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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Intro to ML Foundations	15%	- Discuss Supervised Learning Fundamentals 1. Classification 2. Regression - Discuss Unsupervised Learning Fundamentals - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics
Topic 2: Intro to OCI AI Services	20%	- OCI Speech - OCI Language - OCI Vision - OCI Document Understanding
Topic 3: OCI Generative AI and Oracle 23ai	10%	- Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services
Topic 4: Get started with OCI AI Portfolio	15%	- Discuss OCI AI Services Overview - Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI
Topic 5: Intro to DL Foundations	15%	- Explain Sequence Models (RNN and LSTM) - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN)
Topic 6: Intro to AI Foundations	10%	- Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data
Topic 7: Intro to Generative AI and LLMs	15%	- Explain Transformers Fundamentals - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain LLM Fine Tuning - Explain Prompt Engineering and Instruction Tuning

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

NEW QUESTION # 14
You are part of the medical transcription team and need to automate transcription tasks. Which OCI AI service are you most likely to use?

A. Vision
B. Speech
C. Document Understanding
D. Language

Answer: B

Explanation:
For automating transcription tasks in a medical transcription team, the most appropriate OCI AI service to use would be the " Speech " service. This service is designed to convert spoken language into text, which is essential for transcribing spoken medical reports or consultations into written form. The OCI Speech service provides capabilities such as speech-to-text conversion, which is specifically tailored for handling audio input and producing accurate transcriptions.

NEW QUESTION # 15
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both assign data points to categories.
B. Classification assigns data points to categories, whereas regression predicts continuous values.
C. Classification and regression both predict continuous values.
D. Classification predicts continuous values, whereas regression assigns data points to categories.

Answer: B

Explanation:
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .

NEW QUESTION # 16
You are working on a multilingual public announcement system. Which AI task will you use to implement it?

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

Answer: C

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 # 17
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

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 is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

A. Directly predicting the final output
B. Storing the input pixel values
C. Providing labels for the output neurons
D. Capturing the internal representation of the raw image data

Answer: D

Explanation:
In an Artificial Neural Network (ANN) designed for recognizing handwritten digits, the hidden layers serve the crucial function of capturing the internal representation of the raw image data. These layers learn to extract and represent features such as edges, shapes, and textures from the input pixels, which are essential for distinguishing between different digits. By transforming the input data through multiple hidden layers, the network gradually abstracts the raw pixel data into higher-level representations, which are more informative and easier to classify into the correct digit categories.

NEW QUESTION # 19
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