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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
OCI AI Portfolio	15%	- Overview of OCI AI offerings 1. AI Services, ML Services, and AI Infrastructure overview 2. OCI Data Science and GPU-based compute infrastructure
Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Supervised learning: regression and classification 2. Reinforcement learning basics and model evaluation concepts 3. Unsupervised learning: clustering and dimensionality reduction
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
Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Recurrent Neural Networks, LSTMs, and sequence models 2. Convolutional Neural Networks (CNN) architectures
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
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
OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Generative AI capabilities on OCI 2. Oracle 23ai Vector Database integration

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

NEW QUESTION # 39
Which capability is supported by Oracle Cloud Infrastructure Language service?

A. Detecting objects and scenes in images
B. Translating text into speech
C. Converting text into images
D. Analyzing text to extract structured information like sentiment or entities

Answer: D

Explanation:
Oracle Cloud Infrastructure (OCI) Language service is specifically designed to analyze text and extract structured information such as sentiment, entities, key phrases, and language detection. This service provides natural language processing (NLP) capabilities that help users gain insights from unstructured text data. By identifying the sentiment (positive, negative, neutral) and recognizing entities (like names, dates, or places), the service enables businesses to process large volumes of text data efficiently, aiding in decision-making processes.

NEW QUESTION # 40
How do Large Language Models (LLMs) handle the trade-off between model size, data quality, data size and performance?

A. They prioritize larger model sizes to achieve better performance.
B. They disregard model size and prioritize high-quality data only.
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 # 41
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 # 42
What is " in-context learning " in the realm of Large Language Models (LLMs)?

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

Answer: D

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

Answer: C

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 # 44
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