1z0-1122-26최신업데이트인증시험자료, 1z0-1122-26시험문제집

Oracle 1z0-1122-26덤프의 유효성을 보장해드릴수 있도록 저희 기술팀은 오랜시간동안Oracle 1z0-1122-26시험에 대하여 분석하고 연구해 왔습니다. Oracle 1z0-1122-26 덤프를 한번 믿고Oracle 1z0-1122-26시험에 두려움없이 맞서보세요. 만족할수 있는 좋은 성적을 얻게 될것입니다.

Oracle 1z0-1122-26 Exam Syllabus Topics:

Section	Objectives
Topic 1: Oracle AI and Machine Learning Services	- Oracle AI Stack 1. AI infrastructure 2. AI data and machine learning services - OCI Machine Learning Services 1. OCI Data Science and machine learning workflows 2. Machine learning capabilities in OCI - OCI AI Services 1. Language 2. Speech 3. Document Understanding 4. Vision
Topic 2: Artificial Intelligence and Machine Learning Fundamentals	- Machine Learning Fundamentals 1. Machine learning models and architectures 2. Supervised and unsupervised learning - Artificial Intelligence Concepts 1. AI fundamentals and terminology 2. AI use cases and applications - Deep Learning Fundamentals 1. Convolutional and sequence models 2. Neural networks
Topic 3: OCI Generative AI and Oracle Database AI Capabilities	- OCI Generative AI 1. OCI Generative AI services 2. Generative AI models and applications - AI Application Frameworks 1. Language frameworks 2. Retrieval and AI application concepts - Oracle AI Database 1. Vector database concepts 2. AI capabilities in Oracle Database
Topic 4: Generative AI and Large Language Models	- Generative AI Fundamentals 1. Generative AI use cases 2. Generative AI concepts and capabilities - Large Language Models 1. LLM fundamentals 2. Language models and generative AI applications

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Oracle 1z0-1122-26시험문제집 & 1z0-1122-26인기자격증 덤프문제

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최신 Oracle Cloud Infrastructure 1z0-1122-26 무료샘플문제 (Q26-Q31):

질문 # 26
What is the difference between classification and regression in Supervised Machine Learning?

A. Classification and regression both assign data points to categories.
B. Classification assigns data points to categories, whereas regression predicts continuous values.
C. Classification and regression both predict continuous values.
D. Classification predicts continuous values, whereas regression assigns data points to categories.

정답：B

설명：
In supervised machine learning, the key difference between classification and regression lies in the nature of the output they predict. Classification algorithms are used to assign data points to one of several predefined categories or classes, making it suitable for tasks like spam detection, where an email is classified as either " spam " or " not spam. " On the other hand, regression algorithms predict continuous values, such as forecasting the price of a house based on features like size, location, and number of rooms. While classification answers " which category? " regression answers " how much? " or " what value? " .

질문 # 27
Which statement best describes the relationship between Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL)?

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

정답：A

설명：
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.

질문 # 28
How does Oracle Cloud Infrastructure Document Understanding service facilitate business processes?

A. By automating data extraction from documents
B. By analyzing sentiment in text documents
C. By transcribing spoken language
D. By generating lifelike speech from documents

정답：A

설명：
Oracle Cloud Infrastructure (OCI) Document Understanding service facilitates business processes by automating data extraction from documents. This service leverages machine learning to identify, classify, and extract relevant information from various document types, reducing the need for manual data entry and improving efficiency in document processing workflows. Automation of these tasks enables organizations to streamline operations and reduce errors associated with manual data handling.

질문 # 29
Which algorithm is primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN)?

A. Gradient Descent
B. Random Forest
C. Backpropagation
D. Support Vector Machine

정답：C

설명：
Backpropagation is the algorithm primarily used for adjusting the weights of connections between neurons during the training of an Artificial Neural Network (ANN). It is a supervised learning algorithm that calculates the gradient of the loss function with respect to each weight by applying the chain rule, propagating the error backward from the output layer to the input layer. This process updates the weights to minimize the error, thus improving the model ' s accuracy over time.
* Gradient Descent is closely related as it is the optimization algorithm used to adjust the weights based on the gradients computed by backpropagation, but backpropagation is the specific method used to calculate these gradients.

질문 # 30
In machine learning, what does the term " model training " mean?

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

정답：D

설명：
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.

질문 # 31
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