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Oracle 1z0-1122-26 Exam Syllabus Topics:

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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q35-Q40):

NEW QUESTION # 35
Lisa is working on a project that involves transcribing thousands of audio files stored in Oracle Cloud. She wants to process multiple files efficiently instead of transcribing them one by one. Which OCI Speech feature should Lisa use?

A. Timestamping
B. Confidence scoring
C. Profanity filtering
D. Batch support

Answer: D

Explanation:
OCI Speech supports batch transcription for efficiently processing prerecorded media. Lisa ' s key requirement is to process thousands of audio files without manually handling them individually. Oracle documentation explicitly identifies OCI Speech as supporting both real-time and batch transcription, while the OCI Speech documentation includes transcription jobs and large batch jobs among its supported workflows.
Oracle Docs Batch support therefore directly addresses high-volume processing of stored audio. Confidence scoring measures the service ' s certainty about recognized speech, timestamping associates transcript content with positions in the source recording, and profanity filtering controls how offensive language is represented.
Those capabilities enhance transcription output but do not provide the required multi-file processing mechanism. Consequently, Batch support is the appropriate OCI Speech capability for scalable transcription of Lisa ' s collection of audio files.

NEW QUESTION # 36
Which feature is NOT supported as part of the OCI Language service ' s pretrained language processing capabilities?

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

Answer: C

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 # 37
What are Convolutional Neural Networks (CNNs) primarily used for?

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

Answer: B

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 # 38
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 # 39
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

A. Supervised learning
B. Unsupervised learning
C. Reinforcement learning
D. Active 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 # 40
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