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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: Machine Learning Foundations	15%	- Machine Learning fundamentals 1. Reinforcement learning basics and model evaluation concepts 2. Supervised learning: regression and classification 3. Unsupervised learning: clustering and dimensionality reduction
Topic 2: Deep Learning Foundations	15%	- Deep Learning and neural networks 1. Convolutional Neural Networks (CNN) architectures 2. Recurrent Neural Networks, LSTMs, and sequence models
Topic 3: 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
Topic 4: 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
Topic 5: OCI Generative AI and Oracle 23ai	10%	- OCI Generative AI Service features 1. Oracle 23ai Vector Database integration 2. Generative AI capabilities on OCI
Topic 6: Introduction to OCI AI Services	20%	- OCI AI Service APIs 1. OCI Language, Vision, Speech, and Document Understanding services 2. OCI Select AI and AI service use cases
Topic 7: 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

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Oracle Cloud Infrastructure 2026 AI Foundations Associate Sample Questions (Q17-Q22):

NEW QUESTION # 17
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. Transcribes audio and video files into text
D. Supports multiple languages including English, Spanish, and Portuguese

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 # 18
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?

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

Answer: A

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 # 19
In machine learning, what does the term " model training " mean?

A. Performing data analysis on collected and labeled data
B. Analyzing the accuracy of a trained model
C. Writing code for the entire program
D. Establishing a relationship between input features and output

Answer: D

Explanation:
In machine learning, " model training " refers to the process of teaching a model to make predictions or decisions by learning the relationships between input features and the corresponding output. During training, the model is fed a large dataset where the inputs are paired with known outputs (labels). The model adjusts its internal parameters to minimize the error between its predictions and the actual outputs. Over time, the model learns to generalize from the training data to make accurate predictions on new, unseen data.

NEW QUESTION # 20
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. Profanity filtering
C. Confidence scoring
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 # 21
What is the main function of the hidden layers in an Artificial Neural Network (ANN) when recognizing handwritten digits?

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

Answer: A

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 # 22
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