # 46
Which AI domain can be employed for identifying patterns in images and extract relevant features?

A. Computer Vision
B. Anomaly Detection
C. Natural Language Processing
D. Speech Processing

Answer: A

Explanation:
Computer Vision is the AI domain specifically employed for identifying patterns in images and extracting relevant features. This field focuses on enabling machines to interpret and understand visual information from the world, automating tasks that the human visual system can perform, such as recognizing objects, analyzing scenes, and detecting anomalies. Techniques in Computer Vision are widely used in applications ranging from facial recognition and image classification to medical image analysis and autonomous vehicles.

NEW QUESTION # 47
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

A. Prompt Engineering adjusts the model ' s parameters, while Fine-tuning crafts input prompts.
B. Both involve retraining the model, but Prompt Engineering does it more often.
C. Prompt Engineering modifies training data, while Fine-tuning alters the model ' s structure.
D. Prompt Engineering creates input prompts, while Fine-tuning retrains the model on specific data.

Answer: D

Explanation:
In the context of Large Language Models (LLMs), Prompt Engineering and Fine-tuning are two distinct methods used to optimize the performance of AI models.
* Prompt Engineering involves designing and structuring input prompts to guide the model in generating specific, relevant, and high-quality responses. This technique does not alter the model ' s internal parameters but instead leverages the existing capabilities of the model by crafting precise and effective prompts. The focus here is on optimizing how you ask the model to perform tasks, which can involve specifying the context, formatting the input, and iterating on the prompt to improve outputs .
* Fine-tuning , on the other hand, refers to the process of retraining a pretrained model on a smaller, task- specific dataset. This adjustment allows the model to adapt its parameters to better suit the specific needs of the task at hand, effectively " specializing " the model for particular applications. Fine-tuning involves modifying the internal structure of the model to improve its accuracy and performance on the targeted tasks .
Thus, the key difference is that Prompt Engineering focuses on how to use the model effectively through input manipulation, while Fine-tuning involves altering the model itself to improve its performance on specialized tasks.

NEW QUESTION # 48
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 disregard model size and prioritize high-quality data only.
C. They focus on increasing the number of tokens while keeping the model size constant.
D. They prioritize larger model sizes to achieve better performance.

Answer: A

NEW QUESTION # 49
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

A. Speech recognition
B. Audio recording
C. Text summarization
D. Text to speech

Answer: D

Explanation:
For a multilingual public announcement system, the AI task that would be most relevant is " Text to Speech " (TTS). This task involves converting written text into spoken words, which can then be broadcasted over public address systems in multiple languages.
Text to Speech technology is crucial for creating accessible and understandable announcements in different languages, especially in environments like airports, train stations, or public events where clear verbal communication is essential. The TTS system would be configured to support multiple languages, allowing it to deliver announcements to diverse audiences effectively .

NEW QUESTION # 50
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 detect specific features in the input image.
D. To classify the input image.

Answer: C

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 # 51
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