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| Section | Weight | Objectives |
|---|---|---|
| Intro to OCI AI Services | 20% | - OCI Document Understanding - OCI Vision - OCI Speech - OCI Language |
| 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 |
| Intro to ML Foundations | 15% | - Discuss Supervised Learning Fundamentals
- Explain Machine Learning Basics - Discuss Reinforcement Learning Fundamentals |
| OCI Generative AI and Oracle 23ai | 10% | - Discuss Autonomous Database Select AI - Discuss Oracle Vector Search - Describe OCI Generative AI Services |
| Intro to DL Foundations | 15% | - Discuss Deep Learning Fundamentals - Explain Convolutional Models (CNN) - Explain Sequence Models (RNN and LSTM) |
| Intro to AI Foundations | 10% | - Discuss AI Basics - Discuss AI Applications and Types of Data - Explain AI vs ML vs DL |
| Intro to Generative AI and LLMs | 15% | - Explain Prompt Engineering and Instruction Tuning - Discuss Large Language Models Fundamentals - Discuss Generative AI Overview - Explain Transformers Fundamentals - Explain LLM Fine Tuning |
>> Question 1z0-1122-26 Explanations <<
This 1z0-1122-26 certification assists you to put your career on the right track and helps you to achieve your career goals in a short time period. There are several personal and professional benefits that you can gain after passing the Oracle Cloud Infrastructure 2026 AI Foundations Associate (1z0-1122-26) certification exam. The prominent 1z0-1122-26 certification benefits include validation of skills and knowledge, more career opportunities, instant rise in salary, quick promotion, etc.
NEW QUESTION # 16
What is the purpose of Attention Mechanism in Transformer architecture?
Answer: C
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 # 17
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: D
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 # 18
In machine learning, what does the term " model training " mean?
Answer: A
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 # 19
Which AI domain is associated with tasks such as identifying the sentiment of text and translating text between languages?
Answer: C
Explanation:
Natural Language Processing (NLP) is the AI domain associated with tasks such as identifying the sentiment of text and translating text between languages. NLP focuses on enabling machines to understand, interpret, and generate human language in a way that is both meaningful and useful. This domain covers a wide range of applications, including text classification, language translation, sentiment analysis, and more, all of which involve processing and analyzing natural language data.
NEW QUESTION # 20
What is the primary purpose of reinforcement learning?
Answer: A
Explanation:
Reinforcement learning (RL) is a type of machine learning where an agent learns to make decisions by taking actions in an environment to achieve a certain goal. The agent receives feedback in the form of rewards or penalties based on the outcomes of its actions, which it uses to learn and improve its decision-making over time. The primary purpose of reinforcement learning is to enable the agent to learn optimal strategies by interacting with its environment, thereby maximizing cumulative rewards. This approach is commonly used in areas such as robotics, game playing, and autonomous systems.
NEW QUESTION # 21
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