1z0-1122-26 Zertifizierungsantworten & 1z0-1122-26 Fragen Beantworten

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Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Weight	Objectives
Topic 1: OCI Generative AI and Oracle 23ai	10%	- Discuss Autonomous Database Select AI - Describe OCI Generative AI Services - Discuss Oracle Vector Search
Topic 2: Intro to ML Foundations	15%	- Discuss Reinforcement Learning Fundamentals - Discuss Supervised Learning Fundamentals 1. Regression 2. Classification - Discuss Unsupervised Learning Fundamentals - Explain Machine Learning Basics
Topic 3: Intro to DL Foundations	15%	- Explain Sequence Models (RNN and LSTM) - Explain Convolutional Models (CNN) - Discuss Deep Learning Fundamentals
Topic 4: Intro to OCI AI Services	20%	- OCI Language - OCI Speech - OCI Vision - OCI Document Understanding
Topic 5: Intro to Generative AI and LLMs	15%	- Discuss Large Language Models Fundamentals - Explain LLM Fine Tuning - Discuss Generative AI Overview - Explain Prompt Engineering and Instruction Tuning - Explain Transformers Fundamentals
Topic 6: Intro to AI Foundations	10%	- Explain AI vs ML vs DL - Discuss AI Basics - Discuss AI Applications and Types of Data
Topic 7: Get started with OCI AI Portfolio	15%	- Discuss OCI ML Services Overview - Discuss OCI AI Infrastructure Overview - Explain Responsible AI - Discuss OCI AI Services Overview

>> 1z0-1122-26 Zertifizierungsantworten <<

1z0-1122-26 Fragen Beantworten & 1z0-1122-26 Vorbereitung

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Oracle Cloud Infrastructure 2026 AI Foundations Associate 1z0-1122-26 Prüfungsfragen mit Lösungen (Q14-Q19):

14. Frage
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

A. AI is a subset of DL, which is a subset of ML.
B. AI, ML, and DL are entirely separate fields with no overlap.
C. DL is a subset of AI, and ML is a subset of DL.
D. ML is a subset of AI, and DL is a subset of ML.

Antwort: D

Begründung:
Artificial Intelligence (AI) is the broadest field encompassing all technologies that enable machines to perform tasks that typically require human intelligence. Within AI, Machine Learning (ML) is a subset focused on the development of algorithms that allow systems to learn from and make predictions or decisions based on data. Deep Learning (DL) is a further subset of ML, characterized by the use of artificial neural networks with many layers (hence " deep " ).
In this hierarchy:
* AI includes all methods to make machines intelligent.
* ML refers to the methods within AI that focus on learning from data.
* DL is a specialized field within ML that deals with deep neural networks.

15. Frage
Which type of machine learning is used to understand relationships within data and is not focused on making predictions or classifications?

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

Antwort: A

Begründung:
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 .

16. Frage
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?

A. Decrease the number of iterations.
B. Decrease the weight of the feature representing the presence of certain keywords.
C. Increase the regularization parameter.
D. Increase the threshold value for classification.

Antwort: D

Begründung:
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.

17. Frage
What distinguishes Generative AI from other types of AI?

A. Generative AI creates diverse content such as text, audio, and images by learning patterns from existing data.
B. Generative AI involves training models to perform tasks without human intervention.
C. Generative AI uses algorithms to predict outcomes based on past data.
D. Generative AI focuses on making decisions based on user interactions.

Antwort: A

Begründung:
Generative AI is distinct from other types of AI in that it focuses on creating new content by learning patterns from existing data. This includes generating text, images, audio, and other types of media. Unlike AI that primarily analyzes data to make decisions or predictions, Generative AI actively creates new and original outputs. This ability to generate diverse content is a hallmark of Generative AI models like GPT-4, which can produce human-like text, create images, and even compose music based on the patterns they have learned from their training data.

18. Frage
What does " fine-tuning " refer to in the context of OCI Generative AI service?

A. Encrypting the data for security reasons
B. Doubling the neural network layers
C. Upgrading the hardware of the AI clusters
D. Adjusting the model parameters to improve accuracy

Antwort: D

Begründung:
Fine-tuning in the context of the OCI Generative AI service refers to the process of adjusting the parameters of a pretrained model to better fit a specific task or dataset. This process involves further training the model on a smaller, task-specific dataset, allowing the model to refine its understanding and improve its performance on that specific task. Fine-tuning is essential for customizing the general capabilities of a pretrained model to meet the particular needs of a given application, resulting in more accurate and relevant outputs. It is distinct from other processes like encrypting data, upgrading hardware, or simply increasing the complexity of the model architecture.

19. Frage
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