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| Section | Weight | Objectives |
|---|---|---|
| Topic 1: Intro to ML Foundations | 15% | - Discuss Reinforcement Learning Fundamentals - Explain Machine Learning Basics - Discuss Supervised Learning Fundamentals
|
| Topic 2: Get started with OCI AI Portfolio | 15% | - Discuss OCI AI Services Overview - Discuss OCI AI Infrastructure Overview - Discuss OCI ML Services Overview - Explain Responsible AI |
| Topic 3: Intro to DL Foundations | 15% | - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals - Explain Sequence Models (RNN and LSTM) |
| Topic 4: OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Topic 5: Intro to OCI AI Services | 20% | - OCI Speech - OCI Document Understanding - OCI Vision - OCI Language |
| Topic 6: Intro to Generative AI and LLMs | 15% | - Explain Prompt Engineering and Instruction Tuning - Explain Transformers Fundamentals - Discuss Generative AI Overview - Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning |
| Topic 7: Intro to AI Foundations | 10% | - Explain AI vs ML vs DL - Discuss AI Applications and Types of Data - Discuss AI Basics |
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It is browser-based; therefore no need to install it, and you can start practicing for the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) exam by creating the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) practice test. Our Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) exam dumps give help to give you an idea about the actual Oracle 1z0-1122-26 Exam. You can attempt multiple Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) exam questions on the software to improve your performance.
NEW QUESTION # 17
Which capability is supported by Oracle Cloud Infrastructure Language service?
Answer: A
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 # 18
You are training a logistic regression model to classify emails as spam or not spam. The model is currently classifying too many emails as spam. What would you do to adjust the model?
Answer: B
Explanation:
In binary classification, the decision threshold determines how much predicted probability is required before an observation is assigned to the positive class. Here, spam represents the positive class, and the model is producing too many spam classifications, indicating excessive positive predictions or false positives.
Increasing the classification threshold requires a higher predicted probability before an email is labeled as spam, reducing the number of positive classifications. Oracle Machine Learning documentation defines the probability threshold as the decision point used for binary classification and explains that changing this threshold changes true-positive and false-positive behavior. Oracle Docs Altering individual feature weights, iteration counts, or regularization affects model training rather than directly controlling the classification decision boundary. Therefore, increasing the classification threshold is the most appropriate adjustment.
NEW QUESTION # 19
What is the purpose of the model catalog in OCI Data Science?
Answer: A
Explanation:
The primary purpose of the model catalog in OCI Data Science is to store, track, share, and manage machine learning models. This functionality is essential for maintaining an organized repository where data scientists and developers can collaborate on models, monitor their performance, and manage their lifecycle. The model catalog also facilitates model versioning, ensuring that the most recent and effective models are available for deployment. This capability is crucial in a collaborative environment where multiple stakeholders need access to the latest model versions for testing, evaluation, and deployment.
NEW QUESTION # 20
What is the purpose of Attention Mechanism in Transformer architecture?
Answer: D
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 # 21
How is " Prompt Engineering " different from " Fine-tuning " in the context of Large Language Models (LLMs)?
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 # 22
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