FRENQUENT 1Z0-1122-25 UPDATE - 1Z0-1122-25 TEST VCE FREE

Frenquent 1Z0-1122-25 Update - 1Z0-1122-25 Test Vce Free

Frenquent 1Z0-1122-25 Update - 1Z0-1122-25 Test Vce Free

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Oracle 1Z0-1122-25 Exam Syllabus Topics:

TopicDetails
Topic 1
  • Intro to OCI AI Services: This section tests the expertise of AI Solutions Engineers in working with OCI AI services and related APIs. It provides insights into key AI services such as language processing, computer vision, document understanding, and speech recognition, allowing professionals to leverage Oracle’s AI ecosystem for building intelligent applications.
Topic 2
  • Intro to ML Foundations: This section evaluates the knowledge of Machine Learning Engineers in understanding machine learning principles and methodologies. It explores the basics of supervised learning, focusing on regression and classification techniques, along with unsupervised learning methods such as clustering and anomaly detection. It also introduces reinforcement learning fundamentals, helping professionals grasp the different approaches used to train AI models.
Topic 3
  • Intro to Generative AI & LLMs: This section tests the abilities of AI Developers to understand generative AI and large language models. It introduces the principles of generative AI, explains the fundamentals of large language models (LLMs), and discusses the core workings of transformers, prompt engineering, instruction tuning, and LLM fine-tuning for optimizing AI-generated content.

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Oracle Cloud Infrastructure 2025 AI Foundations Associate Sample Questions (Q19-Q24):

NEW QUESTION # 19
What would you use Oracle AI Vector Search for?

  • A. Query data based on keywords.
  • B. Query data based on semantics.
  • C. Manage database security protocols.
  • D. Store business data in a cloud database.

Answer: B

Explanation:
Oracle AI Vector Search is designed to query data based on semantics rather than just keywords. This allows for more nuanced and contextually relevant searches by understanding the meaning behind the words used in a query. Vector search represents data in a high-dimensional vector space, where semantically similar items are placed closer together. This capability makes it particularly powerful for applications such as recommendation systems, natural language processing, and information retrieval where the meaning and context of the data are crucial .


NEW QUESTION # 20
What does "fine-tuning" refer to in the context of OCI Generative AI service?

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

Answer: C

Explanation:
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.


NEW QUESTION # 21
In machine learning, what does the term "model training" mean?

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

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 # 22
What is the difference between classification and regression in Supervised Machine Learning?

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

Answer: C

Explanation:
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?".


NEW QUESTION # 23
You are working on a project for a healthcare organization that wants to develop a system to predict the severity of patients' illnesses upon admission to a hospital. The goal is to classify patients into three categories - Low Risk, Moderate Risk, and High Risk - based on their medical history and vital signs. Which type of supervised learning algorithm is required in this scenario?

  • A. Regression
  • B. Multi-Class Classification
  • C. Binary Classification
  • D. Clustering

Answer: B

Explanation:
In this healthcare scenario, where the goal is to classify patients into three categories-Low Risk, Moderate Risk, and High Risk-based on their medical history and vital signs, a Multi-Class Classification algorithm is required. Multi-class classification is a type of supervised learning algorithm used when there are three or more classes or categories to predict. This method is well-suited for situations where each instance needs to be classified into one of several categories, which aligns with the requirement to categorize patients into different risk levels.


NEW QUESTION # 24
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